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Another Fine Mesh top

► Announcing the 2nd AIAA Geometry and Mesh Generation Workshop
  18 Jun, 2018
It’s official. The 2nd AIAA Geometry and Mesh Generation Workshop (GMGW-2) will be held the weekend prior to AIAA SciTech (5-6 January 2019) in San Diego.  This 2-day workshop will cover three meshing cases and provide forward-looking content for future … Continue reading
► Mesh Alignment Sources – New in Pointwise V18.1
  18 Jun, 2018
Among the many new features introduced in Pointwise V18.1, alignment sources may be the most subtle yet the most powerful. You can use them to align the cells in a surface mesh to any curve without increasing the cell count … Continue reading
► This Week in CFD
  15 Jun, 2018
So much meshing this week including the IMR’s meshing contest, geometry modeling and preparation, high-order, and interoperability among other topics. And there are ship propellers all over the place, including the one from Helyx shown here. All this and other … Continue reading
► Survey Results – Mesh Generation and CAD Interoperability
  13 Jun, 2018
Our survey on mesh generation and CAD interoperability collected 136 responses and provided some insight into file-based transfer of B-Rep/NURBS geometry models yet only scratched the surface of the issues involved.   It is easy to joke about how difficult and … Continue reading
► This Week in CFD
    8 Jun, 2018
This week I spent some time on Revolution in Simulation’s website to learn more about Sim Apps and democratization. There’s a fun article about CAD file formats and simulation and updates on software releases and events, including our own webinar … Continue reading
► I’m Tom Chan and This Is How I Mesh
    7 Jun, 2018
I consider myself a true Seattle native, but I was born in South Vietnam. At the time, my parents were living in a thriving Chinese community in Saigon (now Ho Chi Minh City). We were fortunate enough to leave in … Continue reading

F*** Yeah Fluid Dynamics top

► In some parts of the world, fog is a major source of freshwater,...
  19 Jun, 2018

In some parts of the world, fog is a major source of freshwater, but collecting it is a challenge. Most systems use a wire mesh to capture and collect droplets, but the process is highly inefficient, pulling only 1-3% of droplets from the fog. Researchers found that this is due largely to aerodynamic effects. The presence of the wire deflects droplets around it (bottom left). To solve this, engineers introduced an electric charge into the fog. The subsequent electric field actually pulls droplets to the wires (bottom right). When applied to a mesh (top), the efficiency of fog capture improves dramatically. 

The technique can also be used to capture water vapor that would otherwise escape from the cooling towers of power plants. The MIT researchers who developed the technique will conduct a full-scale test at the university’s power plant this fall. They hope the technique will recapture millions of gallons of water that would otherwise drift away from the plant. (Image credits: MIT News, source; image and research credits: M. Damak and K. Varanasi, source)

► Noctilucent – literally night-shining – clouds are a...
  18 Jun, 2018

Noctilucent – literally night-shining – clouds are a phenomenon unique to high latitudes during the summer months. Too dim and sparse to see in daylight, these clouds shine at night because their altitude of around 80 km allows them to catch sunlight long after dusk has fallen at the surface. They form when temperatures in the summer mesosphere drop to nearly -150 degrees Celsius, driven by perturbations that can originate in lower layers of the atmosphere on the opposite side of the Earth. Complex interactions and feedback between atmospheric waves, buoyancy, and Coriolis effect circulate those disturbances in such a way that the summer mesosphere can reach temperatures colder than any other place on Earth. Those frigid temperatures allow clouds to form even in this dry region near the edge of space. (Image credit: S. Stephens; see also: B. Karlsson and T. Shepard)

► Sand and other granular materials can be strikingly fluid-like....
  15 Jun, 2018

Sand and other granular materials can be strikingly fluid-like. Here the impact of a solid sphere on sand generates a splash remarkably similar to what’s seen with water. When the ball hits, it creates a crater in the surface and sends up a bowl-like spray of sand. As the ball continues falling through the sand, the grains try to fill the empty space left behind. The walls of sand collapsing around the void meet somewhere between the surface and the depth of the ball. This generates the tall jet we observe, as well as a second one under the surface that we can’t see. We know that collapse traps an air bubble under the surface because of the eruption that occurs as the jet falls. That’s the air bubble reaching the surface. (Image credit: T. Nguyen et al., source; see also R. Mikkelsen et al.)

