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Another Fine Mesh top

► A Visit to SAE International’s 2018 World Congress Experience (WCX)
  23 Apr, 2018
The Society of Automotive Engineers (SAE) International hosted their automotive-focused 2018 World Congress Experience (WCX) during 10-12 of April .  The conference was held at the Cobo Center in Detroit, MI—best known as the center of the U.S. automobile industry and home to … Continue reading
► The Latest News in Meshing and CFD at Pointwise
  20 Apr, 2018
In lieu of This Week in CFD and at the risk of duplicating our This Week at Pointwise newsletter, here is a quick summary of things happening at and around Pointwise in the worlds of mesh generation and CFD, from … Continue reading
► AIAA Student Paper Competition in Meshing, Visualization, and Computational Environments
  16 Apr, 2018
AIAA’s MVCE technical committee is sponsoring a student paper competition for SciTech 2019. The author of the best extended abstract as judged by the committee will be awarded $500 in advance of the conference to defray travel expenses. The Meshing, … Continue reading
► This Week in CFD
  13 Apr, 2018
In this lucky “Friday the 13th” edition we have some good, long reads on simulation driven design and the cloud, a couple non-traditional applications of CFD, and a wonderful illustration of suspended droplets.  Lucky Reading Lifecycle Insights prefaces an upcoming … Continue reading
► CAD Interoperability and CFD Meshing Survey
  12 Apr, 2018
We hope you will spend just a few minutes on our 9-question survey about geometry models and CFD mesh generation. It is open to everyone in the CFD community and we will share the results here after the survey closes.  … Continue reading
► Pointwise User Group Meeting 2018 – Call for Papers
  11 Apr, 2018
We are now accepting abstracts for the Pointwise User Group Meeting. Your 500 words about an application of mesh generation using Pointwise are due by 13 July.   Mesh generation for CFD may never again be as cool as it is … Continue reading

F*** Yeah Fluid Dynamics top

► A drop of blue-dyed glycerine impacts a thin film of...
  25 Apr, 2018

A drop of blue-dyed glycerine impacts a thin film of isopropanol, creating a spectacular splash and breakup. The drop’s impact flings a layer of the isopropanol into the air, where air currents make the thin sheet buckle inward and break into a spray of droplets. Meanwhile, the liquid from the drop forms a thick, blue crown that rises and expands outward. When tiny droplets of the isopropanol hit the splash crown, their lower surface tension causes the blue glycerine to pull away, due to the Marangoni effect. This opens up holes in the crown, which grow quickly, until the entire sheet breaks apart. (Image and research credit: A. Aljedaani et al., source)

► Much of the rain that falls on Earth began as snow high in the...
  24 Apr, 2018

Much of the rain that falls on Earth began as snow high in the atmosphere. As it falls through warmer layers of air, the snowflakes melt and form water droplets. The details of this melting process have been difficult to capture experimentally, but a new computational model may provide insight. The basic process has a couple stages. As snow begins to melt, surface tension draws the water into concave areas nearby. When those regions fill up, the water flows out and merges with neighboring liquid, forming water droplets around a melting ice core. 

Although this same sequence was observed for many types of snow, scientists also observed some important differences between rimed and unrimed snowflakes. Rime forms when supercooled water droplets freeze onto the surface of a snowflake. Lightly rimed snow still looks light and fluffy, like the animation above, but heavily rimed snow forms denser and more spherical chunks. Because there are lots of porous gaps in heavily rimed snow, water tends to gather there during initial melting. Rimed snow was also more likely to form one large water droplet rather than breaking into multiple droplets like snow with less rime. For more, check out NASA’s video and the Bad Astronomy write-up. (Image credit: NASA, source; research credit: J. Leinonen and A. von Lerber; via Bad Astronomy; submitted by Kam Yung-Soh)

► Timelapse can be a beautiful way to highlight slow-moving flows...
  23 Apr, 2018

Timelapse can be a beautiful way to highlight slow-moving flows like those in the sky. But it can also be valuable in showing differences in speed, as in the latest SKYGLOW Project video, “Colorado Serenade”, which shows the Colorado River and the skies overhead simultaneously. Timelapse highlights the difference in time scales between the fast-moving river and slower-moving clouds.

This mirrors an important phenomenon in fluid dynamics known as “separation of scales”. In a flow, there are often multiple effects at play and they may occur on different time (or length) scales. Which matters most in a given situation will depend on those scales. Consider a rocket engine. Combustion inside the engine ignites fuel and oxidizer, releasing heat. At the same time, the flow in the engine is key to mixing that fuel and oxidizer together so that all of the fuel and oxidizer ignites before it is sent downstream into the rocket nozzle. There are two important time scales here: the time it takes for the flow to mix fuel and oxidizer together and the time it takes for the combustive chemical reaction to take place. In an ideal world, engineers can balance those two time scales to maximize efficiency. But in the (admittedly less ideal) real world, this is not always possible. (Video and image credit: H. Mehmedinovic/SKYGLOW)

► Happy Friday and happy 2000th FYFD post! To celebrate, I played...
  20 Apr, 2018

Happy Friday and happy 2000th FYFD post! To celebrate, I played with surface tension and the Marangoni effect to make some art. For a run-down on the physics, check out this previous post on water calligraphy. Two thousand posts feels like a major milestone. Not everyone realizes this, but FYFD is a one-woman operation, so 2000 posts is a whole lot of research, image editing, and writing. For fun, I’m including here eight completely random FYFD entries, representing less than one-half of one percent of my total archives:  

1. Why did Chinese junks put holes in their rudders?
2. Making droplets in an ultrasonic humidifier
3. Floating on a granular raft
4. Air-trapping fur keeps otters warm
5. The physics of the knuckleball
6. What makes badminton so fast?
7. Playing with fluorescein
8. How frost forms

Want to keep up the random walk? Use to find random entries, or if you prefer your browsing to be more directed, check out the visual archive or the themed series page

As always, a special thanks to those who help support FYFD through Patreon subscriptions - I couldn’t keep writing and making videos without your help! And thank you to all of you who read and share FYFD. Whether you’ve been following along for a week or for the last eight years, your enthusiasm keeps me motivated! Thank you!

(Image credits: 2k animation - N. Sharp; Chinese junk ship - Premier Ship Models; ultrasonic humidifier - S. J. Kim et al.; granular raft -  E. Jambon-Puillet and S. Protiere; 3D-printed “fur” - F. Frankel; knuckleball - L. Kang; shuttlecock - Science Friday; fluorescein - Shanks FX; freezing droplets - J. Boreyko et al.)

► Humans may not be fast enough to run across water, but we’ve...
  19 Apr, 2018

Humans may not be fast enough to run across water, but we’ve found other ways to conquer the waves. It’s even possible (though definitely not recommended) to ride across stretches of water on a dirt bike. To do so, you have to keep the bike (hydro)planing, and to understand what that means, let’s take a moment to talk about boats.

At low speeds, boats stay afloat based on buoyancy, a force that depends on how much water they displace. But when moving at high speeds, modern speedboats lift mostly out of the water and skim the surface instead. At this point, it’s hydrodynamic lift that keeps the boat above the surface and we say that the boat is planing. Calculating that hydrodynamic lift is fairly complicated and depends on many factors – for those who are interested, check out some of David Savitsky’s papers – but, generally speaking, going faster gives you more lift.

This brings us back to the dirt bike. There’s nothing particularly hydrodynamic about a dirt bike. It’s not shaped to provide hydrodynamic lift, but it does come with a high power-to-weight ratio. It’s this ability to create pure speed, and a rider’s keen sense for holding the bike at the right angle, that enables pros to cross open water. Needless to say, this is the kind of stunt that could end really badly, so don’t try it yourself. (Image credits: C. Alessandrelli, source; EnduroTripster, source; via Digg; submitted by 1307phaezr)

► Over geological timescales – on the order of millions of...
  18 Apr, 2018

Over geological timescales – on the order of millions of years – even hard substances like rock can flow like a fluid. Heat from the Earth’s core drives convection inside our mantle, and that fluid motion ultimately drives the plate tectonics we experience here at the surface. But most other planetary bodies, including those with mantle convection similar to ours, don’t have a surface that shifts like our tectonic plates. Mars and Venus, for example, have solid, unmoving surfaces. The images above provide a peek at what goes on beneath. The upper image shows a simulation of mantle convection inside Mars over millions of years. The lower image is a timelapse of dye convecting through a layer of glucose syrup being heated from below. Notice how both examples show evidence of convective cells and plumes that help circulate warm fluid up and colder fluid downward. (Image credit: Mars simulation - C. Hüttig et al, source; N. Tosi et al., source; submitted by Nicola T.)

Symscape top

► Long-Necked Dinosaurs Succumb To CFD
  14 Jul, 2017

It turns out that Computational Fluid Dynamics (CFD) has a key role to play in determining the behavior of long extinct creatures. In a previous, post we described a CFD study of parvancorina, and now Pernille Troelsen at Liverpool John Moore University is using CFD for insights into how long-necked plesiosaurs might have swum and hunted.

CFD Water Flow Simulation over an Idealized PlesiosaurCFD Water Flow Simulation over an Idealized Plesiosaur: Streamline VectorsIllustration only, not part of the study

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► CFD Provides Insight Into Mystery Fossils
  23 Jun, 2017

Fossilized imprints of Parvancorina from over 500 million years ago have puzzled paleontologists for decades. What makes it difficult to infer their behavior is that Parvancorina have none of the familiar features we might expect of animals, e.g., limbs, mouth. In an attempt to shed some light on how Parvancorina might have interacted with their environment researchers have enlisted the help of Computational Fluid Dynamics (CFD).

CFD Water Flow Simulation over a ParvancorinaCFD Water Flow Simulation over a Parvancorina: Forward directionIllustration only, not part of the study

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► Wind Turbine Design According to Insects
  14 Jun, 2017

One of nature's smallest aerodynamic specialists - insects - have provided a clue to more efficient and robust wind turbine design.

DragonflyDragonfly: Yellow-winged DarterLicense: CC BY-SA 2.5, André Karwath

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► Runners Discover Drafting
    1 Jun, 2017

The recent attempt to break the 2 hour marathon came very close at 2:00:24, with various aids that would be deemed illegal under current IAAF rules. The bold and obvious aerodynamic aid appeared to be a Tesla fitted with an oversized digital clock leading the runners by a few meters.

2 Hour Marathon Attempt

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► Wind Tunnel and CFD Reveal Best Cycling Tuck
  10 May, 2017

The Giro d'Italia 2017 is in full swing, so how about an extensive aerodynamic study of various cycling tuck positions? You got it, from members of the same team that brought us the study of pursuit vehicles reducing the drag on cyclists.

Chris Froome TuckChris Froome TuckStage 8, Pau/Bagnères-de-Luchon, Tour de France, 2016

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► Active Aerodynamics on the Lamborghini Huracán Performante
    3 May, 2017

Early on in the dash to develop ever faster racecars in the 1970s, aerodynamics, and specifically downforce, proved a revelation. Following on quickly from the initial passive downforce initiatives were active aerodynamic solutions. Only providing downforce when needed (i.e., cornering and braking) then reverting to a low drag configuration was an ideal protocol, but short lived due to rule changes in most motor sports (including Formula 1), which banned active aerodynamics. A recent exception to the rule is the highly regulated Drag Reduction System now used in F1. However, road-legal cars are not governed by such regulations and so we have the gloriously unregulated Lamborghini Huracán Performante.

Active Aerodynamics on the Lamborghini Huracán Performante

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CFD Online top

► Wall function usage
    3 Apr, 2018
about wallfunction usage
Originally Posted by G_German View Post
Since the documentation on the use of wall functions (or BCs for turbulence models in general) in OpenFOAM is rather small I tried to write up a little summary of what is posted in the internet (for kEpsilon, kOmegaSST & SA).
As this may also be interesting to other people and I would be interested in feedback, please find the summary below:

If the grid near the wall is fine enough (~y+<1 everywhere and at least 4-5 nodes below y+=5) wall functions are not needed.
For kEpsilon a lowRe version is required, SA and kOmegaSST should be applicable also for low Re numbers (though this is under discussion for the OpenFOAM implementation:
For coarser grids (30<y+<100) wall functions exploit the universal logarithmic wall law to model the transition from the laminar near-wall flow to the fully developed turbulent flow.

Basically the type of wall function is chosen in the nut file by the nutxxx wall functions:
- nutWallFunction (seems to be the most basic wall function without further requirements): high-Re wall-function based on k.
- nutkWallFunction (standard for kEpsilon/kOmega, probably requires an equation for k...): nutkWallFunction sets the turbulent viscosity in the first node point based on the logarithmic law ( based on the turbulent kinetic energy close to the wall)
- nutUWallFunction: in comparison to nutkWallFunction this wall function calculates yPlus based on the velocity close to the wall (not k)
- nutUSpaldingWallFunction (standard for SA turbulence model, called nutSpalartAllmarasWallFunction in earlier version, original reference is doi:10.1115/1.3641728):
continuous wall-function which should cover the complete y+ range from O(1) to somewhere of O(10). Might be the best choice (together with low Re kEpsilon, kOmegaSST or SA, when y+ varies for different parts of the wall.
- nutLowReWallFunction (code comment: "Sets nut to zero, and provides an access function to calculate y+." ):
Dummy wall function required for the calculation of yPlusRAS in simulations which resolve the near wall flow (however, there are varying opinions about this in the internet).

For epsilon, omega, k, ... one uses the corresponding wall functions exist:
- epsilonWallFuncion for epsilon ( (fixed value e=0 or better e=1e-8(?) for lowRe calculations):
calculate (for each timestep) the first grid point value by using an algebraic expression derived from the classical logarithmic law-of the wall approach
- kqRWallFunction for k, q, R
in code: Boundary condition for turbulence k, Q, and R when using wall functions. Simply acts as a zero gradient condition.
(appears to be applicable down to yPlus~1, but one should use a fixed value with k=0 or a very small value for y+<1)
- omegaWallFunction for omega; Not really a wall function but the b.c. defined by Menter for Omega, i.e. should be used always for kOmega model, independent of y+)
omegawall=60*nu/(beta*y^2), with nu=kinematic viscosity at the wall, beta=0.075 and y=normal distance between the first fluid node and the nearest wall-> very large value for omega)
The "value" which is specified for the wall functions is only an initial conditions

To check my understanding I did some comparisons of simulations with OF 2.4/3.0 to Wieghardt's flat plate Cf measurements:
► Exporting two different meshes simultaneously from salome to openfoam
  16 Mar, 2018
This is my first post.

I want to export two different meshes at a time from salome 8.3.0 to (OpenFOAM-in-Box-18.02-r638).
I don't want to compound those meshes.
I used med and cgns formats to export, but failed.
When I exported using med, I was unable to convert it into foam file. I tried using ideasUnvToFoam <File_name>.med.
The polymesh folder was created, but all the files i.e. boundary, points, faces, neighbour and owner were empty.
and, When I tried to export using cgns it showed not enough memory on the disk.

Actually, I have created geometry in FreeCAD 0.16-6712 which is given in Tutorial of OpenFOAM in form of blockMesh.
i.e. OpenFOAM-in-Box-18.02/OpenFOAM-dev/tutorials/incompressible/SRFPimpleFoam/rotor2D and meshed it in Salome.
In one mesh I meshed for front, back and freestream and in other I meshed the rotor.
I grouped the geometry well.

I had exported compound mesh in unv and converted it using ideasUnvToFoam, but in paraview of Openfoam the blades of the rotor was not shown.

In Salome the exported unv file shows the correct mesh while in Openfoam rotor blades are not shown.
So, I want to export them individually.
► cfMesh my steps
    3 Mar, 2018
fms file creation
surfaceToFMS file.stl
extraction of edges from stl file
surfaceFeatureEdges file.stl file.fms -angle 30
This is to visualize with paraview
FMSToSurface -exportFeatureEdges file.fms fileEdge.fms
#several steps for improving mesh quality
checkMesh.exe >> log.Mesh1
checkMesh.exe >> log.Mesh1
checkMesh.exe >> log.Mesh1
► MPI barrier inside OpenFoam
    1 Mar, 2018
Pstream has two sub directories :
dummy and mpi

Originally Posted by matteoL View Post
when running in parallel, for some reasons I have some actions that have to be done only by the master core and I would like the other partitions to wait until the master has finished.
(I call those action doing:
If (Pstream::master()){ ...} )

I think the solution in a general c++ code using MPI would be to add a line like:

Is there an already implemented function in OF available?
how could i get acces to the MPI class/function/settings?

Thank you very much,
best regards,
► RUnning FIRE from cmd prompt
  14 Feb, 2018
Originally Posted by cfdvenkatesh View Post
You can use "fire_cmd --help" to know all the options.

Easier way to get started is to launch the FIRE job from GUI and look for a command in job_setup.log file in case directory.

The FIRE 2017.1 version should have an option to directly submit into queuing system from GUI

Hi cfdvenkatesh,

We only have avl2014 installed in our HPC. By the way, I'm mostly using linux environment so I run the simulation in bash.
But now my problem is "time". If I want to run the simulation in my own desktop it takes a few weeks, so I need higher speed. But I'm not sure how to use correct numbers of nodes, cpu's, etc to get the results less than 168 Hours (this is our maximum allowed wall time). Can you help me?
My running scripts are:

#PBS -l nodes=1:ppn=14
#PBS -l mem=32000MB
#PBS -l walltime=168:00:00
#PBS -j oe

module load avl/2014.1

fire_cmd -name=fire -mpi -cpu=14 -hostlist=localhost,1,MPI -project_dir=###
-project=##.fpr -case=### -solver_vers=v2014 -cfd_mesh=###.msh
► Tutorial of how to plot residuals !
    6 Feb, 2018
Originally Posted by wolle1982 View Post
Hi all,

since apearantly noone has an idea of how to plot the residuals of a calculation on-the-fly, I will give a small manual on that:

Tutorial on "How to plot the residuals (and forces) graphically on screen on-the-fly"

Step 1:
Start the calculation and make it write out a log-file. for example
turbFoam >log
"log" is the name of the log-file to be output. It is written into the main-case-folder.

Step 2:
If desired you can open a new console-window of the main-case-folder and follow the text-output by the command
tail -f log
"log" is the name of the log-file to be read in. To stop reading the file constantly just use Crtl+C

Step 3:
To plot the residuals graphically on the screen you can use gnuplot that is delivered with linux already.
Within the main-case-folder you have to put a text file with a name e.g. "Residuals" (also see attachments).
The file should contain the following gnuplot properties:
set logscale y
set title "Residuals"
set ylabel 'Residual'
set xlabel 'Iteration'
plot "< cat log | grep 'Solving for Ux' | cut -d' ' -f9 | tr -d ','" title 'Ux' with lines,\
"< cat log | grep 'Solving for Uy' | cut -d' ' -f9 | tr -d ','" title 'Uy' with lines,\
"< cat log | grep 'Solving for Uz' | cut -d' ' -f9 | tr -d ','" title 'Uz' with lines,\
"< cat log | grep 'Solving for omega' | cut -d' ' -f9 | tr -d ','" title 'omega' with lines,\
"< cat log | grep 'Solving for k' | cut -d' ' -f9 | tr -d ','" title 'k' with lines,\
"< cat log | grep 'Solving for p' | cut -d' ' -f9 | tr -d ','" title 'p' with lines
pause 1
The pause-command sets the seconds till reload. Deletion makes it faster in some cases.

Execute the command
gnuplot Residuals -
in the main-case-folder.

Step 4:
Another good indicator for the calculations convergence is the forces-plot. Therefore you have to set the function in the controlDict that calculates the forces and forceCoeffs. See thread or attachments.

Be sure to have the properties for gnuplot in the main-case-folder (see attachments).

You have the adapt the folder-name where the forceCoeffs.dat is inside before.

While the calculation runs you also can use the gnuplot command
gnuplot forceCoeffs -
in the main-case-folder. When the forces seem to not change any more, the pressure allocation must be constantly what makes the convergent case proofed.

Plotting the real forces is also easy. Proceed identically like in "Step 4" but be sure to set
magUInf 1.0; //free stream velocity magnitude
lRef 1.0; //reference length
Aref 1.632653; //reference area
in the controlDict.

Using the attached text-files, remove the ".txt" first.

Hope that helps somebody.

How to plot residuals by Gnuplot

curiosityFluids top

► Rayleigh–Bénard Convection Using buoyantBoussinesqPimpleFoam
  13 Jun, 2017

Here is an extremely simple simulation to set up that has a surprisingly beautiful output. In this post, we will simulation the classic Rayleigh–Bénard convection (see Wikipedia) in 3D using the buoyant solver, buoyantBoussinesqPimpleFoam.

buoyantBoussinesqPimpleFoam is a solver for buoyant, turbulent, incompressible flows. The incompressible part of the solver comes from the fact that it uses the Boussinesq approximation for buoyancy which is only suitable for flows with small density changes (aka incompressible). A good source for the equations in this solver is this blog post.

Simulation Set-up

The basic set-up for this case is simple: a hot bottom surface, a cold top surface, either cyclic or zero-gradient sides, and some initial conditions.

For this example, I used the properties of water, I set the bottom plate at 373 K (don’t ask me why… I know it’s close to boiling point of water), and the top plate at 273 K. For this case, we will not use any turbulent modeling and will simply use a laminar model (this simply means there is only molecular viscosity, there are no simplifications applied to the equations).

Geometry and Mesh

The geometry is shown below. As the geometry is so simple… I will not go over the blockMesh set up. The mesh discretization that I used was simplegrading (i.e. no inflation), with 200x200x50 cells.



For this case, we will simulate water. The transportProperties file should look like:

transportModel Newtonian;

// Laminar viscosity
nu [0 2 -1 0 0 0 0] 1e-06;

// Thermal expansion coefficient
beta [0 0 0 -1 0 0 0] 0.000214;

// Reference temperature
TRef [0 0 0 1 0 0 0] 300;

// Laminar Prandtl number
Pr [0 0 0 0 0 0 0] 7.56;

// Turbulent Prandtl number (not used)
Prt [0 0 0 0 0 0 0] 0.7;

I don’t typically delete unused entries from dictionaries. This makes using previous simulations as templates much easier. Therefore note that the turbulent Prandtl number is in the dictionary but it is not used.

Selecting Reference Temperature TRef for buoyantBoussinesqPimpleFoam. To answer this recall that when the Boussinesq buoyancy approximation is used, the solver does not solve for the density. It solves the relative density using the linear function:

\frac{\rho}{\rho_0}=1-\beta \left(T-T_0\right)

Therefore, I think it makes sense that we should choose a temperature for T_{ref} that is somewhere in the range of the simulation. Thus I chose Tref=300. Somewhere in the middle!