► Day has turned into night for NASA’s Opportunity rover as a...
  14 Jun, 2018

Day has turned into night for NASA’s Opportunity rover as a massive dust storm envelopes Mars. The first signs of the dust storm were reported May 30th, and over the last two weeks, the storm has grown to an area larger than North America and Russia combined. Despite the low pressure and density of Mars’ atmosphere, solar heating can create fairly strong winds – they don’t reach hurricane-force speeds, but they’d qualify as a very windy day here on Earth. With the lower gravity on Mars, this can lift dust well into the atmosphere, choking out the sunlight Opportunity needs to continue operating. The rover has entered a low-power mode and is no longer responding to communications. Martian dust storms have been known to last for weeks or even months, and this may be the last we hear from the intrepid rover on its fifteen year journey. Here’s hoping that Opportunity makes it through the storm and can eventually get the solar power needed to phone home again. (Image credit: NASA JPL)

► Giving droplets a kick by accelerating the surface they sit on...
  13 Jun, 2018

Giving droplets a kick by accelerating the surface they sit on creates elaborate shapes as the drops respond. As the surface accelerates upward, the droplet flattens into a pancake. When the plate slows down, the droplet continues rising, stretching into a cone as its rim flies upward and its lower surface adheres to the surface. The rim retracts with a constant acceleration while the drop detaches with a constant velocity. That velocity depends on how well it adheres to the surface. The interplay between those two variables determines how conical or cylindrical the drop appears. See more in the full video below. (Image and video credit: P. Chantelot et al.)

► Blocking blood vessels by creating embolisms is, under most...
  12 Jun, 2018

Blocking blood vessels by creating embolisms is, under most circumstances, very bad. But researchers are exploring ways to fight cancer by intentionally and strategically creating these blockages. In gas embolotherapy, researchers inject fluid droplets, which can carry chemotherapy drugs, into the bloodstream. Once they circulate into a cancerous tumor, they use ultrasound to vaporize the droplet and create a gas bubble. Those bubbles lodge inside the capillaries of the tumor, starving it of fresh blood and trapping the chemotherapy drugs inside. It’s a one-two punch to the cancer. Without blood flow, the cancer cells die, and, since the cancer-killing drugs get mostly trapped inside the tumor, patients may require lower dosages and endure fewer side effects. The technique is currently in animal testing, but hopefully it will be a valuable therapy for human patients in the future. (Image credit: Chemical & Engineering News; research credit: Y. Feng et al.; via AIP)

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

► Lognormal Distribution for Particle Size in OpenFOAM
    1 May, 2018
This is for future reference, if anyone wants to use lognormal particle distribution in OpenFoam.
1) Generate PDF from following formula

Here mu and sigma are the geometric mean and standard deviation of the particle size distribution
Here is an example plot of the lognormal distribution with mean 1 and standard deviations of 1.8, and 3. You can first re-create lognormal distribution with these parameters and compare with the one in the fig, to be sure that you are doing it write.

you can use programs like MS excel or matlab to generate data from above distribution
2) Select general distribution in the cloud properties dictionary in constant directory, and copy and paste your generated values under distribution,
the first column is particle size and the second is the probability.
I hope this will help you.
Thank you.
I have attached an excel file that I used to generate lognormal distribution for my work. I have also attached the a sample kinematicCloudProperties dict.

logNormal Pdf.xlsx

► 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

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.

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!

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► Computer Aided Design + Computer Aided Engineering = Digital Exploration
  18 Jun, 2018

ANSYS and PTC partner to deliver simulation for every engineer!

What if every engineer had digital tools that gave them perfect insight into the performance of their physical product and allowed them to almost effortlessly gauge the impact of every design choice made? That is of course the holy grail of digital exploration. While we are not there yet, today we take another big step on this quest by partnering with PTC.

PTC was the first to introduce a parametric modeling CAD system, giving engineers the tools to digitally design and document the form of their products and parts. Today, the parametric paradigm is pervasive across industries, and PTC Creo is one of the most used CAD systems in the engineering world.