And the turbulenceProperties file is:

simulationType laminar;

 RASModel laminar;

turbulence off;

printCoeffs off;

The g file tells the solver the acceleration due to gravity, as well as the direction:

dimensions [0 1 -2 0 0 0 0];
value (0 -9.81 0);

Boundary Conditions

In the “zero” folder, we need the following files: p, p_rgh, T, U, and alphat (this file needs to be present… however it is not used given the laminar simulationType.


dimensions [0 0 0 1 0 0 0];

internalField uniform 273;

 type fixedValue;
 value uniform 373;
 type fixedValue;
 value uniform 273;
 type zeroGradient;


dimensions [0 2 -2 0 0 0 0];

internalField uniform 0;

type fixedFluxPressure;
rho rhok;
value uniform 0;

type fixedFluxPressure;
rho rhok;
value uniform 0;

type fixedFluxPressure;
rho rhok;
value uniform 0;


dimensions [0 2 -2 0 0 0 0];

internalField uniform 0;

 type calculated;
 value $internalField;

 type calculated;
 value $internalField;

 type calculated;
 value $internalField;


dimensions [0 1 -1 0 0 0 0];

internalField uniform (0 0 0);

 type noSlip;

 type noSlip;

 type noSlip;

Simulation Results

The results (as always) are the best part. But especially for this case since they are so nice to look at! I have made a couple animations of temperature fields and contours. Enjoy.

3D Temperature Contours


Temperature Field – Slice Through xy Plane



This case demonstrated the simple set up of a case using buoyantBoussinesqPimpleFoam. The case (Rayleigh-Bénard convection) was simulated in 3D on a fine grid.

Comments and questions are welcome! Keep in mind I set this case up very quickly.


► Time-Varying Cylinder Motion in Cross-flow: timeVaryingFixedUniformValue
  10 Jun, 2017

This post is a simple demonstration of the timeVaryingFixedUniformValue boundary condition. This boundary condition allows a Dirichlet-type boundary condition to be varied in time. To demonstrate, we will modify the oscillating cylinder case.


Instead of using the oscillating boundary condition for point displacement. We will have the cylinder do two things:

  • Move in a circular motion
  • Move in a sinusoidal decay motion

The basics of this boundary condition are extremely simple. Keep in mind that although (here) we are modifying the pointDisplacement boundary condition for the cylinder, the basics of this BC would be the same if you were doing a time varying boundary condition for say pressure or velocity.

In the pointDisplacement file:

 type timeVaryingUniformFixedValue;
 fileName "prescribedMotion";
 outOfBounds clamp;

fileName points to the file where the time varying boundary condition is defined. Here we used a file called prescribedMotion however you can name it whatever you want. The outOfBounds variable dictates what the simulation should do if the simulation time progresses outside of the time domain defined in the file.

The additional file containing the desired motion prescribedMotion is formatted in the following way:

( 0 (0 0 0))
( 0.005 (-0.0000308418795848531 0.00392682932795517 0))
( 0.01 (-0.0001233599085671 0.00785268976953207 0))
( 0.015 (-0.000277531259507496 0.0117766126774107 0))
( 0.02 (-0.00049331789293211 0.0156976298823283 0))
( 9.99 (-0.0001233599085671 -0.00785268976953189 0))
( 9.995 (-0.0000308418795848531 -0.00392682932795479 0))
( 10 (0 -3.06161699786838E-016 0))

The first column is the time in seconds, and the vector defines the point displacement. In the present tutorial, these points were calculated in libreOffice and then exported into the text file.  I arbitrarily made up the motions purely for the sake of making this blog post.

The circular motion was defined as:

x=0.25\cos\left(\pi t\right)-0.25 and y=0.25\sin\left(\pi t\right)

Decaying sinusoidal motion was:

y=\sin(\pi t) \exp\left(-t/2\right)

The rest of the set-up is identical to the set-up in the oscillating cylinder example. The solver pimpleDyMFoam is then run.


Circular Motion


Sinusoidal Decay



This post demonstrated how a more complicated motion can be prescribed by using a little math and the timeVaryingUniformFixedValue boundary condition. Always like to hear questions and comments! Has anybody else done something like this?


► Equations for Steady 1D Isentropic Flow
    5 Dec, 2016

The equations used to describe steady 1D isentropic flow are derived from conservation of mass, momentum, and energy, as well as an equation of state (typically the ideal gas law).

These equations are typically described as ratios between the local static properties (p, T, \rho) and their stagnation property as a function of Mach number and the ratio of specific heats, \gamma. Recall that Mach number is the ratio between the velocity and the speed of sound, a.

These ratios are given here:

Temperature: T_o/T = \left(1+\frac{\gamma -1}{2} M^2\right)

Pressure: P_o/P = \left(1+\frac{\gamma -1}{2} M^2\right)^{\frac{\gamma}{\gamma-1}}

Density: \rho_o/\rho = \left(1+\frac{\gamma -1}{2} M^2\right)^{\frac{1}{\gamma-1}}

In addition to the relationships between static and stagnation properties, 1D nozzle flow offers an equation regarding the choked cross-sectional flow area (recall that the flow is choked when M=1.)

A/A^* = \frac{1}{M}\left(\left(\frac{2}{\gamma+1}\right)\left(1+\frac{\gamma -1}{2} M^2\right)\right)^{\frac{\gamma+1}{2\left(\gamma-1\right)}}

Some excellent references for these equations are:

  • Gas Dynamics Vol. I – Zucrow and Hoffman – 1976
  • Gas Dynamics – John and Keith – 2nd Ed. – 2006


► Establishing Grid Convergence
    9 Sep, 2016

Establishing grid convergence is a necessity in any numerical study. It is essential to verify that the equations are being solved correctly and that the solution is insensitive to the grid resolution. Some great background and details can be found from NASA:

First, here is a summary the equations and steps discussed here (in case you don’t want to read the whole example):

  1. Complete at least 3 simulations (Coarse, medium, fine) with a constant refinement ratio, r, between them (in our example we use r=2)
  2. Choose a parameter indicative of grid convergence. In most cases, this should be the parameter you are studying. ie if you are studying drag, you would use drag.
  3. Calculate the order of convergence, p, using:
    • p=\ln(\frac{(f_3-f_2)}{(f_2-f_1)}) / \ln(r)
  4. Perform a Richardson extrapolation to predict the value at h=0
    • f_{h=0}=f_{fine}+\frac{f_1-f_2}{r^p-1}
  5. Calculate grid convergence index (GCI) for the medium and fine refinement levels
    • GCI=\frac{F_s |e|}{r^p-1}
  6. Ensure that grids are in the asymptotic range of convergence by checking:
    • \frac{GCI_{2,3}}{r^p \times GCI_{1,2}} \approxeq 1

So what is a grid convergence study? Well, the gist of it is that you refine the mesh several times and compare the solutions to estimate the error from discretization. There are several strategies to do this. However, I have always been a fan of the following method: Create a very fine grid and simulate the flow problem. Then reduce the grid density twice, creating a medium grid, and coarse grid.

There are several strategies to do this. However, I have always been a fan of the following method: Create a very fine grid and simulate the flow problem. Then reduce the grid density twice, creating a medium grid, and coarse grid. To keep the process simple, the ratio of refinement should be the same for each step. ie. if you reduce the grid spacing by 2 in each direction between the fine and medium grid, you should reduce it again by 2 between the medium and coarse grid. In the current example, I generated three grids for the cavity problem with a refinement ratio of 2:

  • Fine grid: 80 cells in each direction – (6400 cells)
  • Medium grid: 40 cells in each direction – (1600 cells)
  • Coarse grid: 20 cells in each direction – (400 cells)

Velocity contour plots are shown in the following figures:

We can see from the figures that the quality of the simulation improves as the grid is refined. However, the point of a grid convergence study is to quantify this improvement and to provide insight into the actual quality of the fine grid.

The accuracy of the fine grid is then examined by calculating the effective order of convergence, performing a Richardson extrapolation, and calculating the grid convergence index. As well, (as stated in the article from NASA), it is helpful to ensure that you are in the asymptotic range of convergence.

What are we examining?

It is very important at the start of a CFD study to know what you are going to do with the result. This is because different parameters will converge differently. For example, if you are studying a higher order parameter such as local wall friction, your grid requirements will probably be more strict than if you are studying an integral (and hence lower order) parameter such as coefficient of drag. You only need to ensure that the property or parameter that you are studying is grid independent. For example, if you were studying the pressure increase across a shockwave, you would not check that wall friction somewhere else in the simulation was converged (unless you were studying wall friction as well).  If you want to be able to analyze any property in a simulation, both high and low order, then you should do a very rigorous grid convergence study of primitive, integrated and derived variables.

In our test case lets pretend that in our research or engineering project that we are interested in the centerline pressure and velocity. In particular, let’s say we are interested in the profiles of these variables along the centerline as well as their peak values. The centerline profiles of velocity and pressure are shown in the following figures:

Calculate the effective order of convergence

From our simulations, we have generated the following data for minimum pressure along the centerline, and maximum velocity along the centerline:


The order of convergence, p, is calculated with the following equation:

p=\ln(\frac{(f_3-f_2)}{(f_2-f_1)}) / \ln(r)

where r is the ratio of refinement, and f1 to f3 are the results from each grid level.

Using the data we found, p = 1.84 for the minimum pressure and p=1.81 for the maximum velocity.

Perform Richardson extrapolation of the results

Once we have an effective order, p, we can do a Richardson extrapolation. This is an estimate of the true value of the parameter we are examining based on our order of convergence. The extrapolation can be performed with the following equation:


recall that r is the refinement ratio and is h_2/h_1 which in this case is 2.

Using this equation we get the Richardson extrapolated results:

  • P_{min} at h=0  -> -0.029941
  • V_{max} at h=o  -> 0.2954332

The results are plotted here:



Calculate the Grid Convergence Index (GCI)

Grid convergence index is a standardized way to report grid convergence quality. It is calculated at refinement steps. Thus we will calculate a GCI for steps from grids 3 to 2, and from 2 to 1.

The equation to compute grid convergence index is:

GCI=\frac{F_s |e|}{r^p-1}

where e is the error between the two grids and F_s is an optional (but always recommended) safety factor.

Now we can calculate the grid convergence indices for the minimum pressure and maximum velocity.

Minimum pressure

  • GCI_{2,3} = 1.25 \times |\frac{-0.028836-(-0.025987)}{-0.028836}|/(2^{1.84}-1) \times 100 \% = 4.788 \%
  • GCI_{1,2} = 1.25 \times |\frac{-0.029632-(-0.028836)}{-0.029632}|/(2^{1.84}-1) \times 100 \% = 1.302 \%

Max velocity

  • GCI_{2,3} =1.25 \times | \frac{0.2892-0.27359}{0.2892}|/(2^{1.81}-1) \times 100 \% =2.69187 \%
  • GCI_{1,2} = 1.25 \times |\frac{0.29365-0.2892}{0.29365}|/(2^{1.84}-1) \times 100 \% = 0.7559 \%

Check that we are in the asymptotic range of convergence

It is also necessary to check that we are examing grid converegence within the asymptotic range of convergence. If we are not in the asymtotic range this means that we are not asymptotically approaching a converged answer and thus our solution is definitely not grid indipendent.

With three grids, this can be checked with the following relationship:

\frac{GCI_{2,3}}{r^p \times GCI_{1,2}} \approxeq 1

If we are in the asymptotic range then the left-hand side of the above equation should be approximately equal to 1.

In our example we get:

Minimum Pressure

1.0276 \approxeq 1

Minimum Velocity

1.0154 \approxeq 1

Applying Richardson extrapolation to a range of data

Alternatively to choosing a single value like minimum pressure or maximum velocity. Richardson extrapolation can be applied to a range of data. For example, we can use the equation for Richardson extrapolation to estimate the entire profile of pressure and velocity along the centerline at h=0.

This is shown here:

Conclusions and Additional References

In this post, we used the cavity tutorial from OpenFOAM to do a simple grid convergence study. We established an order of convergence, performed Richardson extrapolation, calculated grid convergence indices (GCI) and checked for the asymptotic range of convergence.

As I said before, the NASA resource is very helpful and covers a similar example:

As well the papers by Roache are excellent reading for anybody doing numerical analysis in fluids:

► The Ahmed Body
    7 Sep, 2016

The Ahmed body is a geometric shape first proposed by Ahmed and Ramm in 1984. The shape provides a model to study geometric effects on the wakes of ground vehicles (like cars).

Image highlights:

In this post, I will use simpleFoam to simulate the Ahmed body at a Reynolds number of 10^6 using the k-omega SST turbulence model. The geometry was meshed using cfMesh which I will briefly discuss as well. Here is a breakdown of this post:

  1. Geometry Definition
  2. Meshing with cfMesh
  3. Boundary Conditions
  4. Results

The files for this case can be downloaded here:

Download Case Files

Note: I ran this case on my computer with 6 Intel – i7 (3.2 GHz) cores and 32 Gb of RAM. Throughout the simulation about 20-ish gigs of RAM were used.

Geometry Definition

STL Creation

The meshing utility cfMesh is similar to snappyHexMesh in that it depends on a geometry file of some type (.stl etc) to create the mesh. But it is different in that the entire domain must be part of the definition.

The ahmed body geometry can be found:

For this simulation, I generated the geometry using SolidWorks. But this wasn’t for any particular reason other than that it was quick since I am familiar with it.

Preparation for Meshing

Once you have an STL file, you could go straight ahead to meshing it with cfMesh. However, some simple preparations to the STL geometry can improve the quality of the mesh created, and make setting up the case easier.

In particular, when you create the STL file in SolidWorks (or your 3D modeller of choice)  it contains no information about the boundaries and patches. As well, cfMesh works best if the geometry is defined using a .fms or .ftr file format.

Use surfaceFeatureEdge utility to extract edge information and create a .ftr file. Firstly let’s extract edge and face information from our STL file. We also define an angle. This angle tells cfMesh that any angle change large than this (in our case I chose 20 degrees) is a feature edge that must be matched.

surfaceFeatureEdge volume.stl -angle 20

After we run this, the new file contains a bunch of face and patch information. 13 surfaces with feature edges were extracted. The first 6 (volume_0 to volume_5) are the boundaries of the simulation (inlet, outlet, ground, front, back, and top).

mergeSurfacePatches ahmed -patchNames '(volume_6 volume_7 volume_8 volume_9 volume_10 volume_11 volume_12)'

After running this command, now contains 7 patches. We are now ready to move on to setting up cfMesh.

Meshing with cfMesh

Set up meshDict file in the system folder

Similar to snappyHexMesh and blockMesh, cfMesh using a dictionary file to control its parameters; meshDict. In this dictionary file we will be modifying a few parameters.

Tell cfMesh what file is to be meshed:

surfaceFile "";

Set the default grid size:

maxCellSize 0.2;

Set up refinement zones:

We want to set up two refinement zones; a larger one to capture most of the flow further away from the body (including the far wake), and a smaller more refined one to capture the near wake and the flow very close to the Ahmed body.

    cellSize 25e-3;
    type box;
    centre (2.4 0 6.5);
    lengthX 1;
    lengthY 1;
    lengthZ 3.5;

    cellSize 5e-3;
    type box;
    centre (2.4 0 7);
    lengthX 0.5;
    lengthY 1;
    lengthZ 2;


Set up boundaries to be renamed:

 newName ground;
 type wall;
 newName back;
 type wall;
 newName inlet;
 type patch;
 newName front;
 type wall;
 newName outlet;
 type wall;
 newName top;
 type patch;
 newName ahmed;
 type wall;

Set up boundary layering:

We require boundary layer on both the Ahmed body, as well as the volume_0 patch. Recall that the Ahmed body is surface mounted!

 nLayers 10;
 thicknessRatio 1.1;
 maxFirstLayerThickness 5e-3;
 nLayers 10;
 thicknessRatio 1.05;
 maxFirstLayerThickness 10e-3;


Run cfMesh

We want to create a hex-dominant grid. This means that the 3D grid will consist primarily of hex cells. To achieve this we will use the cartesianMesh solver from cfMesh.

The results are shown below. The final mesh consisted of approximately 16.9 million cells. The majority of the cells were hexahedra (approximately 99%).


Boundary Conditions for the Solver

For this case, we are going to run a steady-state RANS simulation using the kwSST model and the solver simpleFOAM. This is simply to demonstrate the running of the solver.

The boundary conditions used are summarized in the following table:


As you can see I have used wall functions for the wall boundary conditions. This is due to the very small cell requirements that would be required to resolve the boundary layer on the ground, as well as on the Ahmed body which is at a Reynolds number of one million.

Simulation Results

The simulation took about a day and a half on 6 cores. Throughout the simulation, about 20 gb of RAM was used.


Streamlines and pressure on surface:


Vorticity surface in the near-wake



In this post, we meshed and simulated a surface-mounted Ahmed body at a Reynolds number of one million. We meshed it using the open-source meshing add-on cfMesh. We then solved it as a steady-state RANS simulation using the kwSST turbulence model, and the simpleFOAM solver.

The results gave some nice figures and a qualitatively correct result! And it was pretty fun. cfMesh was extremely easy to use and required much less user input than its OpenCFD counterpart snappyHexMesh.

Some references:

For more information on the Ahmed body:

Some papers studying the Ahmed body:

-See the reference on the above CFD Online page!


As usual please comment and let me know what you think!





► Oscillating Cylinder in Laminar Crossflow – pimpleDyMFoam
  19 Jul, 2016

In this post I am going to simulate an oscillating cylinder in a cross-flow… just for fun… and to provide an additional tutorial case for those wishing to use some of the dynamic meshing features of OpenFOAM.

The case I am going to simulate is a cylinder in a Reynolds number 200 cross-flow (U=2 m/s, D=1 m, nu = 0.01 m^2/s), oscillating at a rate of 0.2 hz.

Tutorial Files

The tutorial files for this case can be downloaded from here:

Download Tutorial Files

Please let me know if the download does not work or if there is a problem running the tutorial files. Note: I ran this case in parallel on a relatively fast 6-core computer. It will take a long time if you try to run it as single core.

Mesh Generation

For this simulation, I built a simple two dimensional O-grid around the cylinder which joined to a series of blocks making a rectangular domain. I built this using the native OpenFOAM meshing utility blockMesh (which I like a lot).

Fig: Grid

If you are wondering about the blockMesh set up… I intend to a blockMesh tutorial post… not that it’s all that necessary since the OpenFOAM manual covers it pretty well.

Case Set-up


When running a dynamic mesh case a solver runs as part of the solution and solves for the new grid at each timestep. Several are available in OpenFOAM and I am not really trying to do a full post on that right now. So I’ll just tell you that in this case I decided to use the displacementLaplacian solver.

Along with the solver one must define the coefficients that go with the solver. For most of the solvers this means setting the diffusivity for the Laplace solver. Since I am relatively new to these types of solutions I did what we all do… and turned to the great CFD online forum! A discussion in this post ( made me think that a good starting point would be the inverseDistance model for the mesh diffusivity.

In the end my dynamicMeshDict (which belongs in the constant folder) looked like:

dynamicFvMesh dynamicMotionSolverFvMesh;

motionSolverLibs ( "" );

solver displacementLaplacian;

      diffusivity inverseDistance 1(cylinder);

The solver produces the grid movements through time as the simulation is performed. Here is what the mesh looks like while it is moving! I added the stationary mesh and the moving mesh. Click on them to get a clearer picture 🙂

Boundary Conditions

The set up for this case is simple. An inlet boundary (on the far left) where I specified the incoming velocity and pressure (both are uniform fixedValue). The top and bottom boundaries are slip boundaries (but could also be freestream depending on your preferred set-up style).

The cylinder itself requires some special treatment because of the moving mesh. It must obey the no-slip condition right? So do we set it as uniform (0 0 0) ? No! The cylinder is moving! Luckily there is a handy boundary condition for this in the U file:

      type movingWallVelocity;
      value uniform (0 0 0);

For a typical simulation using pimpleFoam a p and U file would be all that are required. However, since we are doing a moving mesh simulation there is another parameter that must be solved for and requires boundary conditions… pointDisplacement.

For the pointDisplacement boundary conditions, we know that all of the outer edges should NOT move. Therefore they are all fixed with a type of fixedValue and  a value of uniform (0 0 0).

The cylinder however is moving and requires a definition. In this simulation we are simulating and oscillating cylinder. Since we are using the displacement solver the type is oscillatingDisplacement. We input and omega (rad/s) and an amplitude (m) in the following way:

 type oscillatingDisplacement;
 omega 1.256; 
 amplitude (0 0.5 0); // Max piston stroke
 value uniform (0 0 0);


Yay! Now its time to look at the results. Well since I am not doing this for any particular scientific study… let’s look at some pretty pictures!

Here is an animation of vorticity:

Wake of Oscillating Cylinder

Looks pretty nice! I personally have a big nerd love for vortex shedding…. I don’t know why.

Obviously if you intend to do any scientific or engineering work with this type of problem you would need to think very carefully about the grid resolution, diffusivity model, Reynolds number, oscillation frequency  etc. All of these were arbitrarily selected here to facilitate the blog post and to provide a nice tutorial example!


In this post I briefly covered the set-up of this type of dynamic meshing problem. The main difference for running a dynamic mesh case is that you require a dynamic mesh solver (you must specify in the dynamicMeshDict), and you also require boundary conditions for that solver.

Let me know if there are any problems with this blog post or with the tutorial files provided.

Hanley Innovations top

► Hanley Innovations Upgrades Stallion 3D to Version 5.0
  18 Jul, 2017
The CAD for the King Air was obtained from Thingiverse

Stallion 3D is a 3D aerodynamics analysis software package developed by Dr. Patrick Hanley of Hanley Innovations in Ocala, FL. Starting with only the STL file, Stallion 3D is an all-in-one digital tool that rapidly validate conceptual and preliminary aerodynamic designs of aircraft, UAVs, hydrofoil and road vehicles.

  Version 5.0 has the following features:
  • Built-in automatic grid generation
  • Built-in 3D compressible Euler Solver for fast aerodynamics analysis.
  • Built-in 3D laminar Navier-Stokes solver
  • Built-in 3D Reynolds Averaged Navier-Stokes (RANS) solver
  • Multi-core flow solver processing on your Windows laptop or desktop using OpenMP
  • Inputs STL files for processing
  • Built-in wing/hydrofoil geometry creation tool
  • Enables stability derivative computation using quasi-steady rigid body rotation
  • Up to 100 actuator disc (RANS solver only) for simulating jets and prop wash
  • Reports the lift, drag and moment coefficients
  • Reports the lift, drag and moment magnitudes
  • Plots surface pressure, velocity, Mach number and temperatures
  • Produces 2-d plots of Cp and other quantities along constant coordinates line along the structure
The introductory price of Stallion 3D 5.0 is $3,495 for the yearly subscription or $8,000.  The software is also available in Lab and Class Packages.

 For more information, please visit or call us at (352) 261-3376.
► Airfoil Digitizer
  17 Jun, 2017

Airfoil Digitizer is a software package for extracting airfoil data files from images. The software accepts images in the jpg, gif, bmp, png and tiff formats. Airfoil data can be exported as AutoCAD DXF files (line entities), UIUC airfoil database format and Hanley Innovations VisualFoil Format.

The following tutorial show how to use Airfoil Digitizer to obtain hard to find airfoil ordinates from pictures.

More information about the software can be found at the following url:

Thanks for reading.

► Your In-House CFD Capability
  15 Feb, 2017

Have you ever wish for the power to solve your 3D aerodynamics analysis problems within your company just at the push of a button?  Stallion 3D gives you this very power using your MS Windows laptop or desktop computers. The software provides accurate CL, CD, & CM numbers directly from CAD geometries without the need for user-grid-generation and costly cloud computing.

Stallion 3D v 4 is the only MS windows software that enables you to solve turbulent compressible flows on your PC.  It utilizes the power that is hidden in your personal computer (64 bit & multi-cores technologies). The software simultaneously solves seven unsteady non-linear partial differential equations on your PC. Five of these equations (the Reynolds averaged Navier-Stokes, RANs) ensure conservation of mass, momentum and energy for a compressible fluid. Two additional equations captures the dynamics of a turbulent flow field.

Unlike other CFD software that require you to purchase a grid generation software (and spend days generating a grid), grid generation is automatic and is included within Stallion 3D.  Results are often obtained within a few hours after opening the software.

 Do you need to analyze upwind and down wind sails?  Do you need data for wings and ship stabilizers at 10,  40, 80, 120 degrees angles and beyond? Do you need accurate lift, drag & temperature predictions at subsonic, transonic and supersonic flows? Stallion 3D can handle all flow speeds for any geometry all on your ordinary PC.

Tutorials, videos and more information about Stallion 3D version 4.0 can be found at:

If your have any questions about this article, please call me at (352) 261-3376 or visit

About Patrick Hanley, Ph.D.
Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

► Avoid Testing Pitfalls
  24 Jan, 2017

The only way to know if your idea will work is to test it.  Rest assured, as a design engineer your idea and designs will be tested over and over again often in front of a crowd of people.

As an aerodynamics design engineer, Stallion 3D helps you to avoid the testing pitfalls that would otherwise keep you awake at night. An advantage of Stallion 3D is it enables you to test your designs on the privacy of your laptop or desktop before your company actually builds a prototype.  As someone who uses Stallion 3D for consulting, I find it very exciting to see my designs flying the way they were simulated in the software. Stallion 3D will assure that your creations are airworthy before they are tested in front of a crowd.

I developed Stallion 3D for engineers who have an innate love and aptitude for aerodynamics but who do not want to deal with the hassles of standard CFD programs.  Innovative technologies should always take a few steps out of an existing process to make the journey more efficient.  Stallion 3D enables you to skip the painful step of grid (mesh) generation. This reduces your workflow to just a few seconds to setup and run a 3D aerodynamics case.

Stallion 3D helps you to avoid the common testing pitfalls.
1. UAV instabilities and takeoff problems
2. Underwhelming range and endurance
3. Pitch-up instabilities
4. Incorrect control surface settings at launch and level flight
5. Not enough propulsive force (thrust) due to excess drag and weight.

Are the results of Stallion 3D accurate?  Please visit the following page to see the latest validations.

If your have any questions about this article, please call me at (352) 261-3376 or visit

About Patrick Hanley, Ph.D.
Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.
► Flying Wing UAV: Design and Analysis
  15 Jan, 2017

3DFoil is a design and analysis software for wings, hydrofoils, sails and other aerodynamic surfaces. It requires a computer running MS Windows 7,8 and 10.

I wrote the 3DFoil software several years ago using a vortex lattice approach. The vortex lattice method in the code is based on vortex rings (as opposed to the horse shoe vortex approach).  The vortex ring method allows for wing twist (geometric and aerodynamic) so a designer can fashion the wing for drag reduction and prevent tip stall by optimizing the amount of washout.  The approach also allows sweep (backwards & forwards) and multiple dihedral/anhedral angles.
Another feature that I designed into 3DFoil is the capability to predict profile drag and stall. This is done by analyzing the wing cross sections with a linear strength vortex panel method and an ordinary differential equation boundary layer solver.   The software utilize the solution of the boundary layer solver to predict the locations of the transition and separation points.

The following video shows how to use 3DFoil to design and analyze a flying wing UAV aircraft. 3DFoil's user interface is based on the multi-surface approach. In this method, the wing is designed using multiple tapered surface where the designer can specify airfoil shapes, sweep, dihedral angles and twist. With this approach, the designer can see the contribution to the lift, drag and moments for each surface.  Towards the end of the video, I show how the multi-surface approach is used to design effective winglets by comparing the profile drag and induced drag generated by the winglet surfaces. The video also shows how to find the longitudinal and lateral static stability of the wing.

The following steps are used to design and analyze the wing in 3DFoil:
1. Input the dimensions and sweep half of the wing (half span)
2. Input the dimensions and sweep of the winglet.
3. Join the winglet and main wing.
4. Generate the full aircraft using the mirror image insert function.
5. Find the lift drag and moments
6. Compute longitudinal and lateral stability
7. Look at the contributions of the surfaces.
8. Verify that the winglets provide drag reduction.

More information about 3DFoil can be found at the following url:

About Patrick Hanley, Ph.D.
Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

► Corvette C7 Aerodynamics
    7 Jan, 2017

The CAD file for the Corvette C7 aerodynamics study in Stallion 3D version 4 was obtained from Mustafa Asan revision on GrabCAD.  The file was converted from the STP format to the STL format required in Stallion 3D using

Once the Corvette was imported into Stallion 3D, I applied ground effect and a speed of 75 miles per hour at zero angle of attack.  The flow setup took just seconds in Stallion 3D and grid generation was completely automatic.  The software allows the user to choose a grid size setting and I chose the option the produced a total of 345,552 cells in the computational domain.

I chose the Reynolds Averaged Navier-Stokes (RANS) equations solver for this example.  In Stallion 3D, the RANS equations are solve along with the k-e turbulence model.  A wall function approach is used at the boundaries.

The results were obtained after 10,950 iterations on a quad core laptop computer running at 2.0 GHz under MS Windows 10.

The results for the Corvette C7 model  are summarized below:

Lift Coefficient:  0.227
Friction Drag Coefficient: 0.0124
Pressure Drag Coefficient: 0.413
Total Drag Coefficient: 0.426

Stallion 3D HIST Solver:  Reynolds Averaged Navier-Stokes Equations
Turbulence Model: k-e
Number of Cells: 345,552
Grid: Built-in automatic grid generation

Run time: 7 hours

The coefficients were computed based on a frontal area of 2.4 square meters.

The following are images of the same solution from different views in Stallion 3D.  The streamlines are all initiated near the ground plane 2 meters ahead of the car.

Top View

Side View

Bottom View

Stallion 3D utilizes a new technology (Hanley Innovations Surface Treatment or HIST) that enables design engineers to quickly analyze their CAD models on an ordinary Window PC.  We call this SameDayCFD. This unique technology is my original work and was not derived from any existing software codes.  More information about Stallion 3D can be found at:

Do not hesitate to contact us if you have any questions.  More information can be found at

Thanks for reading.

About Patrick Hanley, Ph.D.
Dr. Patrick Hanley is the owner of Hanley Innovations. He received his Ph.D. degree in fluid dynamics for Massachusetts Institute of Technology (MIT) department of Aeronautics and Astronautics (Course XVI). Dr. Hanley is the author of Stallion 3D, MultiSurface Aerodynamics, MultiElement Airfoils, VisualFoil and the booklet Aerodynamics in Plain English.

FlowViz - Fluid Dynamics top

► As this video says, ‘What does sound look like?’....
    2 Aug, 2014

As this video says, ‘What does sound look like?’. This is one of those things that’s hard to visualise whether you’ve never thought to try, or you’re a hardened fluid dynamic nerd who’s seen hundreds of FlowViz videos. However this video shows it in unbelievable detail and clarity.

This is made possible due to the Schlieren flow viz technique, which allows us to see regions of air with different densities - as is so well explained during the video.

Something I find amazing is one of the simpler examples in this video - the speaker. Those waves spreading out are what our ears hear as sound, simply changing the frequency of the waves changes the note we here.

► A while ago you’ll have seen I posted some FlowViz of a...
  24 Jul, 2014

A while ago you’ll have seen I posted some FlowViz of a special leading edge in one of our wind tunnels. It is one of the exhibits inspired by nature that was on show at the Royal Society’s Summer Science Exhibition. Well it has now gone all the way from theory to practice as McLaren used it on their rear wing this weekend.

What’s even better is that it fits in perfectly with this week’s look at stall and separation. Yes an F1 wing can stall just like the wings we’ve seen this week. The only difference is, that instead of losing lift, a car loses downforce. These wavy leading edges are probably aimed at stopping separation and stall more efficiently than by using vortex generators.

F1 teams use fluorescent paint like the picture HERE to see if their wings are stalling.

► As promised, how to avoid stall - well delay it at least. This...
  23 Jul, 2014

As promised, how to avoid stall - well delay it at least. This video shows a fantastic real life example of using vortex generators, as visualised by the cotton tufts. Stall occurs when the flow over the wing separates.

Vortex generators work by mixing the fast moving air outside the boundary layer with the slow moving flow inside. This adds momentum to the Boundary layer and helps it remain attached. Adding these vortex generators post production can help improve an aircrafts performance. 

These devices aren’t just used on planes but also on Formula 1 cars to manage the flow around the car.

► While searching for yesterday’s video I came across this...
  22 Jul, 2014

While searching for yesterday’s video I came across this one. It’s not an unusual video but does demonstrate something that I still think is quite astounding - the air doesn’t always flow straight over the wing but can actually reverse it’s direction and flow towards the front of the wing.

This situation is called stall and occurs when the flow separates from the upper surface of the wing. (This can be seen well in yesterdays post at 3m05s & 4m49s onwards). This happens at either large angles of attack - like when a plane takes off - or when the plane’s air speed is too slow. At this point the plane loses a large amount of lift which is extremely undesirable as it may not be able to support its own weight. 

In this video the first instance occurs at about 25s. The tufts attached to the wing allow us to see clearly the change in the flow direction. 

There are ways to stop this happening however as we will see tomorrow!

► Back in the day they really knew how to make educational videos....
  21 Jul, 2014

Back in the day they really knew how to make educational videos. I have previously shown a video explaining water waves and, after posting footage from our new FlowViz wind tunnel, I came across this gem. 

A simple explanation of camber, flaps, stall, separation and slots for a basic aerofoil. There are a whole heap of these videos on youtube, check them out if you want to find out more!

► For those that can’t make it to the Royal Society Summer...
    3 Jul, 2014

For those that can’t make it to the Royal Society Summer Exhibition of Science here’s a video to make up for it! This is what you can see in the wind tunnel on our stand. Despite being an old fashioned method of flowviz, it still gives great insight into the flow over a wing and it’s wake.

There is, a Karman Vortex street behind the wing at zero incidence and small angles of attack, clear separation as the incidence is increased and a large turbulent wake.

If you want to ask us any questions on this, like how the flow viz works, or anything else aero/fluid related we’ve got a Twitter Q&A session tomorrow from midday. Tweet to @aeflowcontrol with the hashtag #asksummerscience.

If you want to see some more of what we’re doing check out the links:

CFD and others... top

► Are High-Order CFD Solvers Ready for Industrial LES?
    1 Jan, 2018
The potential of high-order methods (order > 2nd) is higher accuracy at lower cost than low order methods (1st or 2nd order). This potential has been conclusively demonstrated for benchmark scale-resolving simulations (such as large eddy simulation, or LES) by multiple international workshops on high-order CFD methods.

For industrial LES, in addition to accuracy and efficiency, there are several other important factors to consider:

  • Ability to handle complex geometries, and ease of mesh generation
  • Robustness for a wide variety of flow problems
  • Scalability on supercomputers
For general-purpose industry applications, methods capable of handling unstructured meshes are preferred because of the ease in mesh generation, and load balancing on parallel architectures. DG and related methods such as SD and FR/CPR have received much attention because of their geometric flexibility and scalability. They have matured to become quite robust for a wide range of applications. 

Our own research effort has led to the development of a high-order solver based on the FR/CPR method called hpMusic. We recently performed a benchmark LES comparison between hpMusic and a leading commercial solver, on the same family of hybrid meshes at a transonic condition with a Reynolds number more than 1M. The 3rd order hpMusic simulation has 9.6M degrees of freedom (DOFs), and costs about 1/3 the CPU time of the 2nd order simulation, which has 28.7M DOFs, using the commercial solver. Furthermore, the 3rd order simulation is much more accurate as shown in Figure 1. It is estimated that hpMusic would be an order magnitude faster to achieve a similar accuracy. This study will be presented at AIAA's SciTech 2018 conference next week.

(a) hpMusic 3rd Order, 9.6M DOFs
(b) Commercial Solver, 2nd Order, 28.7M DOFs
Figure 1. Comparison of Q-criterion and Schlieren  

I certainly believe high-order solvers are ready for industrial LES. In fact, the commercial version of our high-order solver, hoMusic (pronounced hi-o-music), is announced by hoCFD LLC (disclaimer: I am the company founder). Give it a try for your problems, and you may be surprised. Academic and trial uses are completely free. Just visit to download the solver. A GUI has been developed to simplify problem setup. Your thoughts and comments are highly welcome.

Happy 2018!     

► Sub-grid Scale (SGS) Stress Models in Large Eddy Simulation
  16 Nov, 2017
The simulation of turbulent flow has been a considerable challenge for many decades. There are three main approaches to compute turbulence: 1) the Reynolds averaged Navier-Stokes (RANS) approach, in which all turbulence scales are modeled; 2) the Direct Numerical Simulations (DNS) approach, in which all scales are resolved; 3) the Large Eddy Simulation (LES) approach, in which large scales are computed, while the small scales are modeled. I really like the following picture comparing DNS, LES and RANS.

DNS (left), LES (middle) and RANS (right) predictions of a turbulent jet. - A. Maries, University of Pittsburgh

Although the RANS approach has achieved wide-spread success in engineering design, some applications call for LES, e.g., flow at high-angles of attack. The spatial filtering of a non-linear PDE results in a SGS term, which needs to be modeled based on the resolved field. The earliest SGS model was the Smagorinsky model, which relates the SGS stress with the rate-of-strain tensor. The purpose of the SGS model is to dissipate energy at a rate that is physically correct. Later an improved version called the dynamic Smagorinsky model was developed by Germano et al, and demonstrated much better results.

In CFD, physics and numerics are often intertwined very tightly, and one may draw erroneous conclusions if not careful. Personally, I believe the debate regarding SGS models can offer some valuable lessons regarding physics vs numerics.

It is well known that a central finite difference scheme does not contain numerical dissipation.  However, time integration can introduce dissipation. For example, a 2nd order central difference scheme is linearly stable with the SSP RK3 scheme (subject to a CFL condition), and does contain numerical dissipation. When this scheme is used to perform a LES, the simulation will blow up without a SGS model because of a lack of dissipation for eddies at high wave numbers. It is easy to conclude that the successful LES is because the SGS stress is properly modeled. A recent study with the Burger's equation strongly disputes this conclusion. It was shown that the SGS stress from the Smargorinsky model does not correlate well with the physical SGS stress. Therefore, the role of the SGS model, in the above scenario, was to stabilize the simulation by adding numerical dissipation.

For numerical methods which have natural dissipation at high-wave numbers, such as the DG, SD or FR/CPR methods, or methods with spatial filtering, the SGS model can damage the solution quality because this extra dissipation is not needed for stability. For such methods, there have been overwhelming evidence in the literature to support the use of implicit LES (ILES), where the SGS stress simply vanishes. In effect, the numerical dissipation in these methods serves as the SGS model. Personally, I would prefer to call such simulations coarse DNS, i.e., DNS on coarse meshes which do not resolve all scales.

I understand this topic may be controversial. Please do leave a comment if you agree or disagree. I want to emphasize that I support physics-based SGS models.
► 2016: What a Year!
    3 Jan, 2017
2016 is undoubtedly the most extraordinary year for small-odds events. Take sports, for example:
  • Leicester won the Premier League in England defying odds of 5000 to 1
  • Cubs won World Series after 108 years waiting
In politics, I do not believe many people truly believed Britain would exit the EU, and Trump would become the next US president.

From a personal level, I also experienced an equally extraordinary event: the coup in Turkey.

The 9th International Conference on CFD (ICCFD9) took place on July 11-15, 2016 in the historic city of Istanbul. A terror attack on the Istanbul International airport occurred less than two weeks before ICCFD9 was to start. We were informed that ICCFD9 would still take place although many attendees cancelled their trips. We figured that two terror attacks at the same place within a month were quite unlikely, and decided to go to Istanbul to attend and support the conference. 

Given the extraordinary circumstances, the conference organizers did a fine job in pulling the conference through. More than half of the attendees withdrew their papers. Backup papers were used to form two parallel sessions though three sessions were planned originally. We really enjoyed Istanbul with the beautiful natural attractions and friendly people. 

Then on Friday evening, 12 hours before we were supposed to depart Istanbul, a military coup broke out. The government TV station was controlled by the rebels. However, the Turkish President managed to Facetime a private TV station, essentially turning around the event. Soon after, many people went to the bridge, the squares, and overpowered the rebels with bare fists.

A Tank outside my taxi

A beautiful night in Zurich

The trip back to the US was complicated by the fact that the FAA banned all direct flight from Turkey. I was lucky enough to find a new flight, with a stop in Zurich...

In 2016, I lost a very good friend, and CFD pioneer, Professor Jaw-Yen Yang. He suffered a horrific injury from tennis in early 2015. Many of his friends and colleagues gathered in Taipei on December 3-5 2016 to remember him.

This is a CFD blog after all, and so it is important to show at least one CFD picture. In a validation simulation [1] with our high-order solver, hpMusic, we achieved remarkable agreement with experimental heat transfer for a high-pressure turbine configuration. Here is a flow picture.

Computational Schlieren and iso-surfaces of Q-criterion

To close, I wish all of you a very happy 2017!

  1. Laskowski GM, Kopriva J, Michelassi V, Shankaran S, Paliath U, Bhaskaran R, Wang Q, Talnikar C, Wang ZJ, Jia F. Future directions of high fidelity CFD for aerothermal turbomachinery research, analysis and design, AIAA-2016-3322.

► The Linux Version of meshCurve is Now Ready for All to Download
  20 Apr, 2016
The 64-bit version for the Linux operating system is now ready for you to download. Because of the complexities associated with various libraries, we experienced a delay of slightly more than a month. Here is the link again.

Please let us know your experience, good or bad. Good luck!
► Announcing meshCurve: A CAD-free Low Order to High-Order Mesh Converter
  14 Mar, 2016
We are finally ready to release meshCurve to the world!

The description of meshCurve is provided in AIAA Paper No. 2015-2293. The primary developer is Jeremy Ims, who has been supported by NASA and NSF. Zhaowen Duan also made major contributions. By the way, Aerospace America also highlighted meshCurve in its 2015 annual review issue (on page 22). Many congratulations to Jeremy and Zhaowen on this major milestone!

The current version supports both the Mac OS X and Windows (64 bit) operating systems. The Linux version will be released soon.

Here is roughly how meshCurve works. The input is a linear mesh in the CGNS format. Then the user selects which boundary patches should be reconstructed to high-order. After that, geometrically important features are detected. The user can also manually select or delete features. Next the selected patches are reconstructed to add curvature. Finally the interior volume meshes are curved (if necessary). The output mesh is also stored in CGNS format.

We have tested the tool with meshes in the order of a million cells. But I still want to lower your expectation. So try it out yourself and let us know if you like it or hate it. Please do report bugs so that improvements can be made in the future.

Good luck!

Oh, did I mention the tool is completely free? Here is the meshCurve link again.

► An Update on the International Workshops on High-Order CFD Methods
    9 Sep, 2015
The most recent workshop, the 3rd International Workshop on High-Order CFD Methods, took place on January 3-4, 2015 just before the 53rd AIAA Aerospace Sciences Meeting at the Gaylord Palms Resort and Convention Center in Kissimmee, Florida (Orlando). The workshop was co-chaired by H.T. Huynh of NASA Glenn Research Center and Norbert Kroll of DLR, and sponsored by NASA,  AIAA, DLR and the Army Research Office.

Participants came from all over the world, including students, researchers and practitioners from academia, industry and government labs. A wide variety of methods were covered by the attendees. The final agenda and other details from the Workshop are contained on the following NASA web site:

There are still many unfinished businesses, including high-order mesh generation, robust error estimates and hp-adaptations, and efficient solution methods on extreme scale parallel computers. Please mark your calendar for the 4th Workshop which will take place in the breathtaking Greek island, Crete, on the 3rd and 4th of June 2016 just before the Eccomas / 6th European Conference on CFD (ECFD VI). ECCOMAS will feature a dedicated minisymposium, during which selected participants  will be able to present their results. The Workshop cases and other details are contained here:
Hope to see many of you in Greece!

ANSYS Blog top

► Modelithics Talks 3D Components — Why, When, How?
  24 Apr, 2018

The use of 3D modeling in electromagnetic (EM) analysis is a crucial step in circuit design flows, as wireless technology moves to smaller scales and more compact designs. Although planar, “2.5D” EM co-simulation can provide useful insight into interconnect and grounding effects for most planar structures, it is not generally capable of accounting for the coupling between 3D components that can drastically affect design performance. However, full-wave, 3D EM analysis can accurately predict these coupling effects between components. 3D EM simulations allow designers to have a more complete picture of how their designs will function once fabricated. This is especially important for small form factor (SFF), miniaturized wireless devices including wearables.

3D Geometry model for the Mini-Circuits® HFCN-3800+ high pass filter on a 4 mil Rogers RO4350B® test fixture. Plot of electric field around terminals of the HFCN-3800+.

Top: 3D Geometry model for the Mini-Circuits® HFCN-3800+ high pass filter on a 4 mil Rogers RO4350B® test fixture. Bottom: Plot of electric field around terminals of the HFCN-3800+.

When all physical geometries and material properties (of components) are available to the designer, a complete 3D geometry model can be created for use in EM simulation. The advantage of this approach is that all of the electromagnetic field interactions can be captured in the simulation and visualized by the designer. This provides the designer with unique insight into how their circuit is working. The designer is able to see whether or not significant interactions will occur between closely spaced components, or between components and the chosen grounding and packaging environment. Unfortunately, obtaining these geometries and material property details may prove difficult, if not impossible, for most designers since these details are typically manufacturers’ closely guarded intellectual property (IP). And, obtaining these details is only half of the battle. The accuracy of simulation results depends heavily on correct simulation setup, and the ability to mimic the physical measurement environment. In addition to a properly calibrated simulation setup, accurate measurement validation procedure (even if only for selected test cases) can provide extra confidence in the final simulation results.

To help designers avoid these challenges, Modelithics is using its measurement and modeling expertise, along with its time-tested relationships built on trust with selected vendors, to develop a library of encrypted 3D components for use in ANSYS HFSS software for simulating high-frequency EM fields. All models included in the Modelithics COMPLETE+3D Library for ANSYS HFSS are measurement validated (typically on multiple substrates), documented with a model information data sheet and encrypted to protect manufacturer IP. The designer can simply drop the models from the palette onto their test fixture and use the models in their 3D simulation.

3D Model versus measurement agreement for Mini-Circuits® HFCN-3800+ high pass filter on multiple substrates. Solid lines = model data, symbols = measured data, red = magnitude in dB and  blue = phase in degrees.

3D EM simulation is an extremely powerful tool and is growing in popularity with microwave system and circuit designers due to the excellent model-to-measurement results that can be obtained when 3D component models are properly constructed and validated. The ability to encrypt these models should lead to a growing availability of useful libraries for designers seeking to compact their designs and  account for increasingly complex component interactions.

Register for the upcoming webinar to learn more.