As you know, at ANSYS we have a mission of making simulation pervasive, so every engineer can benefit from the insight CAE gives regarding the function of their product. So we are thrilled to partner with PTC to bring our Discovery simulation technology natively to PTC Creo and its users, combining form and function.

Hopefully­­­ you have by now experienced ANSYS Discovery Live and the breakthrough experience of intuitive, interactive, instantaneous simulation. Where simulation used to have a steep learning curve and take hours if not days to perform, we consistently hear that even novice users are up and running in 15 minutes and iterating through options and results in seconds.

The partnership between PTC and ANSYS will bring the two worlds of parametric modeling and interactive simulation together so engineers can digitally explore designs at the speed of thought. The first product to ship as part of this partnership will be PTC Creo Live. Subsequently we will work together to deliver the breadth of simulation capabilities in ANSYS Discovery AIM to PTC Creo as well. You can see a sneak preview of the product experience above.

For designers and engineers this should be great news. You get more tool choices so you can pick the ones that are right for you. If you are a PTC user or a fan of the parametric paradigm, check out PTC Creo Live. If you are looking for a purpose-built innovation and simulation tool give ANSYS Discovery Live a try. Both products provide a great experience and deliver on the promise of digital exploration.

My question to you is: What will it do to your product design if you can get engineering insight in almost real-time and iterate through multiple choices in minutes? At ANSYS and PTC, we are excited to see what you create!

The post Computer Aided Design + Computer Aided Engineering = Digital Exploration appeared first on ANSYS.

► ANSYS at LiveWorx18: Simulate. Iterate. Innovate.
  14 Jun, 2018

Join ANSYS at LiveWorx18, the world’s most respected digital transformation conference, June 17-20, in Boston, Massachusetts. As product development is transforming, we’re continuing our mission of making engineering simulation pervasive across the product lifecycle — from early exploration to digital prototyping and operations. Stop by Booth #316 to see live demonstrations of our new products that are transforming the simulation industry.

ANSYS Discovery Live: This game-changing technology is altering the nature of engineering simulation and the way we design products. For the first time, all designers can access broad, real-time simulation on their desktops and interactively explore their models to gain valuable insight. With our latest release of Discovery Live, users will see results that are more accurate and robust, and obtain instantaneous feedback in a shorter amount of time. Whether measuring pressure or velocity, transient solutions settle down significantly faster than before, resulting in a higher degree of confidence in the simulation in a shorter period of time.

Four-box graphic showing ANSYS Discovery Live capabilities

Whether visualizing velocity as surfaces, iso-surfaces or a composite smoke-like view, users will gain faster, more accurate and smoother fluids simulation results in Discovery Live.

ANSYS Twin Builder: This just-released predictive maintenance tool quickly and easily builds, validates and connects your digital twin to industrial internet of things (IIot) platforms, including PTC ThingWorx. Using product-mounted sensors, users can collect operating data from the field in real time, and use that information to create an exact replica of the working product in a controlled virtual space. By studying how the simulated product model performs under real-world conditions, users can flag any performance issues, schedule predictive maintenance and reduce downtime. Twin Builder also reduces the twin creation time by half (through model reuse), optimizes product performance by 25 percent and improves operations with a 10 to 20 percent reduction in maintenance costs.

With ANSYS Twin Builder, you can quickly and easily build, validate and deploy digital twins.

ANSYS Additive Suite: Our new suite of products for metal additive manufacturing (AM) delivers the critical insights required by designers, engineers and analysts to avoid build failures and create parts that accurately conform to design specifications. This comprehensive solution spans the entire workflow — from design for additive manufacturing (DfAM) through validation, print design, process simulation and exploration of materials. It includes:

  • Topology optimization for weight reduction and lattice density optimization.
  • STL file and geometry manipulation for geometry repair, lattice creation and cleanup of parts using the software’s faceted data tools.
  • Structural and thermal analysis and design validation that can be applied to models under a variety of thermal and structural loading conditions, for better understanding of performance and durability.