Join us for the webinar “Solving RF and Microwave Design Issues Using 3D Component Modeling” May 9 at 11 a.m. and learn where 3D modeling is required for accurate circuit performance prediction. Register Now.

Modelithics’ COMPLETE+3D Library for ANSYS HFSS includes a sampling of 3D models for several different types of components, including inductors, capacitors, SMA connectors, LTCC filters and QFN packages.

This blog was co-authored by Dr. Larry Dunleavy, co-founder of Modelithics, Inc.

The post Modelithics Talks 3D Components — Why, When, How? appeared first on ANSYS.

► RBF Morph makes ANSYS Fluent more Flexible in RIBES Clean Sky Project
  19 Apr, 2018

Way back in 2005, I co-authored a paper for the Fluent News magazine titled “FSI Makes Fluent More Flexible.” Now I’m here to talk about how RBF Morph makes ANSYS Fluent more flexible In RIBES Clean Sky Project and in ways that you can use to make your simulations more efficient.

In the 2005 paper my colleague and I wrote: “Fluid-structure interaction (FSI) is an important and interesting phenomenon, but it is a difficult challenge for numerical modeling. It even poses difficulties for numerical modelers. Structural behavior is a troublesome boundary condition for the CFD analyst, who prefers to assume that boundaries are rigid. The structural analyst, on the other hand, would like to assume that fluid inside or outside a structure merely generates a constant pressure on the walls.”

Thirteen years later, our computational tools have evolved; FSI is now a challenge that we can tackle. Multiphysics simulations, while still complex, are becoming more common in many fields. They are being made easier with enhancements like the advanced mesh morphing technology in RBF Morph, which can embed structural modes in Fluent to make the CFD model flexible. In this case, flexibility refers to the model’s ability to elastically deform under CFD-computed loads.

Image of the Piaggio P1XX

Advanced mesh morphing technology solves fluid-structure interaction (FSI) challenges. Shown here: the Piaggio P1XX.

It’s well known that structural modes and related frequency signatures represent the dynamic behavior of a structure. RBF mesh morphing allows you to import a certain number of modes (the higher the number of modes introduced the lower the related truncation error will be). The modes, which are computed using ANSYS Mechanical, feed data straight into the Fluent solver. This operation makes the shape of the CFD model parametric with respect to modal shapes.

The flexible CFD model can be used for FSI steady-state analysis. Information exchange that is required for two-way FSI is not required anymore. Mesh updating (i.e., elastic deformation of the CFD model under the current computed loads) is performed in Fluent automatically. It is fast and effective, and it works on HPC as well.

FSI functionalities enabled by coupling RBF Morph and Fluent now make it easy to solve many calculation scenarios for industrial applications:

  • Transient FSI with movement prescribed in advance. This includes flapping devices undergoing complex motion. The motion can be computed using FEA and multibody solvers, or it can be acquired experimentally. Structural mode acceleration in the CFD model can be used to set up reduced order models suitable for nonlinear flutter analysis.
  • Steady-state FSI to model structural deformation on a CFD mesh. This can be used for aeronautical and motorsport applications (wing deformation), and lets you steer shape optimization to account for the coupled response. The deflection of the structure can strongly affect the surrounding flow field.
  • Full coupled transient FSI. In this case, a time marching solution lets you capture the interaction between the flow vortices and the structural vibration.

You can get a better understanding of the workflow by watching the video that follows.

Link to video showing fluid interaction with RBF Morph.Fluid Structure Interaction with RBF Morph: Aeroelastic Analysis of a Full Aircraft Model. rbf-morph.

ANSYS A&D Industry Director, Paolo Colombo, was among the presenters at the workshop titled “Flexible Engineering Toward Green Aircraft” organized in December 2017 at Rome Tor Vergata University to share the great outcomes of the RIBES Clean Sky Project, where RBF morph and simulation were used to drive innovation. (RIBES is an acronym for “Radial basis functions at fluid Interface Boundaries to Envelope flow results for advanced Structural analysis.”) We discussed three examples to demonstrate the aforementioned approaches in aeronautical research.

The first example is summarized in the paper titled “Assessment and development of a ROM for linearized aeroelastic analyses of aerospace vehicles,” which was published in CEAS Aeronautical Journal by Castronovo et al. (2017). This work demonstrated how the transonic dip of the AGARD 445.6 wing can be captured by simulation.

AGARD 445.6 wing: V–g plot of the aeroelastic system (left); structural mode shapes of the FE model (right).

AGARD 445.6 wing. V–g plot of the aeroelastic system computed by a low-fidelity (DLM) and the high-fidelity (CFD) numerical method (M = 0.96, left). First four structural mode shapes of the FE model tuned with experimental modal eigenvectors (right).

The second example is taken from the paper titled “Static Aeroelastic Analysis of an Aircraft Wind-Tunnel Model by Means of Modal RBF Mesh Updating.” Published in the Journal of Aerospace Engineering by Biancolini et al. (2016), this paper compares the steady-state response as computed with traditional two-way multiphysics calculations and with modal superposition for the wind tunnel model of the P1XX wing by Piaggio Aerospace.

Steady-state response computational comparison for the wind tunnel model of the Piaggio P1XXap airplane wing.Piaggio P1XX: RBF-generated points map FEA-computed displacements (green points) onto the CFD mesh, deforming the volume mesh inside the cylinder (red points, left). Lift and drag coefficient as computed with proposed method showing standard two-way FSI vs. experiments (rights).

The third example comes from the paper titled “Fluid structure interaction analysis: vortex shedding induced vibrations” which was published in Structural Integrity Procedia by Di Domenico et al. (2017). This work demonstrates how a full-coupled transient FSI analysis can capture lock-in and lock-off of a hydrofoil.

Full-coupled transient FSI analysis of a hydrofoil.Hydrofoil case: induced vibration frequency as a function of the flow speed and correlation of predicted values at 16 m/s (red point) and 22 m/s (green point).

Full presentations of all three examples are available on the RIBES Clean Sky project page. A book with the full proceedings will be available in late 2018, published by Springer. Keep an eye out for it!

The post RBF Morph makes ANSYS Fluent more Flexible in RIBES Clean Sky Project appeared first on ANSYS.

► Tackling Unprecedented Business Change — Dimensions Magazine
  17 Apr, 2018

In the engineering community we are keenly aware of some truly disruptive forces acting on the industries in which we work. Autonomous vehicles, for example, are continuously in the news. Environmental concerns are pushing efficiency to new limits and driving a shift to more electric systems. Competition is relentless, fueled by new market players from burgeoning economies and traditionally disparate industries. Additive manufacturing is revolutionizing the way products are designed and fabricated, and digitalization, once a buzz word, is becoming real. All businesses need to adapt to disruption.

Dimensions Magazine Spring 2018
As engineers we often must focus on the hard metrics being pursued to deliver functional requirements – cost, efficiency, power, weight, etc. – and achieve these within contradictory constraints. This requires a very difficult balancing act necessitating trade-offs that some seem to make better than others. How can this be done?

business trends - electrification

In our new edition of Dimensions magazine, we share examples of successful companies addressing the business challenges inherent with change. Industry and engineering leaders like Grundfos and TECO-Westinghouse provide insight into how they address electric machine efficiency and innovation, while the University of Nottingham provides a view into the future of environmentally friendly flight with a more electric aircraft. Ever increasing competition dictates an even faster cycle from ideation to product delivery. The director of the High-Performance Computing Center Stuttgart (HLRS) explains how companies can tackle a wider design space of complex engineering problems and solve these problems more quickly via the strategic use of high-performance computing (HPC) resources. ANSYS Vice President Mark Hindsbo reveals that organizations must use simulation every day, on every product, across the entire product lifecycle, to study and improve every aspect of performance and further accelerate new product insertion. And we share how these same techniques can be used to accelerate development even in highly regulated industries such as healthcare.

Enjoy reading the latest issue of Dimensions magazine and let us know what changes you are facing.

Dimensions Magazine Sub-Zero

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► Simulation Spurs Stent Innovation
  13 Apr, 2018

Medicine comprises multiple challenges due to the ongoing development of science and daily discoveries regarding the treatment of diseases. As human life expectancy increases, medical devices, such as a stents and many others, must also continue to evolve to offer more effective and accessible technology for patients of all ages. There are still many countries, however, in which there is no access to appropriate healthcare due to poverty, corruption or appropriation of the resources.

Bioana is a Mexican company dedicated to the design and development of accessible medical technology. Its innovations begin with the recognition of real medical needs and are realized with the use of simulation software to create medical devices used in cardiology, diagnostics and orthopedics.

Finding the Functional Design

For a medical device to be sold commercially, manufacturers must first and foremost demonstrate its functional safety to ensure there is no risk to patients. Before we identify the best performing device, we cycle through multiple design iterations and use ANSYS simulation software for geometry testing and design enhancement.
According to the World Health Organization (WHO), cardiovascular disease is the leading cause of death around the globe. To address this, we design our vascular implants with the aim to increase the accessibility of this hard-to-afford technology.
In cardiovascular disease, fatty deposits build up within the inner walls of the blood vessels. Once the deposits grow, they can produce blood flow blockage that can cause heart attacks or strokes. Vascular stents open the vessels and help restore the blood flow.

Of all the design requirements for these vascular devices, the most important is the devices’ ability to withstand the permanent loading applied due to the pulsatile blood flow.

How Stents Work

A stent is a small tubular mesh — usually metal — that is inserted into the vessel to keep the wall from collapsing. It is implanted through a surgical procedure known as angioplasty into the femoral vein (in most cases) and guided to the site of the blockage using a balloon catheter. The balloon is radially expanded to open the blockage, then deflated, leaving the stent in place. The stent must be able to withstand the loads produced by the pulsatile blood flow and remain fixed to the blood vessel walls.

Design iterations for vascular stents.Design iterations for vascular stents, each with different expansion capabilities.

Typically, doctors use an imaging technique that consists of injecting a radio-opaque contrast agent into the arteries to illuminate the blood flow and the size of the vessel opening. Using a fluoroscope, doctors can also observe the effect on the flow once they stent is implanted.

We also saw it was possible to use ANSYS simulation software to analyze the blood flow behavior resulting from a specific stent design at the implantation Site.
Most frequently Stenosis (or narrowing) occurs most commonly at the bifurcation where the left main coronary artery (LMCA) divides into the left anterior descending artery (LAD) and the left circumflex artery (LCX). We created a 3D model of this bifurcation and the implanted stent. According to the literature, the blood velocity of healthy arteries oscillates at approximately 64 ± 26 cm/s, while the velocity for arteries with fatty deposits is significantly lower (41 ± 26 cm/s). As expected, our results show a higher flow velocity toward the center of the vessel. And although the velocity is reduced on the stented side, it is still higher — an improvement of 15 cm/s — than it would it would be in a fatty artery.

Velocity vectors show reduced velocity in the stented branch of a coronary artery.Velocity vectors show reduced velocity in the stented branch of a coronary artery.

Another important hemodynamic factor requiring analysis is the shear stress on the vessel wall (i.e., the tangential force produced by the blood flow on the endothelial surface). High shear stress values are desirable as they promote flow alignment and secretion of anticoagulants. Low values, conversely, promote apoptosis (cell death).

High wall shear stress values indicate proper stent placement.High wall shear stress values indicate proper stent placement.

According to the literature, regular shear stress values on the LMCA anatomy range from 1 to 2.25 Pa and from 1.41 to 4.65 Pa on the LCX with increased values around the struts and apex of the stent. Our analysis shows a high shear stress value of 4.55 Pa around the stent structure and validates the device’s performance for significantly lowering the possibility of restenosis.

Meeting the Goals

Using ANSYS software, obtained through the ANSYS Startup Program, we have reduced our time and cost for testing protocols and increased our productivity and revenues through expanded development efforts. We can now enter new biomedical fields and continue to innovate — to create high-impact, accessible medical technologies in Mexico.

The post Simulation Spurs Stent Innovation appeared first on ANSYS.

► Moving from Workstations to HPC — A Journey Worth Taking
  12 Apr, 2018

As a computer-aided engineering (CAE) analyst, you often push the limits of your desktop computer, constraining the size of your ANSYS models and limiting the number of the simulations you can perform for any given project.  Moving beyond your desktop to a small high-performance computing (HPC) cluster is the first, very important step. Here’s why …

The chart below compares simulations times running the ANSYS Fluent 19 standard benchmark aircraft wing 14-million cell model on a single compute, two-processor server (1 node), up to a 72-node cluster configuration.

At four nodes, for example, you see a four-times increase in speed. In essence, you could run the same benchmark on a 4-node cluster in 25 percent of the time it takes to run that same benchmark on a single node (or workstation).

Bar graph showing the relationship between node count and speed-up for ANSYS Fluent simulation.

Test with ANSYS Fluent 19 aircraft wing 14-million cell model using a cluster with Intel® Xeon® Gold 6148 Processor 2.40 GHz, 20-core processor with turbo on. Hewlett Packard Enterprise, March 2018.

We realize that time is business critical — as evidenced by the increasing demand to shorten product development cycles. However, with this demand comes the need to maintain quality, which becomes nearly impossible without the right tools or technology. We all know that ANSYS software can enable faster product delivery, but only if your information technology (IT) team can support your engineering needs.  But what if you don’t have access to the IT support for your engineering systems? What can you do to move forward on your journey?

ANSYS, Hewlett Packard Enterprise, and TotalCAE are working together to bring down the barriers preventing the move from workstations to a small cluster either on-premise, off-premise or in the cloud.  Here are a couple of ways you can begin your journey toward greater engineering productivity:

  • Check out the ANSYS Free Performance Benchmark Program. Although the above standard benchmark result may have you convinced, we want to show you the time-savings that HPC can make possible for your model. Once you provide us with your ANSYS CFX, ANSYS Fluent, ANSYS HFSS, ANSYS Maxwell or ANSYS Mechanical model, you will receive a time comparison (versus your current workstation) and consultation for moving to an HPC solution.
  • Join us April 25, 2018, for a special customer experience webinar, with special guests Christopher Basciano, Siva Balasubramanian and Patrick Downie from BD. They will share their insights and experience around moving from workstations to a HPC workflow environment with the help of HPE and TotalCAE.  In addition, you will receive firsthand access to the latest ANSYS 19 benchmark results and the recommended cluster hardware.

Why wait? Bring your model to ANSYS and get a free performance benchmark, then register for the April 25 customer webinar and learn how BD moved from a workstation environment to a fully supported HPC appliance delivered by ANSYS and TotalCAE.

The post Moving from Workstations to HPC — A Journey Worth Taking appeared first on ANSYS.

► Airfoils Don’t Fly, Aircraft Do: Why In-flight Icing Certification Benefits from CFD
  10 Apr, 2018

If you travel by plane during the winter, you are used to the de-icing process: The jet taxis to the de-icing station, where technicians spray the it with chemicals to remove any ice that may have built up while it was sitting on the ground. That’s standard procedure. But what about ice that forms when the plane is flying at high altitude in the cold air? Is that ice dangerous, and can the buildup be predicted to avoid problems in-flight?image of airfoils on an aircraft

Prediction is a complicated process, but it can be done. The only way to numerically predict the in-flight icing behavior (aerodynamics, ice shapes, thermal performance) for the wide range of aerospace assemblies and components (wings, empennages, engine inlets, nacelles, sensors, probes, all the way up to the complete aircraft) is by using CFD models that predict performance “as installed.”

3D CFD simulation shows in-flight ice buildup on aircraft engine blades as installed.

3D CFD simulation accurately characterizes in-flight ice buildup on aircraft engine blades as installed.

Current testing methods can be incomplete. They may deliver less insight than expected or even report misleading results. For example:

  • Airfoils or parts of wings are tested individually in icing tunnels, but the flow may be totally different once engines or propellers are mounted on these wings.
  • Helicopter icing is analyzed in 2D when only 3D will get the right ice shape.
  • Ice protection systems (IPS) systems are designed in 2D, but the flow inside a piccolo tube is “violently” 3D.
  • Pitot tubes are tested in wind tunnels mounted against flat walls, but in operation these pitot tubes will experience a different incoming flow once mounted on a fuselage.
  • Whole airplanes are certified by analyzing a limited number of natural icing test points, but scientific methods are available to determine the behavior of the aircraft for all points.
  • Mathematical methods are well-accepted for optimizing wing performance, but we never apply them to IPS optimization. Designers may produce good and even excellent IPS, but are they optimal? Small performance improvements can have a huge impact over the life of the aircraft.

So, how do you test for ice cracking and tracking?

Industry Needs CFD-Icing Codes that Are Truly Predictive and Not Simply Calibrated

Accuracy in predicting in-flight ice formation must be addressed by developing analytical roughness models that predict ice surface roughness in space (varying all over the 3D body) and in time (roughness is time dependent, increasing asymptotically to a local value as ice accretes). Only in this way can a CFD-Icing code be truly predictive and not simply calibrated.

New 3D CFD-Icing tools permit a more efficient and safer certification method for all types of aircraft by reducing the likelihood of ice-induced hazardous events in service. While dry and icing (wet) wind tunnel testing, flight testing with artificial ice shapes, and flying in natural icing conditions will always play a significant role, advanced simulation tools can shorten the certification process. Simulation can fill important gaps in the data, focus or eliminate the icing tunnel for ice shapes, and predict what will be seen in natural icing testing (calculations and verifications over the entire aircraft with engines, propellers, rotors and turbomachinery stages running, and sensors and probes placed), ultimately increasing safety.


Animation of ice accumulation on the leading edge of an aircraft wing.

CFD simulates ice accumulation on the leading edge of an aircraft wing.

Will further testing be required after using CFD? Of course. But in my experience, final designs created using true 3D CFD will need a lot less tweaking at certification time. Also, safety considerations do not stop when certification is granted but extend to performance during operational life. Incidents and accidents have a high cost in human lives, and can be avoided by a better understanding of the aircraft, engine and appended instruments, as installed.

Graphic illustrating relative times and cost of computational, experimental and flight fluid mechanics.

Learn More at the Simulation Methods for In-Flight Icing Certification Course

Attend the 2018 International In-flight Icing Course. Come see for yourself how CFD-Icing should be pervasive in all stages of analysis, design and certification, from the smallest component to the aircraft itself. Learn from a cross section of experts including code developers, former regulators and an icing lead for a major OEM. You’ll leave with a rigorous, scientifically-based version control, verification and validation dataset that is now widely accepted at all levels.

The course will be held May 21-25, 2018, in Montreal, Canada: Register now. I hope to see you there!

The post Airfoils Don’t Fly, Aircraft Do: Why In-flight Icing Certification Benefits from CFD appeared first on ANSYS.

Convergent Science Blog top

► CONVERGE: 10 Years of Perseverance and Success… and Autonomous Meshing!
  20 Mar, 2018

A lot of things happened in 2008. There was a financial crisis. China hosted the Olympics. Barack Obama won the US presidency. Sarah Palin could even see Russia from her house! But something else was brewing in the Midwestern college town of Madison, Wisconsin. In 2008, Keith Richards, Eric Pomraning, and I realized that we had written a CFD code that could change the simulation world. Not only that, but it was finally ready to be shared with the public. We had spent the last seven years developing this software and now had to figure out how to market it, sell it, support it, and document it, all while running a business. A business that had focused on consulting services since its inception in 1997. How were we going to pull this off? Especially at a time when our biggest client base, the US automotive industry, was facing its own crisis, including bankruptcies and government bailouts. I’ll come back to that in a minute, but first, a bit more history on the CFD code that turns ten this year.

Kelly, Eric, and Keith at a 2003 Badgers football game

Many of you know that CONVERGE started out as MOSES (named by David Schmidt, one of the original founders of our company). But did you know that this stood for Modular Open Source Engine Simulation? That’s right, open source. CONVERGE was originally written with the intent of replacing KIVA, the software that we (and many others) had used in our graduate studies. We accomplished a lot with KIVA, and it taught us a lot about reacting flow CFD. However, there was one big problem with it and, frankly, all CFD codes at the time: the mesh. The mesh quality was often low and the resolution coarse, both of which significantly degraded the accuracy of CFD simulations. Moreover, for anything but a simple, stationary geometry, mesh creation was incredibly painful. In fact, much of the consulting we did in the early years was based around KIVA mesh generation. Keith had written a tool called G-Smooth, which allowed us to make full, complicated engine meshes for KIVA faster than anyone else—in one week. Are you kidding? An entire week of engineering time to make a mesh was considered fast? We knew something needed to change.

In 2001 we sent the US government a proposal called The MOSES Project: A Modular, Open Source Engine Simulation Code for Parallel Computation of Complex Geometries. Their response was something along the lines of “this sounds like a great idea; please come back when industry is interested.” In their defense, this was unsolicited, they weren’t asking for MOSES, and “never make a mesh again” probably sounded pretty far-fetched to the reviewers. This was the first of many times when it would have been easy to throw in the towel.

An early version of the user manual

Instead, we went to industry, specifically to a large engine manufacturer (Engine Maker X). It wasn’t easy to convince them, but they too were frustrated with the time required for making meshes as well as the accuracy implications of using a poor quality mesh. A few key people at Engine Maker X believed in us enough to secure funding to help with early development. So in late 2001 we went to work—Keith and Eric developed the core solver while I kept up the consulting side of things to keep the lights on. A couple of years later, I joined them full-time on code development and started implementing the spray and combustion models. I’ll never forget the month of April 2004. The first version of MOSES was due on May 1, and like any good researchers, we pulled an all-nighter—that lasted the whole month! This month-long all-nighter was required because we’d scrapped our original approach when we realized that an immersed boundary method wasn’t going to work for our purposes. Yet another time when throwing in the towel would have been easy, but I’m so glad we didn’t.

Fast forward to 2008 and a lot of changes were about to drop. Our company, Convergent Thinking, LLC changed its name to Convergent Science, Inc. We also changed the name of our software from MOSES to CONVERGE (fun fact: believe it or not, we discovered there was another CFD code called MOSES!). We were entering the automotive CFD market at one of the worst times in history for that industry. But we quickly learned that the financial crisis was motivating companies to make changes. Companies needed to tighten their belts and find the most efficient and accurate simulation tools possible—enter CONVERGE.

Sales of CONVERGE unofficially started where else but at the SAE World Congress in 2008. Daniel Lee, who was one of the original founders of our company, had left to pursue a career at Fluent (and later ANSYS) after graduating from UW-Madison in 1998. Ten years later, he had honed his CFD sales chops and was ready to come back to Convergent Science. Good timing, as Eric, Keith, and I had no experience selling software. So the four of us met at SAE, ready to take on the big players in the engine industry. No booth, no brochures, just Dan’s little sales notebook and phrases like “imagine a car without gas” (trying to make an analogy with “imagine a CFD code where the user doesn’t have to make a mesh”). These days gasless cars are a little easier to imagine, but our analogy sounded good at the time!

Our website circa 2009

Selling any software is difficult, but selling CFD software can be next to impossible when you’re dealing with companies that have an established workflow and years of experience with a legacy code. We heard a lot of “no” before hearing “yes.” We were told many times early on that we were, hands down, the best CFD code, but there was concern that our company was too small. Eventually, though, we convinced automotive engineers, and later engineers from a host of other industries, that we were (and still are!) a company dedicated to continued innovation and superlative customer service.