Layer-by-layer additive manufacturing (AM) process simulation

Please visit our event landing page for additional product information. And don’t forget to join us in Booth #316 to see live demonstrations. We hope to see you there!

The post ANSYS at LiveWorx18: Simulate. Iterate. Innovate. appeared first on ANSYS.

► ANSYS Real Time Configurator Allows Yachting Industry to Navigate Design with Simulated Prototypes
  12 Jun, 2018

Combine the ANSYS Virtual Reality (VR) configurator with Aston Martin luxury in a powerboat, and you have a yacht that James Bond would have looked good in while chasing the bad guys in his inimitable style. (Bond loved driving his Aston Martin automobile starting in the series’ third movie, “Goldfinger,” in 1964 and continuing for a span of 11 films over 30 years.) That’s the experience that Quintessence Yachts, an Aston Martin official licensee, is presenting at TechXLR8 in London this week— the Quintessence AM37 boat configurator, based on ANSYS VR simulation technology — which is poised to revolutionize the design, feel and appearance of powerboats.

Instead of creating a CAD design and then making a series of physical models of a new yacht to explore the possibilities of materials and color, ANSYS virtual reality simulation makes it possible for designers, engineers and customers to view virtual models of the yacht in 3D from any angle. Working together, the designer and customer can customize the yacht’s design materials, determining how they interact with different high-quality paint colors in the Aston Martin palette under various lighting conditions, to achieve the customer’s dream yacht.

The dream yacht could be the AM37, a ‘gran turismo’ leisure powerboat with a maximum speed of around 44 knots and premium-class quality and refinement, or the AM37 S with twin 520 hp Mercury gasoline engines for a maximum speed of up to 50 knots (to catch the bad guys faster).

Using the Quintessence AM37 boat configurator, powerboat engineers can create and modify their products using a touch-screen to change the colors or ergonomics of the design, then view it in real-time 3D on the Quintessence website, a mobile phone or a VR headset. Viewing the details of the yacht from any angle is like exploring it in a showroom, only better. The customer can remotely climb into the cockpit, tour the cabin, walk on polished wood deck, and watch the sun gleaming off the chrome fixtures.

ANSYS VR simulation not only accelerates the design process to save time and money, but it ensures that the design will be optimized for aesthetics, ergonomics and performance. This kind of optimization is hard to achieve using traditional physical prototyping methods.

If you are attending TechXLR8 in London this week, you can see the Quintessence AM37 boat configurator in action at AR & VR World, one of the eight conferences brought together under the one roof of TechXLR8. Tell them “Bond — James Bond” sent you.

The post ANSYS Real Time Configurator Allows Yachting Industry to Navigate Design with Simulated Prototypes appeared first on ANSYS.

► Student Team Uses Electromagnetic Simulation to Design Hyperloop Brakes and Motor
    8 Jun, 2018

KN Trakcji i Torów is a registered organization in the department of electrical engineering at the Warsaw University of Technology in Poland. We are an ANSYS Academic Student Team conducting research and running electromagnetic simulation for better understanding of electric traction. During 2017 and 2018, we focused on two main projects:

  • Designing electromagnetic passive brakes for a prototype hyperloop vehicle.
  • Designing a permanent magnet, linear synchronous motor for personal rapid transit and Maglev technology.

Electromagnetic Brakes

In 2017, our student group was a part of the Hyper Poland University Team, which designed a prototype for the Hyperloop Pod II Competition organized by SpaceX. In August 2017, our design made it to the finals of the competition. This achievement was made possible by student engineers who designed passive electromagnetic brakes to slow down the Hyperloop pod from 100 m/s and bring it to a stop.

The electromagnetic brakes were based on NdFeB N52 permanent magnets. The vehicle used four Halbach arrays of permanent magnets as a primary braking system. A Halbach array works by strengthening the magnetic field on one side of a magnet pack while reducing the other side to near zero magnetic field using a spatially rotating magnetization pattern. Magnet packs were positioned symmetrically on both sides of the central aluminum rail, so that the strengthened magnetic field faced the aluminum rail. Permanent magnets were arranged radially, with the magnetization direction of every magnet cube rotated 45 degrees in relation to its neighbors (see magnetization arrows in individual magnets in Figure 1).