We’re also experts in our application fields. There really is something to all of the firsts that we’ve introduced to the community (autonomous meshing, direct detailed chemistry for combustion, grid-convergent Lagrangian spray modeling, cyclic variability with URANS, etc.), many of which were originally quite controversial. I like to tell students that if they’re not working on something at least somewhat disruptive in their research, it’s probably not that novel. Boy were we controversial with CONVERGE—many of the entrenched CFD best practices were based on software and hardware limitations rather than fundamental scientific concepts, and we challenged these practices with new ideas based on first principles. These innovations have no doubt succeeded in helping move the needle from postdiction to prediction over the last ten years, and these innovations have helped propel CONVERGE’s continued growth.

In 2018, ten years after we started selling CONVERGE, we have around 100 team members in our offices across the globe. Approximately 95% of engine makers in the US use CONVERGE, and around 85% of engine makers worldwide use CONVERGE. We’re taking our predictive CFD approach to new markets, and we’re once again changing the game with CONVERGE 3.0, which will be released later this year. CONVERGE has taken on a life of its own, with a large user community that includes engineers in industry, national labs, and universities. We have annual user conferences in both the US and in Europe (don’t miss our US event in Madison in September, celebrating CONVERGE’s ten-year anniversary!). Indeed, the quotation from Robert Fritz that I added to CONVERGE’s main.c subroutine many years ago has, in fact, come true: “At first, I am giving energy to the creation, but later the creation seems to be giving energy to me.” Happy tenth birthday, CONVERGE, may you continue to empower CFD users to never make a mesh again for years to come.

► 2017: A Year of Global Growth
  18 Dec, 2017

In 2017 Convergent Science saw tremendous growth and success in many areas. We now have nearly 100 employees (almost double the 2014 headcount), new clients are using CONVERGE CFD to investigate pumps and compressors, and our customer base using CONVERGE for aftertreatment and gas turbine design continues to grow. We’ve done all this while also increasing our majority share of the global internal combustion (IC) engine simulation market.

Our dedication to accurate and grid-convergent simulations ensures that CONVERGE offers cutting-edge CFD solutions for a wide variety of complex flow problems. Our teams dedicated to IC engines, gas turbines and aftertreatment, and new applications such as compressors and pumps ensure that CONVERGE can meet the unique challenges of each industry.

Continued Authority at SAE, DOE Merit Review, and ASME ICEF

The Society of Automotive Engineers World Congress Experience (SAE WCX17) once again reinforced the prevalence of CONVERGE in innovative IC engine design. In April, more than 35 papers demonstrated results achieved with CONVERGE, including publications from Aramco Research Center, Argonne National Laboratory, GE Global Research Center, Groupe Renault, IFP Energies nouvelles, King Abdullah University of Science and Technology, Oak Ridge National Laboratory, and University of Perugia.

In June, CONVERGE’s utility for innovative, energy-saving research was affirmed at the annual DOE Merit Review. Seventeen programs reviewed by the U.S. Department of Energy referenced partnerships with Convergent Science and work with CONVERGE. Topics ranged from clean combustion in light-duty engines to knock prediction to soot modeling with gas kinetics and surface chemistry.

Seattle played host to this year’s ASME Internal Combustion Engine Fall Technical Conference (ICEF 2017), where 25 papers featured results from CONVERGE. Topics of CONVERGE papers at ICEF included pressure oscillations in numerical simulations of IC engines, gasoline compression ignition calibration, selective catalytic reduction of NOX with detailed surface chemistry, and cycle-to-cycle variation prediction with LES turbulence modeling. Many of our most influential collaborators, including Argonne National Laboratory, GE Global Research Center, and Oak Ridge National Laboratory, presented their topics at this engaging conference in October. Two of Convergent Science’s most experienced support engineers, Shawn Givler and Sameera Wijeyakulasuriya, gave a popular workshop at ICEF that introduced engineers, students, and experimentalists to some of CONVERGE’s powerful tools for modeling heavy-duty engines.

Gas Turbine Relight with GE and Honeywell

Engineers from both GE Aviation and Honeywell Aerospace were interested in CONVERGE’s ability to predict high altitude ignition and relight in gas turbines. This phenomenon has been notoriously difficult to simulate due to the long meshing times associated with complex geometries and the need for accurate, detailed combustion modeling. But Scott Drennan’s Gas Turbine team was up to the task.

GE Aviation engineers gave Scott’s team a diverse range of operation conditions, and Scott’s team proved that CONVERGE can indeed accurately predict relight (or a failure to relight) across the varying conditions. GE Aviation engineers are now testing CONVERGE on fundamental and practical combustor designs for further validation, development of best practices, and optimization.

Honeywell Aerospace successfully used CONVERGE to model relight in their five-sector combustor geometry, and they noted that CONVERGE’s autonomous meshing capabilities help make CFD a viable option to assess all facets of gas turbine design.

CONVERGE-ing in Europe

We hosted our first European user conference in Vienna in early March. This inaugural European CONVERGE experience was a big hit with European clients as well as super-users from the United States who traveled to Vienna to share and learn with their counterparts across the pond. Networking sessions included a tour of a castle and a traditional evening at a Viennese tavern. Attendees from many institutions—including GE, Groupe PSA, IFP Energies nouvelles, Politecnico di Torino, Renault, University of Rome Tor Vergara, and Volvo Cars—helped make this inaugural event a memorable one.

Motor City Hosts Fourth Annual U.S. User Conference

Since so many automotive OEMs are based in Detroit, the Motor City was the perfect location for our fourth annual North American user conference. Two hundred fifteen people registered for this conference, making it the largest CONVERGE conference to date. Users from 42 companies, 19 universities, and six national laboratories experienced two days of informative presentations. Many also participated in some of the introductory and advanced CONVERGE training sessions that were offered. Social events included a Ford Rouge Factory Tour and a dinner social at The Dearborn Inn.

IDAJ’s Continued Success in Asia

IDAJ, our distributor in Asian markets—mainly Japan, China, and Korea—has continued to provide encouraging growth in sales, promotion, and support of CONVERGE users on the other side of the globe. In November, Daniel Lee, Eric Pomraning, and Yunliang Wang gave users an overview of the current status and future developments of CONVERGE at the IDAJ ICSC 2017 Conferences in Yokohama, Japan; Seoul, South Korea; and Shanghai, China. IDAJ continues to host regular CONVERGE training sessions and has expanded the CONVERGE user base in the automotive OEM market, non-engine markets, and academia.

Assorted Consortia

Convergent Science is proud to be a member of the High-Efficiency Dilute Gasoline Engine (HEDGE-IV) Consortium launched by Southwest Research Institute. This consortium gives Convergent Science the opportunity to work with some of the brightest minds in the engine world who are working together to produce cost-effective solutions to engine efficiency-related challenges.

Our Computational Chemistry Consortium (C3) was formed to bring together industry, academic, and government partners to refine existing computational chemistry tools. C3 is now in full swing, with a major global automotive OEM leading the way. With more than twenty years of experience developing comprehensive chemistry models to help predict real fuel behavior, Professor Henry Curran is well-positioned as Chief Technical Advisor of this consortium. Professor Curran and I are guiding C3 in its efforts to develop new models, tools, and mechanisms to lead the advancement of combustion and emissions modeling for the entire scientific community.

Convergent Science: India

A great opportunity presented itself when CEI (formerly our distributor in India) closed its office in Pune: Ashish Joshi, former CEI India manager, master of all things EnSight, and CONVERGE super-user and distributor, agreed to be the leader of our newly formed CS India office. Ashish is now helping to sell CONVERGE and support users in India and southeast Asia. According to Ashish, a distinct possibility for huge growth in the engine design market in India exists because by 2020 engine manufacturers will be held to a much stricter Euro VI-level emissions standard. This is an exciting time to have a solid foothold in the Indian engine design market.

Convergent Science Turns 20

Founded in December 1997 by a group of University of Wisconsin-Madison graduate students, Convergent Science (originally Convergent Thinking) made its debut as an engine CFD consulting group. Twenty years later, CONVERGE is now the global leader in IC engine CFD simulation and is continuing to grow in this market and in others.

2018 and Beyond

In 2018, we are looking forward to continuing to expand CONVERGE’s presence in the European, Asian, and Indian automotive markets. Equally exciting is our ongoing expansion into the compressor and pump industries, which are primed to implement accurate transient CFD. Finally, 2018 is a special year because it marks CONVERGE’s 10th anniversary as a commercially available CFD code (keep your eye out for a blog post commemorating this event in just a few months!). We look forward to bringing continued innovation and superlative customer service to all of our clients. Want to join us? Check out our website to find out how CONVERGE can help you solve the hard problems.

► Convergent Science India LLP
  29 Nov, 2017

Same Face, New Name

Many CONVERGE users in India and Southeast Asia are quite familiar with the magnificently mustachioed Ashish Joshi, who was the head of CEI’s Pune office. Until recently, CEI was a CONVERGE distributor, and Ashish sold CONVERGE and provided technical support to many CONVERGE users. Now Ashish will continue serving in a similar capacity and will take on some additional duties as the leader of Convergent Science’s newest office, Convergent Science India LLP.

In opening the Indian office, we capitalized on some changes to the CEI organizational structure and brought Ashish officially onto the Convergent Science team. Ashish has six years of experience using CONVERGE in a support and distribution capacity, so he is perfectly equipped to succeed in his new role of promoting, selling, and supporting CONVERGE throughout India and southeast Asia.

Ashish is no stranger to new ventures. He helped create the CEI office in Pune back in 2011 after about seven years of working in sales and support for a different CAE software vendor. He is thrilled to use his organizational and planning talents to give Convergent Science a reputable presence in India from its new office in Pune.

Opportunity Abounds in India

We have recently extended Convergent Science’s Indian presence based not only on this country’s strong history of CFD in research, but also on the engine market’s growth opportunity and other expanding research and development efforts in this quickly industrializing nation.

“India’s government has lagged in its adoption of strict emissions standards,” Ashish says. “But by the year 2020, engine manufacturers will be held to a much stricter Euro VI-level standard. This means they’ll need CONVERGE’s unique ability to accurately predict engine performance and emissions in order to remain competitive and comply with regulations.”

India’s highly sought-after technical universities drive demand for CONVERGE in academic research. Consequently, these same researchers demand CONVERGE when they move to commercial R&D groups. IIT Bombay, IIT Delhi, IIT Madras, IISc (Indian Inst of Science), and several NITs are among the many academic research groups already using CONVERGE. Ashish has already begun work expanding CONVERGE’s presence in the most prestigious technical institutions of India.

Ashish outside new Convergent Science India office location.

Multinational corporations have been drastically increasing their presence in India. Indian engineers, scientists, and other professionals have seen an increase not only in the quantity of work in the past 10 years but also in the depth and quality of work for which they are responsible.

Beyond India

Ashish’s territory includes India, Australia, Malaysia, Indonesia, Singapore, and other southeast Asian nations. He’s especially excited to build on his past success by working with professionals in Singapore. “The business culture in Singapore is very progressive and results-focused,” notes Ashish. “There is very little bureaucracy. The researchers I worked with on wind turbine simulations were eager to use the cutting-edge technology that CONVERGE offers.”

Ashish has worked on fluid-structure interaction simulations with Indonesian aerospace researchers and has sold to universities in Malaysia, and Australia. With his efforts now fully dedicated to expanding the use of CONVERGE, Ashish is excited to develop collaborative networks in these nations to bring the benefits of CONVERGE to the swiftly changing nations of southeast Asia.

The Real Motivation

An expansion into India may seem like it was just the logical next step, but our main motivation to open an office in India is to better serve CONVERGE users. Our world-class support and applications team has developed a deep understanding of the needs of CONVERGE users in India. Based on this understanding, our leadership made the strategic decision to ask Ashish to represent Convergent Science in an official capacity from the new office in Pune.

Renovation of the Convergent Science India office space.

Engineers and researchers using CONVERGE are more than just users. They are partners with Convergent Science. We know that when CONVERGE users succeed, Convergent Science succeeds, regardless of the type of simulation, organization affiliation, or country in which the researchers work. Ashish is an expert CONVERGE user, an excellent communicator, and a very important part of the success of CONVERGE as we expand into new geographic areas.

If you are based in India, Australia, or southeast Asia, please contact Ashish directly with any questions about how you can start using CONVERGE to gain insight into your research and design for IC engine combustion, gas turbine combustion, aftertreatment, or fluid flow through any complex system.

Ashish at his desk in the finished Convergent Science India office.


Ashish Joshi

Principal Engineer & Manager

Indian Operations | LinkedIn

► Designing Wind Farms with CONVERGE
  21 Nov, 2017

I once saw a wind turbine blade traveling on an open-bed truck on a back country highway. It was more than a hundred feet long, white, smooth, and curved, and it filled me with awe. How very far we have come since a fictional Don Quixote tilted at windmills[1]: we build these giant blades that sweep an acre (!) with each spin and mount them on towers taller than the Leaning Tower of Pisa.

So there was this blade being trucked along somewhere. How did they decide what it should look like and where to put it? Once you decide you want a wind farm (a network of individual wind turbines connected to the power grid), the first thing to do is to carry out detailed studies of weather, wind, and terrain at candidate sites. Then, the industry standard is to perform simple parameterized empirical simulations for wind turbine wakes using models such as the WAsP software suite[2]. However, with growing computational resources and access to high-performance computing, you can design every aspect of the wind farm using CFD: from individual blade loading (valuable for blade design), to optimizing the location of the individual turbines to prevent interference from the other wakes, to estimating the impact of the environment on the tower and its associated structures. For example, Hannah Johlas, a graduate student at the University of Massachusetts Amherst, whose research is co-funded by Convergent Science, studies offshore support structures using CONVERGE. Other research has focused on the physical impact of the wind farm on the environment[3] (such as land surface temperature[4] and crops[5]) or the weather[6]. Local governments and stakeholders can find results from CFD persuasive.

But there’s a catch: getting detailed and accurate results from CFD in these scenarios can be computationally expensive and time-consuming for the engineers running the simulations. CONVERGE CFD can provide a detailed picture of any aspect of wind farm design and optimization and contains several features, including autonomous meshing, genetic algorithm optimization, and smooth handling of complex moving geometries, to make the cost of these simulations manageable.

Although wind farms have historically been sited on flat terrain, such as offshore or in Iowa, which now leads wind energy production in the United States[7], wind farms are increasingly situated in more complex terrain, such as various sites in California[8]. In the figures below, we show CONVERGE results of wind flow around four wind turbines placed in a complex terrain, containing a hill, a road, and a truck moving on the road.

Figure 1

The most powerful feature used here is CONVERGE’s autonomous meshing capabilities, including Adaptive Mesh Refinement (AMR), which adds cells exactly where additional resolution is required. Here we also use boundary embedding, which moves along with the blade. Previous CFD studies have used an actuator disk simplification for the wind turbine with a bulk source term[9], but this neglects the impact of the blade motion on the downstream wake. CONVERGE can resolve the rotating blade motion and the consequent downstream wake effects (Figure 1). In Figure 2 below, you can see an isosurface of the Q criterion (a visualization of the vortex structure) with additional mesh resolution along the blade. By including blade motion instead of an actuator disk, the turbulent wake is resolved more accurately. This, in turn, lets you observe the flow field around a complex piece of terrain such as the hill (Figure 3).

Figure 2
Figure 3

It can be useful to look at the effects of other factors on the wind farm as well.​ ​In Figure 1, you can see​ a truck on the road in the simulation domain. With AMR and with boundary embedding along the surface of the truck,​ ​CONVERGE can resolve the​ effect of the moving truck on the flow field, as well as the interaction of the wakes of the turbines closest to the road and the truck.

CONVERGE offers many features to optimize and design a wind farm. You can set different roughnesses for different surfaces in the terrain (e.g., water, farmland, and hills). You can allow the towers to rotate and respond to changing winds (specified as a source on the boundary of the domain). You can specify an actuator-line model to simulate wind flow through a wind farm. When designing tower placement and height to compensate for wake effects, you can use genetic algorithm optimization (available within CONVERGE) to spawn a number of candidate configurations, which can save you design time upfront. You can then use CONVERGE CFD simulations to determine the optimal configuration for a wind farm with your particular topography and wind conditions.

Countries all over the world have been investing steadily in infrastructure to increase wind capacity, and the world’s cumulative capacity for wind power has tripled in the last five years[10]. With the growing push toward renewable sources of energy, it is imperative to have tools to ensure effective planning for wind farm design. CONVERGE CFD can accurately (and easily!) simulate many aspects of wind farms so that you can take full advantage of this increasingly popular source of energy.





[4] Zhou, L., Tian, T., Roy, S.B., Thorncroft, C., Bosart, L.F., and Yu, Y., “Impacts of Wind Farms on Land Surface Temperature,” Nature Climate Science, 2, 539-543, 2012. DOI:10.1038/NCLIMATE1505

[5] Rajewski, D.A., Takle, E.S., Lundquist, J.K., Prueger, J.H., Pfeiffer, R.L., Hatfield, J.L., Spoth, K.K., and Doorenbos, R.K., “Changes in Fluxes of Heat, H2O, and CO2 Caused By a Large Wind Farm,” Agricultural and Forest Meteorology, 194, 175-187, 2014. DOI:10.1016/j.agrformet.2014.03.023

[6] Roy, S.B. and Trauteur, J.J., “Impacts of Wind Farms on Surface Air Temperatures,” PNAS, 107(42), 17899-17904, 2010. DOI:10.1073/pnas.1000493107





► Convergent Science: Not Just Your CFD Vendor, Your CFD Partner
  25 Oct, 2017

When you purchase software, there are often many resources to bring you up to speed on its use and application for your industry—manuals, online tutorials, YouTube videos. How often, though, will the software vendor be available to work directly with you to optimize that software for your particular problem? Is it typical for the software vendor to have detailed knowledge not just of their product, but the particular problem you are trying to solve? How often will that vendor offer that detailed, industry-specific knowledge to help you use their software to solve a problem?

At Convergent Science, we not only supply our customers with our innovative CONVERGE CFD software, we work directly with our clients to help them apply CONVERGE in the most effective way to their particular engineering problems. By combining our deep knowledge of our software and of computational fluid dynamics in general with our clients’ understanding of their specific problems, we truly can solve the hard problems.

We at Convergent Science see long-term engineering collaborations with our clients as an indispensable part of our core product. It helps our users generate better results, and it helps our developers and applications engineers improve the modeling capabilities available in CONVERGE. Nearly half of our engineers work on the applications team, which directly supports our users. If you’re thinking about taking the plunge, you’ll be doing so hand-in-hand with subject matter experts in combustion modeling, numerical methods, practical engine development, and a myriad of other relevant topics.

Take, for example, a scenario where you, as a user, are faced with choosing the best physical models for the use in your simulation. It is a CFD truism that you must select appropriate numerics according to the physics, or you will generate physics based on your selection of numerics. That’s not wrong, but it’s not always very helpful. When you set up your case, you might need to choose one of a dozen turbulence models, one of two dozen flux limiter functions, one of more reaction mechanisms than I care to name… the design space is vast. CONVERGE’s example cases are a great starting point, but they may not address the subtleties of your specific case. It’s probably not practical for an organization or user to explore every last combination of case setup parameters, but that’s our bread and butter. With their detailed knowledge of this parameter space, our applications engineers can quickly guide you through case setup and help you select the most appropriate settings and models.

Here’s one example. A leading European automotive manufacturer had transitioned to CONVERGE. They were simulating NOx formation in a diesel engine, using the same physical models as they’d always used. Agreement with experimental measurements was, in a word, poor. Convergent Science applications engineers identified some case setup improvements, using physical models that the client was aware of but had never tested.

These setup improvements weren’t limited to maximizing out-of-the-box performance. They were a joint research effort. Convergent Science engineers applied real engineering knowledge to the problem. While running the client’s case on our local systems, Convergent Science engineers developed a hybrid reduced-order chemical mechanism for the client to improve the NOx formation prediction, without requiring a large and expensive mechanism set. Several setup iterations later, the simulated emissions were within the measurement tolerances of the experiment. The result of this collaboration was that the client had a predictive case setup that they applied to further studies, and Convergent Science had an improved chemistry mechanism that had been validated with experimental data.

The collaboration doesn’t end once the case is running. Because our engineers are highly experienced with and knowledgeable about both the numerical underpinnings of the software and with engine design, they can also help you interpret and understand the results.

CONVERGE simulation of von Karman vortex shedding from a cylinder.

For example, consider von Karman vortex shedding from a cylinder. Depending on tiny variations in the actual running of the case (different partitioning, machine truncation error, etc.), there will be a phase difference between two shedding cases. Which result is correct? Both, of course, are valid solutions of the Navier-Stokes equations. Both are correct. Complex engine simulations can display similar behavior (cycle-to-cycle variability), and our experienced and knowledgeable applications engineers can help you understand what you might be seeing. Sometimes what looks like the result of a setup error is physically correct and operationally important.

We see our applications team’s work as part of a true partnership, not just good customer support. The best way to improve our software and our understanding of challenging problems is to use our software to solve those problems! Every time we see simulated results trace through experimental data points, that’s another validation case. The aforementioned hybridized reduced-order chemical mechanism is a mechanism that we now recommend to clients as an example case. Our developers and applications engineers all benefit from this collaboration.

At Convergent Science, we don’t think of ourselves as your CFD vendor. We’re your CFD partner. Want to learn more? Please check out our Customer Experience page and don’t hesitate to get in touch.

► Aircraft Cabin CFD: Improving Safety and Comfort
  16 Oct, 2017

You hauled your heavy bag through the airport, stood in the molasses-slow airport security line, reached your gate just before boarding ends, and rushed aboard. Now, after you’ve crammed your carry-on into the overhead bin and found your seat, you’re sweating. It’s uncomfortably hot. Quickly, however, the cabin environmental control system activates, and the jet of refreshingly cool air returns you to comfort for the rest of the flight.

Optimizing the cabin environmental control system (ECS) is an important part of aircraft design. In addition to passenger comfort, engineers and transportation officials must understand the behavior of airflow in the cabin in the event of contaminant introduction.

In this example, we consider a CONVERGE simulation that models a passenger moving throughout an aircraft cabin and introducing airflow via exhalation, the operation of the ECS jets, and the fluid flow resulting from the interaction of the two.

Such a simulation presents several computational challenges. First, the aircraft geometry is quite complicated. In the interest of obtaining an accurate flow field, the geometry includes rows of seats, which have many sharp-feature edges (shown below in Figure 1). Also, the passenger moves throughout the cabin during the simulation, which presents a challenge for capturing the effects of this motion.

Figure 1: The aircraft cabin geometry.

In CONVERGE, addressing these CFD challenges is straightforward. CONVERGE’s autonomous meshing capability creates the volume mesh automatically at runtime, eliminating the tedious procedure of generating a mesh for the complex cabin geometry.

Additionally, autonomous meshing creates a new mesh at each time-step to accommodate the passenger’s motion. As such, the mesh remains stationary, minimizing the artificial viscosity that pollutes results obtained from the common approach of using a mesh that moves with the geometry. This feature is optimized in CONVERGE such that it does not slow down the computation.

Without the need to generate a complex mesh, the simulation setup involves importing clean aircraft cabin and passenger geometries into CONVERGE’s graphical pre-processor, CONVERGE Studio, and specifying simulation parameters. We assign the surface representing the passenger a typical walking velocity and a boundary condition modeling exhalation to the passenger’s mouth (a separate boundary). To model the pathogen dispersal from the passenger’s breath, we specify a passive scalar entering the cabin via the mouth boundary (shown in Figure 2). In CONVERGE, passive scalars do not influence the flow field but instead convect and diffuse with the bulk fluid motion to help visualize the flow. We also specify a passive scalar as emanating from the ECS jets.

Figure 2: The passenger geometry. Note that the mouth is a separate boundary.

To ensure accuracy, the simulation must maintain sufficient mesh resolution. Using a high resolution mesh throughout the entire cabin, which is very large, would be computationally unrealistic. CONVERGE’s autonomous meshing capability includes several tools to easily specify higher resolution in areas of interest or importance.

Boundary embedding, specified on the passenger boundary, ensures that additional mesh resolution is supplied around the passenger to help resolve the flow field as the passenger moves. Adaptive Mesh Refinement (AMR) automatically adds resolution in areas with complex flow structures and, in this simulation, is applied to the passenger’s breath and the ECS jet passive scalars.

Figure 3 below shows an animation of the flow field resulting from the interaction between the ECS jets, the passenger’s breath, and the passenger’s motion. Note the added mesh refinement around the breath and the jets.

Figure 3: Animation of the flow field.

Despite the thorny CFD challenges for a case like this, CONVERGE makes the simulation easy by eliminating user meshing time and supplying you with a suite of tools to quickly reach the desired level of accuracy for your application. With CONVERGE, you can solve the hard problems.

Numerical Simulations using FLOW-3D top

► Making the Right Choices for Faster Postprocessing
  24 Apr, 2018

Putting together a workstation boasting 3GB of VRAM, an NVIDIA graphics card from the Quadro family and 64 GB RAM does prepare users for a seamless postprocessing experience in FlowSight. But there is more that can be done to speed things up.

When it comes to postprocessing, a commonly-used feature is stepping through time, which allows users to understand the evolution of their simulation and analyze changes in relevant variables. While extremely important, stepping through time tends to use up computational resources. FlowSight has two modes for stepping through time – Original and Flipbook. Making the right choice between the two can lighten the computational load on your machine.

Original Mode

FlowSight’s default, Original mode generates new data (e.g., isosurface clips) on the fly when cycling through time. If the problem requires frequent cycling back and forth through time, then this mode can be very time consuming, depending on the size of the problem. Although this operation can be slow, it has the advantage of smooth and fast graphic interaction. Additionally, Original mode allows the user to cycle through variable data and is overall much more memory-efficient than using a Flipbook.

Flipbook Mode

Flipbook is the second mode available in FlowSight. When a Flipbook is created, all temporal data for an isosurface or clip is generated and stored in memory ahead of time. The advantage of this approach is that cycling through time is very fast since each frame is already in memory. For the same reason however, Flipbooks can consume large amounts of memory, and generating one takes a lot of time. Additionally, this mode doesn’t allow for cycling through variables, which means that a separate Flipbook must be generated for each variable. And depending on the number of frames and size of the problem, graphic interaction with a Flipbook may not be as smooth as in Original mode.

To cut the time to generate frames and reduce memory consumption, Flipbook allows for incremental loading. Flipbooks can also be generated as an Object or Image. An Object means the Flipbook object on the screen can be fully interacted within the graphics window (zoomed, rotated, panned, etc.) like any other object on the screen.

What to Use and When

The choice between Flipbook and Original mode depends on several factors, such as problem size, memory availability, processor type, and GPUs. We’ve created the table below so that you can quickly determine which mode is best for postprocessing your simulations.



Good for


Memory efficient, fast graphical interaction, quicker changing of variables

Time consuming to step through time

Small RAM, cycling through variables, stepping through few timesteps


Very quick to step through time, flexibility

Takes a long time to generate, memory intensive, reduced graphics interaction performance, does not allow variable cycling

Large RAM, stepping through many timesteps

Wisely choosing the correct mode in addition to the recommended hardware, allows users to fully leverage the advanced features of FlowSight. Please visit the FlowSight page on our website to see more cool features.

► 2018 FLOW-3D European Users Conference Speakers Announced
  17 Apr, 2018

Santa Fe, NM, March 17, 2018 — Flow Science, Inc. has announced the speakers for its 2018 FLOW-3D European Users Conference, which will be held at Le Méridien Stuttgart Hotel in Stuttgart, Germany on May 14 – 16, 2018. The conference will be co-hosted by Flow Science Deutschland.

Customers who use the FLOW-3D product suite as the basis for innovative research and development will present their work, including topics such as additive manufacturing and foaming applications, sediment transport modeling, centrifugal casting processes, cryogenic tank flows, and flow in a peristaltic pump. Speakers from Pöyry Energy GmbH, Roche Diagnostics GmbH, ArianeGroup GmbH, Mott MacDonald, EDF-CIH, Österreichisches Gießerei-Institut, JBA Consulting and Endurance Overseas are part of a diverse lineup of presenters. Additionally, Flow Science’s senior technical staff members will present current and upcoming developments for the FLOW-3D product suite. A full list of speakers and their topics is available at:

Advanced training will be offered on Monday, May 14. Attendees can choose from two tracks: Metal Casting and Water & Environmental. The Metal Casting track, taught by Dr. Matthias Todte of Flow Science Deutschland, will focus on best practices for setting up models and interpreting results, including defect identification. The Water & Environmental track, taught by John Wendelbo, Director of Sales at Flow Science, will explore many facets of a real-world fish passage case study.

Flow Science’s HPC partner, Penguin Computing will participate as a conference sponsor. Penguin Computing is a leading developer of open, Linux-based HPC, enterprise data center and cloud solutions, offering a range of products from Linux servers to integrated, turn-key HPC clusters. Penguin Computing on Demand (POD) offers HPC accelerated time-to-solution without the complexity and expense of owning a cluster. Penguin Computing can be found online at

More information about the conference, including online registration, can be found at:

► Speakers Announced for the 2018 FLOW-3D European Users Conference
  12 Apr, 2018
2018 FLOW-3D European Users Conference

We are thrilled to announce the speakers for the 2018 FLOW-3D European Users Conference; their topics and breadth of experience promise an exciting and informative conference.

Customers who use the FLOW-3D product suite as the basis for innovative research and development will present and discuss their work, including topics such as additive manufacturing and foaming applications, sediment transport modelling, centrifugal casting processes, cryogenic tank flows, and flow in a peristaltic pump. Speakers from Pöyry Energy GmbH, Roche Diagnostics GmbH, ArianeGroup GmbH, Mott MacDonald, EDF-CIH, Österreichisches Gießerei-Institut, JBA Consulting and Endurance Overseas are part of a diverse lineup of presenters.

In addition to the work presented by our customers, our senior technical staff members will present current and upcoming developments for the FLOW-3D product suite.


Solver Developments: FLOW-3D v11.3 and FLOW-3D CAST v5.1
Michael Barkhudarov, Flow Science

Accelerating Simulation Workflows through User Interface Design
John Ditter, Flow Science

Additive Manufacturing and Foaming Applications
Raed Marwan, Flow Science Japan

IMPROVEit, a Modern Optimization Engine for FLOW-3D
Raul Niccolò Pirovano, XC Engineering SrL


Analyzing the Flow of Cement Paste in the FlowCyl
Jon Spangenberg, Technical University of Denmark

Extension of FLOW-3D with a New Phase Change Model to Simulate Cryogenic Tank Flows
Martin Konopka, ArianeGroup GmbH, Airbus

Flow in a Peristaltic Pump: Two-Fluids Approach
Julien Bœuf, Roche Diagnostics GmbH


Faster FLOW-3D Time-to-Solution with HPC Cloud or on Premise
Rod McAllister, Penguin Computing


A Case Study of Flood Modelling from Tailings Storage Facility Failure
Rudolf Faber, Pöyry Energy GmbH

Complexity & Diversity in Sands & Gravels: A Case Study of Decision Options for Sediment Transport Modelling
Kate Bradbrook, JBA Consulting

Hydrodynamic Forces on a High-head Slider Gater
Boris Huber, Vienna University of Technology

Influence of Floating Debris Racks on Flow Rate at Standard Spillways
Guyot Grégory, EDF-CIH

Orlik Dam – Protection of the Dam Against Large Floods
Tomas Studnicka, AQUATIS

Spillway Assessment – When is a Splash a Splash?
Mary Jeddere-Fisher, Mott MacDonald


Simulation-based Development of a Casting Process to Produce Clad Aluminum Strips
Stefan Heugenhauser, Österreichisches Gießerei-Institut

Support of the Design Process for Iron Castings
Malte Leonhard, Flow Science Deutschland GmbH

Improvement of Shrinkage Macro-Porosity Prediction Capability
Daniele Grassivaro, Form S.r.l.

Definition of a Robust Aluminum HPDC Process by Mean of Virtual Simulation, Design of Experiment and Taguchi Methodology
Claudio Mus, Endurance Overseas

Simulation of Centrifugal Casting Processes for Manufacturing of Compound Work Rolls
Martin Liepe, Institute of Manufacturing Technology and Quality Management, Otto-von-Guericke-University Magdeburg

Feasibility Study on the Simulation of Ultrasonic Treatment of Liquid and Solidifying Aluminium A356
Eric Riedel, Institute of Manufacturing Technology and Quality Management, Otto-von-Guericke-University Magdeburg

► Modeling Gas-liquid Flows in Water Treatment Plants
  10 Apr, 2018

Ozone disinfection is a process in water treatment plants for removing bacteria/viruses from the infected water, reducing the concentration of iron, manganese, and sulfur, and reducing taste and odor problems. Ozone disinfection is critical to ensuring that the water coming out of a treatment plant is of high quality.

Ozone is formed in water treatment plants using an ozone generator. The untreated water is passed through a venturi, which pulls the ozone into the water, creating an ozone-water mixture. The disinfection effectiveness of this mixture depends on the mixing, advection, and dissolution of ozone in water. Capturing the behavior of such a gas-liquid mixture requires a clear understanding of the effect of the local hydrodynamics and mixing. A good numerical solution can capture these complex physics and accurately model gas-liquid flows in water treatment facilities. Accordingly, the physical parameters of the ozone-water mixing equipment and the ozone formation characteristics can be optimized.

In this blog, I discuss FLOW-3D’s new gas mass dissolution model, which helps our users better understand the behavior of the ozone-water mixture in water treatment plants.

How the Gas Mass Dissolution Model Works

The gas mass dissolution model keeps track of the relative gas concentrations in a computational cell. If the concentration of gas in the cell is less than the saturation concentration of gas, then there is a mass transfer of gas from the gas bubbles to the water. The equation of gas-liquid mass transfer below summarizes this idea:


where \displaystyle {{k}_{L}} is the local liquid mass transfer coefficient, \displaystyle \alpha  is specific interfacial area, \displaystyle C_{L}^{*}~~ is the saturation concentration of dissolved gas and \displaystyle {{C}_{L}} is the local concentration of gas. With this relatively simple governing equation, the FLOW-3D gas mass dissolution model does an excellent job of tracking gas dissolution into the neighboring fluid.

In a typical simulation that uses this model, the gas is generated and introduced into the fluid, where the dynamics of the gas-fluid mixture are tracked. Using the FLOW-3D particle model, the gas is generated as particles, which then dissolve in the fluid over time, increasing the concentration of gas in the fluid.

FLOW-3D simulation showing gas particle generation and dissolution in the surrounding fluid.

This simulation has a saturation concentration (\displaystyle C_{L}^{*}~~) of 0.0004 and a local liquid mass transfer coefficient (\displaystyle {{k}_{L}}) of 0.07. The particles are generated at a rate of 100 particles/s for 100 seconds. Particles move upwards purely due to buoyancy. At this level of saturation concentration and local liquid mass transfer coefficient, all the gas particles almost entirely dissolve in the fluid and barely make it to the free surface. The animation also shows the particle lifetime, total number of particles and the concentration of gas in the fluid, illustrating the gas dissolution over time.

FLOW-3D simulation showing the mixing and dissolution of gas particles inside a mechanical mixer.

This simulation has a mixture of fluid and gas particles that are released into a container containing the mixer blade. As the blade rotates, the gas particles are dissolved into the fluid. A turbulence model (k-ε) is activated in this simulation to enhance the mixing, in addition to the already accelerated mechanical mixing happening due to the rotating blades.

These examples highlight a technique for simulating the mass transfer of gas into a fluid in simple or complex environments, which is crucial in predicting gas-liquid flow in water treatment facilities. Along with the gas mass dissolution model, FLOW-3D’s chemistry model can be used to capture additional ozone-water mixture physics based on chemical reaction rates. We’ll have more on the chemistry model in the future blogs. Stay tuned, and please contact me at for more details on the applications of the gas mass dissolution model.

► Senior Application Engineer – Metal Casting
  31 Mar, 2018

Our software products are used by engineers, designers, and scientists at top manufacturers and institutions throughout the world to simulate and optimize product designs. Many of the products used in our daily lives, from many of the components in an automobile to the paper towels we use to dry our hands, have been designed or improved using FLOW-3D and FLOW-3D CAST.

The successful candidate in this key position supports and trains primarily our customers using FLOW-3D CAST as well as customers using FLOW-3D for other applications.

The successful candidate will meet these requirements:

  • Master’s degree in metal casting (preferred), mechanical engineering or physics
  • 4+ years of experience in the use of a commercial CFD code, in industry is required.
  • Strong fundamentals in fluid dynamics and heat transfer are required.
  • Experience with Windows and Linux is required. Fortran 90 and/or C, C++ is a plus, but not required.
  • Excellent oral and written communication skills, ability to prioritize work requirements and solve complex problems.

In this role, you will:

  • Be the primary expert for support, training, and documentation for FLOW-3D CAST.
  • Run sample simulations for prospective customers in the metal casting industry.
  • Cross train other CFD Engineers in metal casting applications and develop and deliver advanced specialized training for metal casting customers
  • Provide code customization for customers.
  • Publish papers for and attend trade shows and conferences.
  • Participate in metal casting industry groups.
  • Conduct research in the metal casting industry to develop real-world quality case studies and validations for use in customer support, marketing and sales demonstrations.
  • Suggest changes to the FLOW-3D code in both numerical modeling capability and interface design and test and validate models in development with real-world simulations.
  • Provide consulting services for FLOW-3D CAST

Here’s what Flow Science offers

Flow Science offers an exceptional, market leading benefits package to full-time employees including medical, dental, vision insurances, life and disability insurances, 401(k) and profit sharing plans with generous employer matching, and an incentive compensation plan that offers year-end bonus opportunity.


Resumes may be submitted via mail (Attention: Human Resources, 683 Harkle Road, Santa Fe, NM 87505), fax (505-982-5551) or e-mail (

Visit our Careers page >

► Application Engineer – Metal Casting
  31 Mar, 2018

Our software products are used by engineers, designers, and scientists at top manufacturers and institutions throughout the world to simulate and optimize product designs. Many of the products used in our daily lives, from many of the components in an automobile to the paper towels we use to dry our hands, have been designed or improved using FLOW-3D and FLOW-3D CAST.

The successful candidate in this key position supports and trains primarily our customers using FLOW-3D CAST as well as customers using FLOW-3D in other applications.

The successful candidate will meet these requirements:

  • Master’s degree in metal casting (preferred), mechanical engineering or physics
  • Strong fundamentals in fluid dynamics and heat transfer are required.
  • 2+ years of experience in the use of a commercial CFD code, either in industry or university settings is required.
  • Experience with Windows and Linux is required. Fortran 90 and/or C, C++ is a plus, but not required.
  • Excellent oral and written communication skills, ability to prioritize work requirements and solve complex problems.

In this role, you will:

  • Be the primary expert for support, training, and documentation for FLOW-3D CAST.
  • Run sample simulations for prospective customers in the metal casting industry.
  • Attend trade shows and conferences.
  • Participate in metal casting industry groups.
  • Conduct research in the metal casting industry to develop real-world quality case studies and validations for use in customer support, marketing and sales demonstrations.
  • Suggest changes to the FLOW-3D code in both numerical modeling capability and interface design and test and validate models in development with real-world simulations.
  • Provide consulting services for FLOW-3D CAST.

Here’s what Flow Science offers

Flow Science offers an exceptional, market leading benefits package to full-time employees including medical, dental, vision insurances, life and disability insurances, 401(k) and profit sharing plans with generous employer matching, and an incentive compensation plan that offers year-end bonus opportunity.


Resumes may be submitted via mail (Attention: Human Resources, 683 Harkle Road, Santa Fe, NM 87505), fax (505-982-5551) or e-mail (

Visit our Careers page >

Mentor Blog top

► Event: Thermal Design Considerations for Autonomous Vehicle Electronics
  24 Apr, 2018

In this session thermal design considerations for Autonomous vehicle sensors and sensor fusion systems using Mentor Mechanical Analysis CFD (Computational Fluid Dynamics) based design tools will be explored.

► Blog Post: Article Roundup: Modular Emulation Hardware, LiDAR, Cost-Effective Arm SoC Development, Military Avionics CFD Simulation & Power Aware Static Verification
  20 Apr, 2018

Mentor aims to grow emulation with lower gate-count hardware LiDAR Goes Back To The Future Webinar: Fastest Lowest-Cost Route to Developing ARM based Mixed Signal SoCs Using thermofluid simulation to optimize liquid cooling of avionics power systems Power Aware Intent And Structural Verification Of Low-Power Designs     Mentor aims to grow emulation with lower gate-count hardware Tech Design Forum

► On-demand Web Seminar: Advanced Thermal Management of Electric Vehicle Systems Utilizing Air Conditioning
  19 Apr, 2018

Learn about the new Air Conditioning (AC) capabilities introduced in FloMASTER V9 along with modelling of vehicle thermal management systems

► Technology Overview: Controlling Heat Exchange
  19 Apr, 2018

Heat and its behavior are complex. Rules of thumb are often used to visualize the heat path for design or physical prototypes; but knowing how heat is traveling, at what speed and where it will go, is difficult. Watch this 3 minute video to learn how FloEFD can help you keep heat exchange under control easily. While this video shows FloEFD inside Solid Edge, you can expect the same level of integration with Siemens NX, Creo and CATIA V5. 

► Technology Overview: Filtration and Separation simulation
  19 Apr, 2018

If left uncontrolled, particles can wreak havoc with expensive machinery components and ultimately the wear and tear will result in equipment breakdown. 

A variety of separation and filtration strategies exist for use by design engineers depending on the state of the materials being separated.Centrifugation, cyclones, fluidized beds, various types of filter, settling tanks, and evaporators, are all applications where CFD can play a vital role in design and reducing the number of physical prototypes needed. 

Watch this 7 minute video to learn how FloEFD can be used to help improve filtration and separation. While this video shows FloEFD inside Solid Edge, you can expect the same level of integration with Siemens NX, Creo and CATIA V5.

► Technology Overview: Mixing Simulation
  19 Apr, 2018

Mixing efficiency is affected by temperature, fluid properties, pressure and flow rate; thus making both the choice and design of industrial mixing equipment a difficult task. Watch this 3 minute video to get a better understanding of how FloEFD can help you solve your mixing problems.

Tecplot Blog top

► Post Processing CONVERGE Data Tecplot 360
  13 Apr, 2018
This video is the demo portion of a joint Webinar between Tecplot and Convergent Science. It shows you how to load and manipulate CONVERGE data in Tecplot 360. You can learn more about Tecplot 360 and CONVERGE »


The Webinar demo covers these benefits for using Tecplot 360 with CONVERGE data. Most of this text is a transcription.


Get one-hour of free, online training and full technical support during your trial period. Why wait?

Try Tecplot 360 for Free


CONVERGE post_convert Utility

Before I dive into the Tecplot 360 demo, I want make a statement about the post_convert utility. Convergent Science has put a nice bit of effort into post_convert. An update to post_convert was released in early March (2018), which does a really great job with the Tecplot format. So if you are running CONVERGE and have tried Tecplot 360 in the past or the Tecplot output from post_convert, please do give it a try again. They’ve done a really great job with the tool.

Tecplot 360 Tour

We will jump into Tecplot 360, and for those of you who have not seen Tecplot 360 before, I’ll give you a quick tour. When you first start Tecplot 360, you have what we call the Welcome Screen, with links to all of our documentation, online resources and version number at the bottom. On the left side, we call this the Plot Sidebar, which we will have more information about once I load a data set. We also have Pages. Think of Pages like a worksheet in Excel or a tab in Chrome. You can have multiple Pages in Tecplot 360.

We have here what we call our Quick Macro Panel, so if you have any custom scripts that you want to run, you can access them very easily. Then down here, Probing. If you need to find information about your data set, it is available using the Probe Tool.

Loading Cell-Averaged Output Files

We will start with loading a cell-averaged output file, emissions.out. To open this file, we are going to use our General Text Loader. We will select it to instruct Tecplot to use that specific loader. Now, we are presented with the user interface. This is a very general loader for any type of text file. What is really important here is to understand what that text file looks like in order to populate it with the proper instructions. Watch our video tutorial on the General Text Loader.

I’ll bring up Notepad++ with emissions.out. The important points on this file are the variables.

  • Variables are listed on line 3.
  • The data is listed on line 6 and goes through the end of the file.
  • On the variable line, we have this hash mark that we need ignore.

Those are the three key points for loading this data. For those of you who have not run CONVERGE before, these .out files are produced as part of the run. Each one of these would represent an iteration of the solver.

  • We will go into variables and we will say that variables start and end on line 3.
  • In the data tab, we have this set up to start on line 6 and go through the end of the file.
  • In general data filters, we are going to say ignore columns in position 1. That is how we ignore the hash mark.

If I want specific variables to load, I can go back into that variables dialog and say select variables to load. Here, let’s just load crank, NOx, and carbon monoxide.

Line Map

Now we have our plot. You can see that we only have NOx on the y-axis. To see the carbon monoxide, you go into what we call the Mapping Style dialog. We can see that we have what Tecplot calls a Line Map. We will just activate this. It is off the screen, so I’ll do a CTRL-F to fit. This is also available under View > Fit to Full Size. Now, we can see my entire data set.

What I really want to do here is to compare this against another run. Let’s go ahead and append another data set. We will go to Run 2. Again, we will look for emissions.out. We will open this and we will say Append Data to Active Frame. All of these settings have been retained, except for this one. We will go back and we will say I just want crank, NOx, and carbon monoxide.

Line Plots

Now the data has been loaded, but it does not show up. Let us find out where it went. We go into Data > Data Set Info. This is a key dialog in Tecplot 360. It tells you all about the data that you have loaded. We see that we have Zone 1 and Zone 1. This is from our first run, so we will just double click on this and say Run 1. We will double click on my second zone and say Run 2. That way we can identify these and we can see the differences. Run 1 we did 8,200 iterations, Run 2 a little over 12,000.