Figure 1. Braking magnets package with magnetization direction above aluminum rail (dimensions in mm)

While moving along the rail, permanent magnets cause eddy currents in the aluminum. The eddy currents create their own magnetic field, which counteracts the magnetic field of the permanent magnets. As a result, a perpendicular force repels the permanent magnets from the aluminum rail. Parallel forces also counteract the movement of the magnets, causing the Hyperloop pod to slow down.

To design the electromagnetic brakes, we conducted hundreds of 3D simulations using ANSYS Maxwell. In these simulations, two sets of permanent magnets moved along the aluminum plate in transient mode to reveal all the eddy currents and magnetic fields in the model (Figures 2 and 3).

Figure 2. Screenshot from transient simulation

Figure 3. Cross section of the model showing predicted magnetic field in the air gap

After a series of simulations, we were able to estimate the final dimensions of the brakes and required number of magnetic cubes. We obtained the following results for one braking Halbach array (Figures 4 and 5):



Figure 4. Characteristic stabilizing force generated by one braking Halbach array versus vehicle speed





Figure 5. Characteristic braking force generated by one Halbach array versus vehicle speed



Permanent Magnet Linear Synchronous Motor

At the end of 2017, our team became interested in personal rapid transit (PRT) and Maglev technology. We decided to design a single-sided, permanent magnet linear synchronous motor (PMLSM) for contactless propulsion and levitation of a PRT or Maglev train to transport people.

Figure 6. Screenshot from simulation of PMLSM propulsion for Maglev

We are still performing the simulations using the transient mode of ANSYS Maxwell 2D; the optimal dimensions have not been obtained yet. When we have determined the dimensions, we will conduct 3D simulations. In the next two years, we are planning to build a small-scale model of a Maglev train and begin investigating the requirements of a full-scale vehicle. To learn more about our team, please follow us on Facebook at

The post Student Team Uses Electromagnetic Simulation to Design Hyperloop Brakes and Motor appeared first on ANSYS.

► Position Tracking Yields Efficient Autonomous Travel
    7 Jun, 2018

Some highly publicized benefits of autonomous vehicles (in addition to driver downtime) are the reduction of traffic congestion and increased safety. Today, much of autonomous systems engineering centers on adapting cars, trains, buses, trucks and drones that were originally designed for human operators. But, what if, in addition to changing how vehicles are driven, we also alter the environment to make it more conducive for autonomous navigation?

Autodrive Solutions, a startup from Madrid, is developing systems to make autonomous driving safer and more coordinated by mapping routes with plastic paint spots (for roads) or bars (for rail tracks). Using these markers and an onboard radar unit that reads them, a central host computer will determine each vehicle’s location to within a centimeter so that traffic flow can be synchronized and improved.

radar position system for vehciles

The company used software obtained through the ANSYS Startup Program to design portions of its Radar Positioning System (RPS). It leveraged ANSYS HFSS SBR+ to design the lens that focuses the radar waves onto the paint dots to achieve the 1-cm positioning accuracy required, and ANSYS SCADE to develop the safety-critical embedded software that will flawlessly control the hardware and meet rigid EU certification standards.

paint bit for position tracking autonomous vehciles

Those of us in North America understand roadway traffic jams and how a centrally controlled system could smooth our commutes. In other areas of the world where train travel is more common, the autonomous operation of railways can prove highly productive. Trains already perform a preprogrammed braking process when passing by a radio frequency identification (RFID) unit. However, a large number of variables (number of cars, wear, etc.) must be considered with regard to braking time, which a positioning system could help to quantify. And, considering the number of trains on a limited number of tracks, exact positioning would provide more accurate speedup and slowdown ranges to deliver faster passage and larger volumes on crowded routes. Continual speed adjustments on the rails could result in a savings of 20 percent of the 8-billion-euro energy cost of the European train fleet each year.

ANSYS SCADE proved to be mission-critical to Autodrive Solution’s engineers, saving the company 80 percent in development time in getting RPS to market. Learn more in the ANSYS Advantage article Autonomy on Roadways and Railways.