To get these to display, we will go into the Mapping Style dialog. We will select these two maps. We can copy them. But, we now want these to point to Run 2. We will display those. Now, we can see both Run 1 and Run 2 here, but we can’t quite tell them apart. Let’s add a legend (Plot > Line Legend, check Show Line Legend). Then, we will rename these to include the zone name to help identify them. Then, finally, we will give Run 1 symbols that are a Delta and a Gradient. We can turn on the symbols. Because there were so many iterations, the symbols are quite dense. We can set up the symbol spacing. I like my symbols a little smaller and filled with the line color. Now, we have a really nice plot showing the differences between Run 1 and Run 2.

What I’m really trying to get across here, in this plot, is that Tecplot gives you a lot of control over line plots. It helps you create nice production quality plots that you can be proud of when you put them into a paper or communicate with your colleagues. If you do need to share this outside of Tecplot, we do have some very nice export tools. In particular, for line plots, using the vector-based outputs such PostScript or WMF make really, really nice plots when you put those into papers or into PowerPoint.

That covers line plots. We will just rename this page here to Emission. Let’s add a new page and load some additional data. Here we are going to move onto working with parcel information.

We will go back into Run 1 and the output folder. We are going to select our Tecplot Data Loader. I have already run post_convert to convert the CONVERGE output to Tecplot format. We can just select all of these and hit open. You see that the files opened fairly quickly. What does this represent here? If we go to the Data > Data Set Info, we have a total of 93 million elements spread over about 145 time steps. Quite a lot of data loaded very quickly into Tecplot.

Solution Time Is Crank Angle

You’ll notice that one of the nice things that Convergent Science has done with post_convert utility is solution time is actually your crank angle. One of the first things that users have asked me is, “How do I get that crank angle on the plot?” A simple way to do that is using our Text Insert Tool (Text Insert Tool). We have what we call dynamic text. We will just type CA:&(SOLUTIONTIME). As we step through, you can see this updates. Now, the formatting is a little bit longer than I want, so we can do some formatting here to shorten the string. Quite a lot of flexibility in how you can add text and format it.

Macros and Customizing Workflows

I talked about macros and customizing. If you don’t want to have to remember everything, you can register a macro on our Quick Macro Panel here. I can just double click. You can see that added a piece of text. If you’re doing some of these actions all the time, using this Quick Macro Panel can be really nice way to customize your workflows. Watch the video tutorial on Quick Macros.


On to parcels. In order to display parcels, we need to turn on our scatter layer. By default, scatters are turned on for all of our zones. To show scatters for the spray, go into the Zone Style dialog where all of the style settings about your data are set. Select all of our zones except for spray, then turn off scatter. It is left on just for spray. Let’s change this to scatter Points. Points render quite quickly and allow you to see all of your spray quite nicely. We will turn on scatter and we will advance time to where the spray is coming in. We can turn on Translucency and you can see the interior.

Understanding Attributes of Spray

This is quickly how you display scatter, but let’s say you want to understand some of the attributes of your spray. Let’s look at the DP_film_flag. This describes some attributes about your spray: whether it is in the wall film, whether it is in the volume, whether it is a rebounded parcel, etc. We can do that by turning on contour coloring. We will go here into Contour Details dialog and we will set up our contour coloring to use this DP_film_flag. See the video tutorial on Displaying Spray.

Color Maps

We know that CONVERGE uses the values from 0 to 5 to represent the film flag. We will say that we want levels from 0 to 5. Then, we will go back into the Zone Style dialog and we will tell this to use my contour group. Now, we can see the different coloring. If we want some more distinct coloring, we can choose a different color map. Qualitative Dark 2 works pretty nicely. You can see zero represents the parcels that are not in the wall film, blue now represents ones that are in the wall film. You can see how those change as you animate through your data set.

This is a really nice way to look at parcels and understand them.

Isolating Parcels with Value Blanking

Another thing you may want to do is isolate a certain set of parcels. Here we have quite a number of them. If I want to look at just the ones that are in the wall film, I can use what we call Value Blanking (Plot > Blanking > Value Blanking…). We will go into the Value Blanking dialog and we will select our DP_film_flag variable. We will say that we want value blanking to be included when it is not equal to zero. This is going to isolate just a single set of my parcels.

I can automate this in the Quick Macro Panel. We have a macro command called Prompt for Text String. Here I want to look at just the parcels that are in the wall film. The Quick Macro has adjusted that value blanking capability for me. We will rename this page to Spray.


We will add yet another page. Here we will just flip to 3D (In the Plot Sidebar, select 3D Cartesian). Doing this has shared the data set with the previous page. We have not actually loaded another data set, and are using shared memory in this case. It keeps Tecplot’s memory footprint quite low.

Now, we will look at using slices. To add a slice, it is quite easy. We have a Slice Tool (Slice Tool) here. You can just click and place it. You can see that it slices inside the volume. Again, on our Quick Macro Panel, double click Clip Above Primary Slice to see what the slice looks like. All of the macros on the Quick Macro Panel are custom for this webinar. We will be sure to share out these items after the webinar. What I want to look at now is temperature. A nice way to do this is to simply double click on the Legend and then, we can just change it to temperature. A color map that I enjoy using is one called Viridis. This is not built into Tecplot yet, but you can incorporate your own color maps through the Import Color Maps, which is what I’ve done here.

I want to know what contour range to use here. Again, we will plot our maximum contour value over time. Our current contour value is temperature. I can simply double click on the quick macro, Plot Max Contour Over Time. You can see that we are advancing through time. The macro has just created a line plot showing the maximum temperature for my data set through time. You can see that prior to the combustion phase, temperature is fairly low. Let’s choose a contour range here of about … let’s use the Probe Tool(Probe Tool) to find the range of temperatures. The temperature here is about 1200 and here it is about 2800. We will adjust our contour levels to about that range. We will say from 1000 to 3000.

Plot Layout Flexibility

Tecplot gives you a lot of flexibility in how you want the layout of your frame. What I have here is a multi-frame layout, multi-page layout. I like this view of my data.

One of the important things for people using CONVERGE is that it uses an adaptive mesh refinement. The first time you will see your mesh is when you bring in your data. If you want to put a mesh on your slice, you can see what the mesh that CONVERGE has created looks like. If we get a little bit closer to crank angle zero, where combustion is occurring, you can see how CONVERGE has refined that mesh and what a nice job it is done.

Multi-Frame and Multi-Page Layouts

Let’s create another view. I’ll just do a CTRL-C, CTRL-V, so now I have copied my frame. We will drag it over here. Now I have two frames of the same data, but let’s create a different view. Here I want to disable my clipping plane and I want to put my slice in a different position. I want to get it aligned right here with the valve. We will choose the Slice Tool again. I’m going to hit Y on the keyboard to make it Y-aligned. You can see how we got it aligned with the valves. Then, we will just put it in a planar position, turn off shade, and now I can see my slice in this position here. We will go ahead and animate. Now these frames are all linked through time.

This is a really nice way to create a multi-frame layout that communicates a lot about your data very quickly. I can really create a nice story to share with colleagues or communicate to my manager.

Animation and Movie File Export

If I do need to save this out as a movie file, I can do that quite easily as well. Right here in solution time controls, I can go into details and just click on this film icon and select a couple of different movie formats. MPEG-4, AVI are probably your best bets for the movie formats. Antialiasing with a super sample factor of three makes for really nice, crisp lines and really nice, crisp text. You can also enter the width.

Let me just show you an animation that I produced with an earlier layout. As you can see here, nice looking animation, nice, crisp text, and a great way to communicate your data, especially when you’re dealing with this transient data that are produced by these internal combustion cases.


I have not hit isosurfaces or streamlines yet. I’ll just briefly talk about them. Isosurfaces are set up, again, through what we call the derived objects. A derived object is something that is created from your volume data. We can simply go into Isosurfaces > Details. One thing that often times can be difficult is figuring out what is a good value for my isosurface. That is where the Probe Tool comes in nicely. You can see that we have a color band change here. We can click right on that and see our temperature. It is 1265, give or take. We can use that value for my isosurfaces.

Let me disable clipping. We will turn on Translucency and let’s make that even more translucent. We will go into Zone Style > Effects tab and say 90 percent translucent. Now, you can see your isosurface in that volume more clearly. Using the Probe Tool is a great way to figure out a good candidate value for isosurfaces.


We have a couple of different types streamtraces (Streamtraces Details dialog).  Volume Line is a line that is going to go through your volume. Volume Rods and Volume Ribbons are also very nice ways to display your streamlines. Let’s grab a ribbon and we will place a streamline right here. It is very easy to place streamlines in your data set just with point and click. If you have very specific locations, you can also define those by entering X, Y, Z positions, etc. Watch the video tutorial on Streamtraces on a Slice.


I’ve shown a couple of these capabilities that we put on the Quick Macro Panel. With the Python toolkits, PyQt, you can actually create custom user interfaces. Here, it took me about a few hours to write this custom user interface using the Python language. This has a lot of these capabilities that you have right here on the Quick Macro Panel as well. Again, if I want to show the crank angle or show RPM, just single click. If I want to jump to a specific crank angle, I can do that right here. Really, I’m centralizing a lot of the capabilities that you as a CONVERGE user might need in one nice easy to use dialog using the Python language.


Learn more about working with CONVERGE results

► Tecplot 360 Basics: Data Alter
  29 Mar, 2018

Tecplot 360 Basics: Data Alter (Specify Equations).

This quick tutorial about Data Alter covers the Specify Equations Syntax in Tecplot 360. We will be calculating a commonly desired variable which is often not included in exported data sets: velocity magnitude.

Video Script

In Tecplot equation syntax, creating a new variable or referencing a variable by name requires surrounding it in curly braces. However, variables on the right side of equations may be referenced by two other methods:

First by the variable index. Or secondly, if they are defined as Tecplot’s assigned variables axis or velocity components, in this case u and v. This indicates Tecplot should use whatever variable is assigned to the u and v vector component variables.

Additionally, common mathematical functionality such as SIN, COS, SQRT and other common mathematical operands are available in the equation syntax. Notably, squared or second power is indicated by double star.

For our velocity magnitude calculation, all three of these equations will achieve the same result.

{Vmag} = sqrt({X Velocity}**2 + {Y Velocity}**2)
{Vmag} = sqrt(V4**2 + V7**2)
{Vmag} = sqrt(u**2 + v**2)

If you would like to learn additional capabilities and examples of the Specify Equations dialog, check out previous tutorial videos:

► General Text Loader
  29 Mar, 2018

Tecplot General Text Loader.

This video will show you how to use the General Text Loader to load CONVERGE cell-averaged output files (*.out files).

Video Script

To do this we will use the General Text Loader, which is one of several text data loaders in Tecplot 360. The General Text Loader, by default, only identifies files with a .txt extension.

The first thing we want to do when we load our data (File > Load Data) is to select All Supported Files or All Files.

Next we select the emissions.out file, and instruct Tecplot to use the General Text Loader. This will be a slightly different workflow on Linux but it is very similar. Now we will go ahead and click Open.

Open the Output File

Now, we will need to instruct the General Text Loader on some of the attributes of our data file. Open the output file (emissions.out) in any general text editor.

We can see that line 3 starts with a hash mark. The hash mark is interpreted as a comment, and we will show you how to process this.

Line 3 lists our variable names.

Line 6 is where our data starts and the data goes all the way to the end of the file. And those are the three bits of information that we need to know to load this dataset.

  1. First we will tell the General Text Loader where to look for the variable names, which again are on line 3.
  2. We will tell the loader where to find the data, which starts on line 6 and goes through to the end of the file.
  3. And then under General Filters we will tell it to ignore information in column number 1, which is where we saw the hash mark on line 3.

Scan for Errors

To scan for errors, select View Processed Data and Scan File. This is a check to confirm that the loader picked up all of the information about our data and variables, and there were no errors reported.

Click OK and now our data is loaded into Tecplot 360.

Our variables of interest are NOx and carbon monoxide. To view them, open the Mapping Style dialog.

Line Maps

Tecplot 360 automatically creates Line Maps only for the first variable in our file, and not for any other variables in the file. But we want to look at NOx and carbon monoxide, so we will deselect the first variable and select NOx and carbon monoxide. Then we can close the Mapping Style dialog.

To fit the data, click Ctrl+F. Now we can see the two line maps. We also see that they have very different scales.

We want to choose a separate scale for carbon monoxide. To do this, open the Mapping Style dialog, select the carbon monoxide row, right click on the Which Y-Axis column, and select Y2. This puts carbon monoxide on our secondary axis. Now click Ctrl+F to fit carbon monoxide on the new axis. We now see that the data are scaled similarly but with different values. We can also adjust the label layout by choosing the Adjust Tool and moving the labels where we want them.

To associate the line plots with their respective axes, we can color the axis to match the color of the line plot. If we open the Mapping Style dialog and click the Lines tab, we see that the pink line is carbon monoxide.

To color the Y2 axis, right-click on it to open the Axis Details dialog. Select Y2 and the Line tab, and choose the Color to match the line plot. Now we can see which line plot is associated with which axis.

Additionally, we can toggle on the Line Legend to see the mapping for each plotted line.

Learn more about working with CONVERGE results

► Unsteady or Time-Varying Data in Tecplot Files
  26 Mar, 2018

“Big whirls have little whirls
That feed on their velocity;
And little whirls have lesser whirls,
And so on to viscosity.” –Lewis Fry Richardson

Unsteady Fluid Flow

Anyone who has watched the flow of water down a river, or the vibration of the control surfaces on the wing of an airplane, knows that fluid flow is generally unsteady (changing over time). In fact, most data, in real life, varies with time. For computational efficiency we often assume that field data is steady (unchanging in time) but that is generally a simplification of the real physics. In computational fluid dynamics (CFD), as with elsewhere in computational physics, the trend is toward modeling of unsteady flows.

What is the best way to represent unsteady data in Tecplot files? As mentioned in my previous post on Data Sharing, Tecplot divides datasets into zones. These zones may represent different areas of the physical domain, the same physical domain at different time steps, or different results that you may wish to compare. The zones may also have different dimensionality; for example, there may be a mixture of 3D volume zones that contain field variables and 2D surface zones that contain the problem geometry and surface properties.

This means that unsteady data will have at least one zone for each time step, and may in fact have many zones per time step. Since there are frequently hundreds of time steps, the dataset may contain thousands, or tens of thousands, of zones. To make the visual analysis of this data easy and efficient, it is important to correctly specify a couple of parameters for each of the zones: Solution Time and Strand ID.

Solution Time

Solution time is a floating-point number that can either be the actual time (in seconds, for example) or some number that increases with time – like iteration number. When Solution Time is set for each zone, Tecplot’s animation capabilities are activated. Using Solution Time also improves Tecplot performance because, initially, Tecplot needs to load only the first time step.

If you are a code developer, you should take advantage of Solution Time when writing out multiple time steps. When writing binary data, this is done with the SolutionTime parameters of the TecIO TECZNE function. For ASCII data (not recommended except for very small datasets) Solution Time is set by specifying the SOLUTIONTIME in the zone header.

Get the latest improvements to the TecIO Library
Learn more about TecIO »

Strand ID

Strand ID is an integer number used to group zones together for various purposes. When dealing with time-dependent data, Strand ID is used to identify related zones through time. For example, if a sequence of zones represents the variation through time of data on the same geometric region of the wing, they should be assigned the same Strand ID. Likewise a separate Strand ID number would be specified for zones representing the fuselage, or volume zones.

In practice, Strand ID is a way to condense the amount of zones you work with while setting style for your plot. Style is set using tables in the Zone Style dialog. If you have thousands of zones it can be cumbersome to set the style for all zones. If Strand ID is set, the style is set simultaneously for all zones with a given Strand ID. In the Zone Style dialog, you only see the Strands. If you have 100 time steps, there are 100-times fewer strands than zones so they are far easier to work with.

Like Solution Time, Strand ID is a number (an integer in this case) which is set when the zone is created. When writing binary data, this is done with the StrandID parameters of the TecIO TECZNE function. For ASCII data (not recommended except for very small datasets) StrandID is set by specifying the STRANDID in the zone header.


Vortex Shedding Behind a Swept Cylinder

One time step of vortex shedding behind a swept cylinder.

The image above is of one time step of vortex shedding behind a swept cylinder. The dataset contains 100 time steps with 14 zones per time step. Solution Time is defined for all zones and you can see that the animation controls are active. Strand ID’s are also defined for each zone, with strand 1 being zones 1, 15, 29, …, strand 2 being zones 2, 16, 30, …, and so on. The image below shows the Zone Style dialog which contains only 14 rows, one for each of the 14 strands. The * next to the zone number indicates that it is actually a strand and the zone number at the current time, as specified by the animation controls, that is shown. If we had not defined Solution Time of Strand ID’s, Zone Style dialog would have had 1400 rows.

Zone Style Dialog

Zone Style dialog which contains only 14 rows, one for each of the 14 strands.

In Summary

  • Do set the solution time for zones in time-dependent data. It enables special features in Tecplot, like animations, and it improves the performance.
  • Do set the Strand ID for zones in time-dependent data. It simplifies the setting of plot style and improves the user experience.

Happy computing!

Scott Imlay
Scott Imlay
Chief Technical Officer
Tecplot, Inc.
► Data Sharing in Tecplot Files
  21 Mar, 2018

Data Sharing in Tecplot Files – What to Share and What Not to Share…

“Sharing is good, and with digital technology, sharing is easy.” –Richard Stallman

Data Sharing in Tecplot Files

Trapezoidal Wing Solution

Tecplot divides datasets into zones. These zones may represent different areas of the physical domain, the same physical domain at different time steps, or different results that you may wish to compare. The zones may also have different dimensionality; for example there may be a mixture of 3D volume zones that contain field variables and 2D surface zones that contain the problem geometry and surface properties.

In many cases, different zones contain the same data. For example, multiple time steps of a CFD solution often have the same X, Y, Z coordinates of nodes. In this case, repeating the specification of X, Y, and Z for each time step is wasteful.

Luckily, Tecplot allows for variable sharing where the values of X, Y, and Z are only specified for one zone and the other zones share those values.

Likewise, Tecplot allows for connectivity sharing, where the node-map (list of node-numbers that define the finite-element cells) can also be specified once and shared with other zones. By using the variable and connectivity sharing correctly, you can dramatically reduce data file size and Tecplot memory usage.

And, because Tecplot only has to read this data once, the performance improved. WIN WIN!

Take Advantage of Variable Sharing

If you are a code developer, you should take advantage of variable sharing when writing out multiple time steps. When writing binary data, this is done with the ShareVarFromZone and ShareConnectivityFromZone parameters of the TecIO TECZNE function.

For ASCII data (not recommended except for very small datasets) specify the VARSHARELIST and/or CONNECIVITYSHAREZONE parameters in the zone header.

Another way to take advantage of variable sharing is to write separate grid/solution files. This is done by specifying in TECINI a FileType of 1 for the grid file and a FileType of 2 for the solution file. When you read in the grid file and multiple solution files it will automatically share the grid variables (X, Y, Z and node-map).

Get the latest improvements to the TecIO Library
Learn more about TecIO »

Data Sharing Can Be Very Beneficial

Data CreateZone Mirror

Display the whole plane with Date->CreateZone->Mirror

There are other scenarios where data sharing is very beneficial. For example, CFD solutions and other physical simulations often take advantage of known solution symmetries. For example, the left side of an airplane is generally the mirror image of the right side. When the airplane is flying straight, you really only need to solve the flow equation for half of the plane. This was done for the Trapezoidal Wing Solution image shown above.

When presenting this solution, you may want to display the whole plane. In Tecplot, this can be done with Date->CreateZone->Mirror, and specify mirror about the XZ plane. This command will automatically take advantage of variable sharing for those variables that are identical. In this case, that is every variable but Y and the y-component of velocity. The result looks like the image at right.

Be Careful When Using Data Sharing

So far, I’ve made data sharing look like a wonderful thing and, if you are curious person, you may have already thought of many other ways you can use data sharing.

BE CAREFUL! Variable sharing should only be used when both zones use all of the nodes.

DO NOT Share Data Between Volume and Surface Zones

A temping place to use variable sharing is with volume zones and bounding surface zones. For example, in the trap wing solution above, the nodes on the surface are also nodes in the volume zone, and the coordinates and field data at those nodes are the same.

So, why not share all the variables? And for the surface, create a new FEQuad zone that has a different node-map that references a small fraction of the volume nodes?

Although this will save some memory in the file, it is very bad for Tecplot performance.


Many algorithms within Tecplot loop over all the nodes. In the case of surfaces sharing volume nodes it will loop over all of the volume nodes for each of the surfaces. For the half trap wing solution there are only 2.6 million nodes on the surface but 76 million nodes in the volume. If you shared the volume data with the surface, the algorithms that loop over the nodes would be doing at least 30 times more work than required. Since the surface data is actually broken up into four zones, it will actually be doing 120 times more work than required. DON’T DO IT!

DO NOT Share Variables with Smaller Volume Zones

It is sometimes nice to partition the volume data into logical regions. If you do this, you might think it would be convenient to write all of the volume data into one zone and share it with the other partitions – each zone using a fraction of the total nodes. This is bad for the same reason sharing volume nodes with surface zones is bad – algorithms that loop over all nodes waste a lot of time.  

In Summary

  • Do share data between volume zones that all use the full set of nodes.
  • DO NOT share data between zones where one of them uses less than the full set of nodes.

Happy Computing!

Scott Imlay
Scott Imlay
Chief Technical Officer
Tecplot, Inc.
► Display Spray Particles
  15 Mar, 2018

Displaying Spray Particles.

In this video we will show you how to display spray particles in CONVERGE data sets.

Video Script

To display spray particles, you must use the Scatter Zone Layer. By default, Scatter is enabled for all zones, so the first thing we want to do is turn Scatter off for all of the zones except for the Particle zones.

We also want to change the Symbol shape to Points as they are the fastest to render. Now we will turn on the Scatter Zone Layer on the side bar.

The spray particles show up part way through the simulation, so we’ll drag the time slider to a point after the spray is injected. Here you can see the Scatter symbols peeking out of the surface slightly. Turning on Translucency allows us to see the spray particles in the interior.

Now, we’ll setup the Scatter symbol coloring, based on the dp_film_flag variable.

To setup the coloring, go into the Zone Style dialog and right-click on color for the particle zone. We will select contour group one, which this is currently assigned to mass.

Film FlagThen, launch the Contour Details dialog and change the variable assignment to dp_film_flag. The film flag is an integer with values from zero to five, which represent the different parcel properties. Shown here are the meanings for each film_flag integer value. So, we’ll set the contour levels from zero to five with six total levels.

We can create more distinct colors by selecting a better color map, such as Qualitative – Dark 2. Finally to align the labels with the colors we go into the Number Format dialog on the Legend tab and use the notation for Prefix and Suffix.

Learn more about post-processing CONVERGE results in Tecplot 360 »

Schnitger Corporation, CAE Market top

► DS: Industrie 4.0 is old news; makers and innovators will rule tomorrow
  25 Apr, 2018

Dassault Systèmes just announced Q1 results, and they are mostly positive. As with each release, there are various accounting treatments, as-reported and constant currency, grouped-together buckets of revenue … I suggest that you dig through the details here, while I summarize some of what I found most interesting on the US analyst call and the Europe investor webcast.