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► New ANSYS Fluent Dynamic Mesh Captures Wall Degradation Due to Erosion
    5 Jun, 2018

Erosion of pipe walls due to solid particles suspended in a carrier fluid has been a matter of concern for many industries, but the oil and gas industry is the one that suffers the most. The production of sand as part of drilling operations makes it almost impossible to operate equipment without some erosion.

Corroded with rust and poor condition of pipe line on board tanker vessel

A tanker’s corroded pipes

Physical tests to determine part life are expensive and time-consuming.  The industry needs to be able to model this phenomenon with high-fidelity so they can predict life and set maintenance schedules accordingly. By high-fidelity, I mean not only the validity of the erosion model for the operating conditions, but also the ability to calculate local changes in flow due to material removal.

At ANSYS, we have been helping our clients in this area by providing a user-defined erosion module that is included in the latest release of ANSYS Fluent. Our customers who have been using the older method will find this new model mesh easier to set up, and will benefit from more robust wall deformation calculations. As usual, the new model has gone through rigorous testing and validations based on published material. Below, I will share some salient features of the model and a validation case. I will not discuss the basic overview of erosion as that was covered in an earlier blog by my colleague, Mohammad Elyyan.

The model is called Erosion Dynamic Mesh. It is activated as soon as erosion is checked under “physical models” in the discrete phase model (DPM) menu shown below.

Once the model is turned on, it is straightforward to set up a wall for erosion calculations and specify its density. There are four reference erosion models included. You can also modify model constants or load your own user-defined erosion rate model. The main panel also allows for automatic dynamic mesh setup, which takes care of the wall deformation. Within dynamic mesh setup, the boundary layer mesh on the wall moves with the deformation to keep the wall cells from stretching abnormally. The “Run Erosion-Dynamic Mesh Simulation” button in the software takes you to the run time settings, as shown in the picture. Here you can set the fixed or variable time stepping method, specify the total time of erosion, save files and pictures and generate an automated mesh deformation report. Simulation progress is shown as elapsed time at the bottom of the screen.

Several cases were tested using this new workflow. Here we describe a case that is based on the experimental paper published by Nguyen et al., 2014. This is a flat plate erosion scenario in which a jet of liquid hits the plate at 30 m/s. The suspended sand particles cause the plate surface to erode. The geometry setup is shown here.

Figure 1. Geometry setup shown (above) with flat plate erosion model (below)

The Oka erosion model with default settings was used, and the predicted erosion pattern was compared with the measured data. Figure 2 shows this comparison.

Figure 2.  Depth of erosion along a diametrical line at three different times

The x-axis runs across the eroded area as shown by the black line in the top right corner inset. The y-axis shows the current location of surface nodes compared to the original uneroded state. Our simulation results match the trends of Nguyen’s experiment quite well at three different times (5, 15 and 30 minutes). The absolute value of erosion is also close in the simulations and the experiments. These numbers can be improved by tuning the Oka model to the test condition.

The effect of the number of particle streams on the erosion magnitude is also important (see Figure 3).

Figure 3. Erosion rate dependence on the sample size of the discrete phase

More tracks result in a smoother erosion pattern. However, calculating more tracks means more CPU time. Hence, there is an optimum number of tracks beyond which the simulated results change very little. From Figure 3, this optimum number is somewhere between 50 and 100 tracks.

The mesh smoothing scheme is quite robust. Together with cell remeshing, it allows you to set up simulations with very large deformations. The wall node motion logic is general enough to be employed for other rate-based phenomena, such as corrosion or ablation due to surface reactions, as one example. For details, please contact me at

Take a look at this video to see examples of erosion-dynamic mesh simulations:

To learn more about the latest fluids innovations, watch our webinar.

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► Return of an Old Friend: One Engineer’s Thoughts on Tecplot 360
  31 May, 2018

You may have seen the press release: starting May 31, a version of the Tecplot 360 flow visualization software will be packaged with CONVERGE. No corporate details here–this is an engineer’s viewpoint. I am a longtime Tecplot user, having worked extensively with nearly every version since 2008 R1. I’m not a trainer, so I won’t try to teach you how to use Tecplot (if you’d like to see a CONVERGE-focused introduction to Tecplot 360, Tecplot Product Manager Scott Fowler gave a webinar earlier this year). Rather, I’ll tell you what I like about it as a CFD research engineer, and what you might like too. The brief version: Tecplot for CONVERGE is a user-friendly tool that works well and makes sense.