For me, the most compelling tidbit was CEO Bernard Charlès’ declaration that DS is so over the whole Industrie 4.0 thing. He said that it is “yesterday’s way of thinking, that tomorrow is about ecosystems of makers inventing new things with the end-customer in mind: new services, new content, that industries will offer their clients.” That’s fascinating, because many of the industrial companies I deal with are still very much in the midst of Industrie 4.0. They’re trying to figure out what that means to them, and what/how to digitalize more of that they do and if they even should. To be sure, they’re looking for value internally and, ultimately, externally and that may include new customer offerings. M. Charlès didn’t coin a new term for what the next thing is going to be called, but I bet they’re working on it.

A brief overview of the financials:

  • Total revenue for Q1 was €769 million, up 1% as reported using IAS accounting standards and up 9% on a constant currency (cc) basis/using other accounting treatments (see below for more on accounting)
  • Software revenue was €686 million, up 2% (or up 10% cc etc.)
  • Services revenue fell short, with revenue of €84 million, down 7% (or flat, cc) on soft demand for 3DEXCITE and other brand implementations. DS made sure to point out that 3DEXPERIENCE service engagements are proceeding as expected
  • Within software, and now using cc and non-IFRS accounting,
  • CATIA software revenue was €237 million, up 5%. New license revenue was up in double digits, let by Asia and the Americas and the global indirect channels. DS said that half of large deals are now on the 3DEXPERIENCE platform.
  • ENOVIA software revenue was €73 million, up 11% on momentum in 3DEXPERIENCE adoption that drove new license revenue up 36%
  • SOLIDWORKS software revenue was €173 million, up 13%. Unit volume was up 8%, so these must have been more expensive units — but it’s also likely that (maintenance) subscriptions contributed, too.
  • Other software revenue €205 million, up 14%. For the first time, DS gave some color on its SIMULIA brand, saying that it now accounts for 15% of software revenue for the quarter and the year, and that Q1 organic revenue was up 9% cc (in other words, excluding Exa.) Including Exa, SIMULIA revenue was up 26% over Q1 a year ago. Why do this now? DS CFO Pascal Daloz explained that he believes DS is #2 globally in CAE software revenue, and that it wants to be recognized as such — possible only if there is greater disclosure. (Hint, hint, Siemens.) A bit of math indicates that SIMULIA had revenue of around €100 million in Q1, so maybe €400 million ($500 million) for the year.
  • Again on a non-IFRS and constant currency basis, revenue by geo broke out as
  • Revenue from the Americas: €225 million, up 10%, coming from both North and Latin America, and all major brands
  • From Europe, €327 million, up 5% on a tough comparable a year ago, and with strong performance from France, Southern Europe and Russia
  • Asia led the goes, with total revenue of €219 million, up 16%, on growth in Japan, China, India and South Korea
  • All three sales channels did well, as did 9 of the 12 verticals. DS singled out transportation and mobility, aerospace and defense and industrial equipment among its traditional industries, and AEC, retail and natural resources in its “new” bucket as reporting double-digit cc software revenue growth.

Yup. Lots of numbers, mostly good. Also interesting was the color on adoption of V6/3DEXPERIENCE. M. Charlès told investors that it was now complete in terms of scope, and that it’s an operating system for DS solutions and, via POWER’BY, to legacy systems. You may recall that it’s been a slow roll, as switching to ENOVIA may not work for all possible adopters. DS said that 3DEXPERIENCE sales now make up 34% of license revenue overall (up 53% year/year cc), over half of CATIA license revenue and over 75% of ENOVIA license revenue in Q1. That’s all well and good, but it means that significant numbers of buyers are opting for not-V6/3DEXPERIENCE, even years after launch.

DS has completely changed its language around SOLIDWORKS and its convergence (or not) with the 3DEXPERIENCE platform and the CGM kernel. DS now sees says that it offers a a new portfolio that supplements the traditional SOLIDWORKS offering, bringing cloud and other technologies to the SOLIDWORKS users and channel, without disrupting the core products. They’re calling this “Powered by 3DEXPERIENCE” — it’s still in there, but clearly not as prominent as it was.

DS basically left its outlook for the rest of 2018 as it was, with a tiny downward move in services offset by slightly higher growth in software. It sees non-IFRS revenue growth for the year of 8% to 9% cc. For Q2, DS forecasts non-IFRS revenue of around €815 million to €830 million, which is cc growth of about 9%.

You might remember that last year DS made a bit of noise about a big Boeing win. DS said that its main impact isn’t financial (yet) but that it already affects how other clients look at the 3DEXPERIENCE and perhaps using the platform for service in addition to design/manufacturing and the other traditional applications. As for the financial contribution from the Boeing deal, for 2018 it will be an incremental €10 million; for 2019 Boeing alone will account for 1% of growth.

M. Charlès also reinforced DS’ commitment to the indirect channel, saying that he believes DS will ultimately see greater growth from indirect sources than its direct. He said that DS spends a lot of time and energy to ensure that value channel resellers understand their importance to DS and that coming cloud migration will not disenfranchise them. They will provide services and be directly involved in client success, he said, and remain crucial to both DS’ and the customer’s success.

Finally, why accounting matters. DS is now reporting using a couple of different accounting standards, including IFRS 15. IFRS 15 applies to reports issued after 1 January 2018 and has to do with how and when revenue is recognized. It’s meant ensure that companies use the same five steps to determine what can be recognized when, so that investors can better compare financials from one company to the next. For DS in Q1, it seems to have had a significant positive impact on reported revenue and, therefore, profit. From the company’s press release, total revenue was either €769 million under IAS guidelines, €737 million under IFRS 15 or €820 million under non-IFRS. Why should you care? Because public companies can legitimately cherry-pick which set of data they use to create the impression they want investors to have. It’s not unethical, it’s not illegal and it’s not really “wrong” –we all want to look good, right?– but it does mean that everyone needs to carefully read the fine print. Did DS do anything to bump total revenue from €737 million to €820 million? Sell more stuff? Find more customers? No. Purely accounting magic.

But don’t let that distract you too much –just read the fine print– from what were solid results for Q1 and a good start to the whole year.

The post DS: Industrie 4.0 is old news; makers and innovators will rule tomorrow appeared first on Schnitger Corporation.

► Quickie: Trimble acquires Viewpoint for $1.2 billion
  23 Apr, 2018

Just into my inbox: Trimble, makers of all sorts of software and hardware targeted at the broader AEC market is acquiring Viewpoint, which makes construction management software. More on this tomorrow, but Viewpoint fits perfectly (with some overlap) into Trimble’s portfolio, matching its design, planning and measurement technologies with Viewpoint’s contractor financials and resource management solutions.

This is a huge deal, with Trimble paying $1.2 billion to buy Viewpoint from Bain Capital. The PR didn’t give Viewpoint’s 2017 or 2018 revenue, but says that Viewpoint is expected to “contribute approximately $200 million of non-GAAP revenue in 2019 with operating cash flow of greater than $50 million.” The deal will close in Q3, after the usual regulatory approvals. Trimble will pay for the acquisition with cash on hand and new debt.

But that’s not all Trimble has been up to. When this news came, I was researching steel fabricators –the businesses that prefabricate beams and more complex structures– because Trimble just announced that it’s acquired FabSuite a supplier of steel fab management software, a bit like AVEVA’s Fabtrol. Trimble sees FabSuite plus Tekla creating a complete workflow for designing, planning, managing and automating the steel fab to maximize constructability and utilization while minimizing waste. As I learned last year at AVEVA World Conference in Houston, these businesses can be very old-line, and ripe for modernization.

More soon.


The post Quickie: Trimble acquires Viewpoint for $1.2 billion appeared first on Schnitger Corporation.

► Quickie: MuM announces most profitable quarter ever
  23 Apr, 2018

What trough? As a reminder, we look at Mensch und Maschine to gauge how resellers are faring as Autodesk transitions to subscriptions — and MuM is on the upswing with its Q1 2018 report. Per its announcement, “While the [proprietary MuM] Software segment continued to contribute a larger share [of earnings], the VAR Business [aka Autodesk reselling] grew at a much higher percentage. So the gap begins to shrink, as expected.”

A few of the details:

  • Total revenue in Q1 was €49 million, up 7%
  • MuM’s Software business reported revenue of €14 million up 9%;  the VAR Business reported revenue of €35 million, up 6%
  • The “most profitable” headline comes from highest-ever gross margins and earnings before interest, taxes, depreciation and amortization (EBITDA). EBITDA was €6.62 million, up 22% from a year ago

CEO Adi Drotleff commented in the press release that MuM’s “EBITDA target range of EUR 22-23 million is solidly underlined by the start of 2018, particularly as in Q2 the VAR Business sales are expected to speed up due to the renewal of many multi-year Autodesk maintenance contracts.”

Parse that quote and we start to see the value of subscriptions, from the vendor’s and reseller’s perspective: In Q2, MuM will see renewals of maintenance contracts — now a subscription — and customers have signaled to MuM that they intend to do so. Clearly good for MuM, but also good for Autodesk. We’ll see Autodesk’s side of the story in about a month, when they report on their fiscal first quarter.

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► PTC’s FQ2 featured ‘strong’ CAD, PLM performance
  19 Apr, 2018

PTC announced results on Wednesday that were, in a single word, impressive. Growth across most revenue categories, product lines, channels — and with positive outlook for the rest of 2018, too, which means no deals were pulled from the future into the fiscal second quarter to boost its appearance. Indeed, on the earnings call CEO Jim Heppelmann said that one large deal slid out of FQ2 into FQ3 (and has already closed), so FQ2 could have been even better.

The details:

  • Software revenue was $262 million, up 12%. That’s especially impressive given that the mix continues to shift to subscriptions —remember that subs are a smaller amount each quarter without the big bump upfront
  • That continues shift to subs means that around 91% of FQ2 software revenue came from recurring revenue, up from 88% a year ago
  • Subscription revenue was $113 million, up a crazy 72% from a year ago
  • Perpetual license revenue was down 17% to $23 million —not a bad thing, given the planned transition to subs– and now accounts for just 7% of total revenue. Let’s think about that. I just looked at PTC’s annual report from fiscal 2008, when license revenue was 30% of total revenue of $1030 million, maintenance was 50%, and consulting and other services made up the rest. In 10 years, PTC has shifted itself from being a deal-hungry sales machine to one that, in theory at least, focuses on adding value to subscriptions because it knows that renewal will be as hard to close as a brand new deal. It certainly hasn’t been easy but it does appear that the company and its customers are weathering the transition.
  • Total revenue in FQ2, which includes professional services too, was $308 million, up 10%
  • Solutions Software (CAD, PLM, SLM, etc.) revenue was $234 million, up 10% as reported and up 4% in constant currencies (cc). PTC said growth came from “strong CAD, PLM and global channel bookings performance over the past several years, despite a 1000 basis point increase in subscription mix in Q2’18 compared to Q2’17. Strong bookings in the past mean revenue now and in the future — so we should see growth continue.
  • As usual, PTC spent much of its air time on the earnings call talking about IoT, yet Solutions makes up solidly 90% of software revenue. A couple of nuggets from the earnings call: “Year-to-date CAD bookings have considerably outpaced market growth rates and our outlook for the balance of the year remains very strong” … “nine consecutive quarters of double-digit bookings growth in our reseller channel” … But “PLM declined sequentially in Q2 … due to the timing of large deals in the pipeline. On the first half basis, PLM bookings are tracking at market growth rates. The PLM pipeline for the back half of fiscal ’18 looks good and we expect this business to have a strong backup and deliver a full year growth at or above the market rate.” And, finally, SLM “has been performing below our expectations for a number of quarters [but] posted solid results in Q2 highlighted.”
  • In response to an investor question, Mr. Heppelmann said “We’re starting to sell more and more PLM in the cloud.” That’s interesting, given PTC’s recent announcement of a partnership with Microsoft. Cloud adoption in PLM (outside of CAE solvers, renderers, etc.) is still nascent as privacy concerns slow adoption — but we all know it’s coming
  • Solutions recurring software revenue grew 14% YoY and has grown double digits for five consecutive quarters. As our transition matures, recurring software revenue growth is expected to accelerate due to the compounding benefit of a subscription business model
  • The rest of PTC’s products are lumped into IoT Software, which reported revenue of $29 million, up 33% as reported (up 30% cc). PTC said this “Recurring software revenue grew 34% YoY and 13% sequentially on continued strong bookings growth, driving our total IoT software growth. Q2’18 subscription mix was about flat with the same period a year ago. As our transition matures, recurring software revenue growth is expected to accelerate due to the compounding benefit of a subscription business model.”
  • The channel, in general, seems to be doing well. PTC said that its channel “continues to exceed expectations, growing bookings double-digits for the ninth consecutive quarter.”
  • By geo, software revenue from the Americas was $113 million, up 6% as reported and cc. Interestingly, PTC said that new bookings were up 19% “despite a 900 basis point increase in the subscription mix.”
  • Software revenue from Europe was $98 million, up 20% as reported and up 8% cc with a “1200 basis point increase in the subscription mix in Q2’18 compared to Q2’17.” During his remarks, Mr. Heppelmann said performance in Europe is as expected and “based on the timing of large deals in the pipeline throughout the year primarily coming from our PLM segment.” PLM! Europe!
  • Finally, software revenue from APAC was $51 million, up 10% as reported and up 5% cc.  Mr. Heppelmann said that PTC saw “solid performance in China, Taiwan and Korea” and that progress continues in Japan, which now is tracking to a full year plan of modest growth.

Looking ahead, PTC sees the shift to subs continuing (obviously, since it is discontinuing sales of perpetuals in North America and Europe), which can continue to make for difficult comparisons in the short term. That said, FQ2 leads to optimism and an upward revision of revenue targets. Total revenue is now expected to be between $310 million and $315 million, or up 7% year/year, and up $13 million from prior forecasts. That $13 million takes into account a $10 million bump in recurring software revenue due to slightly higher renewal rates than previously estimated. For the year, PTC now sees total revenue of $1250 to $1260 million, up about 8% year/year — even with higher subscriptions driving a near-$40 million decline in perpetual revenue.

Let’s recap: Subscription revenue, up 72%; support aka maintenance down 11% but now included in subscriptions; perpetual licenses down 17% but, again, moved to subs. A work in progress as Mr. Heppelmann says: “We have to transform PTC into one of the premier software companies in the world. Our current plan says that by 2021, we will achieve revenues approaching $2 billion with double-digit growth rates and margins in the low 30s. [At LiveWorks in June, PTC will lay out for investors a long range plan through 2023]. I think you’ll enjoy seeing how compelling our business looks when all of the business model transition effects are finally behind us.” Okay then — but I think it looks pretty good right now.

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► PLMish earnings kick off with solid reports from PTC and AVEVA
  19 Apr, 2018

PTC started things off on Wednesday evening, with a report that exceeded expectations. Fiscal second quarter revenue was up nearly 10%, above the high end of the company’s guidance as well as Wall Street expectations. I’m working on a longer post for later today, but for now, know that performance in FQ2 was so strong that CEO Jim Heppelmann is raising targets for the rest of the year. Why? “For the first half of the fiscal year, CAD bookings grew double-digits, far outpacing market growth, PLM bookings grew at market, ThingWorx continued to set the standard for Industrial Innovation Platforms, and interest in our augmented reality (AR) solutions accelerated.” CAD bookings up double digits! More to come.

Today is the day that Schneider Electric reports earnings, too — for the first time since its software assets merged with AVEVA’s. Because Schneider will report AVEVA’s financials with its own, that led to a slight change in AVEVA‘s typical earnings calendar and prompted the company to issue one of its periodic updates. What did we learn? Not much — but it sounds positive. Schneider Electric said that acquisitions contributed €88 million to its Q2 revenue, a total that includes the one-month consolidation of AVEVA. For its part, AVEVA said that it saw a stabilizing of conditions in its oil and gas and marine end markets, which accelerated revenue growth in the second half of the fiscal year — leading to full year revenue growth of “comfortable double digits” in constant currencies basis, up from 6% in the first half. A little bit of math says that revenue growth must have rocketed in the second half.  As far as the Schneider Electric Software (SES) business, AVEVA says it was “solid” during the year ended March 31, with no real change in market conditions. In all, SES reports low single digit revenue growth on a constant currency basis. We’ll know more when AVEVA reports with more detail on June 14.

So, two reports from two very different companies and end-industry profiles. A solid start to this earnings seasons, no?

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► Siemens shines at Chicago’s DMDII
  18 Apr, 2018

Siemens held another one of its innovation days a few weeks ago, this time at the UI LABS DMDII facility in Chicago. I’ve attended several of these at Siemens HQ in Munich; this was the first I’ve been to at a neutral site and the focus was completely different. In Munich, the sessions were geared towards investors and were high-level, to suit that audience. In Chicago, the point was manufacturing: how Siemens does it and how Siemens can help customers do it more effectively. It was about opportunity: how Siemens uses its massive portfolio of people, products and technologies to show leadership in areas ranging from traditional and additive manufacturing IoT to autonomous vehicles to lightweighting to cybersecurity. And more, I’m sure — but I was only able to attend one of each time slot’s concurrent sessions.

UI LABS is a University + Industry (UI, get it?) collaboration that figures out ways to apply digital technologies to legacy industries. UI LABS started with the manufacturing and infrastructure industries and plans to branch out to other industries over time. DMDII, the Digital Manufacturing and Design Innovation Institute, is one of several sites where manufacturers, technology companies, inventors and academics can showcase what they’re doing, learn from one another and, it is hoped, move manufacturing as a whole forward.

For Siemens, DMDII also acts as a showplace, to bring the Digital Manufacturing concept to life via small production lines, taking a design from concept/CAE to design/CAD to manufacturing planning, to execution to operations to maintenance. One Siemens person told me that they cycle hundreds of users, prospects, academic partners and others (like the media, in this case) through DMDII each year. You can see Chuck Grindstaff’s take on DMDII —and why it’s important to Siemens— here:

This particular event, Siemens Innovation Day 2018, showcased how Siemens applies digital technologies to many (all?) of the verticals Siemens operates in — and, therefore, encourages customers to. The Siemens team from the aerospace, automotive and other verticals presented how the concepts of product twin, performance twin, and production twin work in their specific domains and across R&D, simulation/test, and manufacturing. It was, truly, about innovation within Siemens — and, not coincidentally, always with technology from Siemens PLM as the backbone.

As is usual for me, I learned a great deal about how the parts of Siemens outside the PLM business apply PLM technologies. For example, did you know that Siemens is light weighting electric engine parts for aircraft? I didn’t — but it makes so much sense that a company making both simulation technology and aircraft engine parts would figure out to use one on the other. Or that technology from recent acquisitions Mentor Graphics and TASS are being merged to produce physics-based simulated sensor data for driving scenarios and traffic situations, to speed up the virtual test of autonomous vehicle concepts? Or that this is just one element of a broader Siemens push towards smart mobility, where sensors embedded in roadways will monitor cars, pedestrians and traffic lights for greater safety and smoother travel for all?

My point: Siemens is active in so many different areas of power generation, manufacturing and mobility — that now also use its PLM tools. These business, in turn, may be some of the most critical users of NX, Simcenter, Teamcenter, Tecnomatix, Mindsphere, et al. and can inform further development of those technologies. I asked a couple of the people speaking on behalf of the Siemens non-PLM portfolio, and all said that their businesses rely on the close working relationship they have with the PLM team — not always perfect but always pushing forward. These close working relationships between users and maker moves everything, faster.

The formal presentations included keynotes from Siemens CTO Roland Busch and US Technology head Kurt Bettenhausen, who reinforced Siemens’ intention to be a global leader in electrification, automation, and PLM. They highlighted Siemens’ ongoing efforts in additive, IoT, advanced materials, the continued focus on simulation — and again said that Siemens is one of the world’s top 10 software companies. (I really wish they’d back up that claim, as they make it so often.)

Siemens PLM CEO Tony Hemmelgarn, Mentor CEO Wally Rhines and leaders from Siemens’ mobility group (which includes automotive industry products) spoke about how impossible it is to physically put autonomous vehicles through enough miles to ensure their operating safety; their success will hinge on simulations. Mr. Rhines and a colleague from Siemens’ Intelligent Traffic group later gave a keynote on “First and Last Miles, And Everything in Between—The Future of Driving” which touched on how simulations inform the artificial intelligence used is autonomous operations, and how Siemens offers sensors and other data collection systems in support of all sorts of autonomous transport projects.

There were other keynotes, too, that you can see here, and you can watch an interview with Mr. Hemmelgarn and Mr. Rhines here. You may need to agree to a disclaimer, since these are on the Siemens investor website.

The good stuff happened after the keynotes, when we toured the DMDII manufacturing space. As you might expect, there was a lot about MindSphere and MindApps, Siemens’ IoT platform and the specific apps that can offer visibility into machine tool operations, drive systems, inventory or whatever else one may want to have data about. In the case of the demonstrator production line, Siemens showcased how a MindSphere-connected sensor held up production while a worker replenished inventory, then rerouted some items to a secondary station. What was cool was how the production line was connected to build instructions: the worker’s picking bins were spotlighted to show which part to pick up; then the assembly was highlighted to show where to place it. A screen had more detailed instructions, if they were needed, but the worker could do the entire task while comfortably looking down. Nifty.

I also learned that there are now cheap, cheap after-market sensor modules* available for IoT retrofits. I am often asked if IoT is even possible in a case where old equipment doesn’t have a specific sensor —it may have temperature but not vibration, or it may have nothing because no one expected it to matter. The Siemens guys showed me a multi sensor that costs less than $10, is fully powered, and is meant to be stuck (literally, glued) onto the equipment you want to monitor. It lasts several years; then throw it away and provision a new one. The little production line at DMDII had a number of these scattered across the gear, to show that IoT doesn’t require all new equipment. Use what you have, add some of these sensors if needed, try an IoT app to see if it gives you insights you need. And if the sensors are on the wrong equipment (or improperly positioned), just move them! With these cheap sensors, a Siemens Nanobox (the edge computer that collects data, does some analytics and pushes data to MindSphere) and a MindSphere app or two, you can be testing an IoT implementation for $1,000.

One last thing: After the first Innovation Day I attended, I told you about next47, the Siemens in-house venture capital subsidiary. It didn’t really get formal mention in Chicago, but is a clear part of the strategy to get Siemens involved in startups and others working at the cutting edge of digitalization. One area of interest for next47 is mobility, such as vehicle-to-vehicle or vehicle-to-infrastructure communication for applications such as smart traffic control. Did you know that Siemens made the first electric traffic lights? Me neither. Siemens clearly wants to be at the forefront of the massive infrastructure modernization that will come when/if we ever get autonomous vehicles off the ground.

These Innovation Days never fail to impress. Combine Siemens’ sheer size, range of activities, industry and geo reach with the growing software portfolio– the potential combination are limited only by investment priorities and imagination. And Siemens has plenty of the latter.

Note: Siemens graciously covered some of the expenses associated with my participation in the event but did not in any way influence the content of this post. The cover picture is of Lisa Davis, Member of the Managing Board of Siemens AG, Chair and CEO of Siemens Corporation, USA, and responsible for the company’s Energy Divisions (Power and Gas, Power Generation Services) for Oil & Gas.

*Update: Several of you have written to ask about the sensors. The ones Siemens demonstrates are from Bluvision, a Siemens MindSphere partner company.

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