To me, the most important characteristic of any tool is usability. If I can’t figure out how to make it work, it’s of no use to me. My introduction to Tecplot involved no formal training and no user guide (although I’m sure there was one available). I was working off of nothing except for my fellow graduate students and a willingness to experiment. It turned out that this was enough!

Tecplot’s user interface is approachable and unintimidating. The workflow is logical and smooth. Some software packages give the impression that they were designed by a GUI team with no engineering knowledge; some packages look like they were written by engineers with no clue about interface design. Tecplot bridges that gap. The answer to “How do I…” is usually logical and straightforward, and Tecplot feels like it was designed from the ground up by a team that had extensive experience with CFD.

Let me give you an example. Suppose I’m loading a complex 3D flowfield. When I first load that dataset, I don’t know exactly how I want to visualize it. I will probably pan, zoom, and rotate through a wide variety of views, trying to figure out the best perspective from which to visualize my flow. As with many packages, in Tecplot I can do this with just the mouse, without resorting to menu buttons to change modes. The difference is, once I find a view I like, Tecplot gives me precise control over the camera location and direction. If I alter the view and don’t like the change, I can revert the view setup. If I want to compare several different datasets, I don’t have to fiddle around with the mouse controls to get approximately the perspective I want. I can copy the center of rotation and spherical angles and get precisely the right view, with a minimum of fuss. As my understanding of my dataset grows from general familiarity to exacting detail, Tecplot offers me increasingly exacting controls.

I like Tecplot’s approach to data and file structuring. In my workflow, all data at a certain simulation time is written to a single <casename>_<filenumber>_<time>.plt file. Because of this one-to-one relationship, I can see at a glance how many datasets I have available for my case (rather than having different files for different variables). When I load my .plt file, data are structured by zone. Each zone might have different variables (e.g., a fluid zone versus a parcel zone), and I can display them differently. I can extract sub-zones (e.g., a slice from a fluid zone) and display those separately. If I loaded multiple data files, I can animate zones over time. If I write out a new .plt file, I can write specific zones to that file. This is especially helpful for, say, preserving interesting cross-sections of a very large volumetric dataset.

Importantly, Tecplot for CONVERGE retains nearly the entire feature set of a stand-alone Tecplot 360 installation. It is not limited in cell count, processor count, plot details, data alteration, or most other functional details. The chief restriction is in file formatting. Tecplot for CONVERGE is limited to output files that have been converted using a new post_convert executable. This post_convert will be released with Tecplot for CONVERGE and will be included with all future CONVERGE Studio and CONVERGE solver packages. Make sure to select the “Tecplot for CONVERGE” or “Tecplot 360” option in post_convert when converting output files. If you opt to purchase a full Tecplot 360 license, of course it is able to read these specially formatted files.

Tecplot offers powerful data alteration and calculation tools, as well as a robust scripting capability. Once you tell Tecplot which variable names correspond to which state variables, Tecplot can calculate on demand many derived quantities commonly used in CFD. No more trying to remember the functional form of the Q-criterion, because it’s built in. If the quantity you need is not included in this hundred-odd set, you can directly specify whatever calculation you wish to perform. Further, all of this can be scripted in a macro. You can record macros through the GUI (which journals all of the actions you’ve performed) and play them back, or you can write a plaintext macro directly.

Finally, Tecplot makes attractive images and animations! Much like the viewport commands, Tecplot adopts sensible defaults but gives you exacting control when you want it. I can add, adjust, and remove control points on the contour plot color map, banded or continuous, and drop in contour lines at specific values. I can plot spray parcels by various shapes and in various colors and sizes (including by spray variable values). Tecplot’s flexibility allows me to make a plot in twenty seconds that looks pretty good, spend twenty minutes making it look exactly right, or anywhere in between.

I love Tecplot, and I am very happy that Convergent Science is partnering with the Tecplot team. Not every engineer prefers the same tool, of course. CONVERGE will continue to support a wide range of flow visualization and analysis packages through the post_convert utility. But no matter your present visualization tool of choice, I encourage you to take Tecplot for CONVERGE out for a spin. I think you’ll like what you see.

► 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.

Numerical Simulations using FLOW-3D top

► Software Engineering Intern
  14 Jun, 2018

Flow Science has an open position for a Software Engineering Intern for the Summer or Fall of 2018. The principal tasks and responsibilities of a Software Engineering Intern is supporting our Software Engineers in their design and implementation of new elements and improvements in the FLOW-3D GUI.


Recent Computer Engineering or Computer Science graduates (December 2017 or May 2018) with a Bachelor’s or Master’s degree.


The ideal candidate for this internship will have experience in object oriented programming and exposure to C++, excellent oral and written communication skills, excellent interpersonal skills, and the ability to work both independently and as part of a team.


A resume and cover letter should be e-mailed to Please reference “Software Engineer Application” in the subject line or body of the email.

Applicants must be available to attend an onsite interview in Santa Fe, New Mexico.

Learn more about careers at Flow Science >

► Software Engineer
  13 Jun, 2018

Flow Science has a job opportunity for a Software Engineer. As a Software Engineer, you would use your object oriented programming skills to create and maintain the user interface between our simulation software and the end user. You’ll have an opportunity to combine your creative skills with your analytic skills to contribute to a tool which is used by customers around the world.

Required education, experience, and skills

  • Bachelors degree or higher in computer engineering or computer science.
  • Experience using object oriented programming techniques with C++
  • Entry level to 2 years of experience

Desirable experience and skills

  • Qt
  • OpenGL
  • Sockets programming

Attributes of a successful candidate

  • Conceptual/creative thinking and problem solving skills
  • Adaptable, inquisitive and open-minded
  • Excellent oral and written communication skills
  • Ability to work in a collaborative environment
  • Ambitious and highly motivated


Flow Science offers an exceptional compensation and benefits package superior to those offered by most large companies. Flow Science employees receive a competitive base salary, employer paid medical, dental, vision coverage, life and disability insurances, relocation assistance, 401(k) and profit sharing plans with extremely generous employer matching, and an outstanding incentive compensation plan that offers year-end bonus.


A resume and cover letter should be e-mailed to Please reference “Software Engineer Application” in the subject line or body of the email.

Learn more about careers at Flow Science >

► Combined Sewer Overflow
    6 Jun, 2018

A typical combined sewer system collects stormwater runoff, domestic and industrial waste into a single pipe for transport. In normal conditions, the combined wastewater is sent to a treatment plant. However, during especially strong storms or increased runoff due to snowmelt, the amount of wastewater may exceed the sewer system’s capacity. In such cases, the Combined Sewer Overflow (CSO) is directly discharged into nearby water bodies. This discharge poses an environmental and human health hazard, and since the passage of the 1972 Clean Water Act, municipalities across the country have been working to abate their CSO discharge.

Improving CSO systems entails complex hydrodynamics, complicated geometry, and requires many iterations for an optimal design to limit the overflow to meet regulations. In this blog, I will discuss how FLOW-3D can be used to design or modify complex CSO systems.

Combined sewer overflow
Snapshot of a FLOW-3D simulation showing wastewater moving through the CSO system

The illustrated CSO system has two interceptors that sequentially receive the wastewater before an overflow event. If the capacity of the first interceptor is not sufficient, a second interceptor can take the remainder. Sometimes, however, even if the capacity of a CSO is greater than the net inflow of wastewater, overflow can still occur. This may be due to the geometry of the CSO system not accounting for flow characteristics such as bulking.

Flow rates CSO system
Plot showing the evolution of flow rates in various parts of the CSO system

Another aspect of CSO system design is the possibility of the pipes, interceptors or any other part of the system getting pressurized.  High pressurization can cause damage to sewer pipes in the short and long term. FLOW-3D can help designers accurately predict the parts of the CSO which are susceptible to high-pressure forces.

FLOW-3D’s Volume-of-Fluid (VOF) method for tracking free surfaces, combined with turbulence models, is ideal for accurately modeling CSOs. Additionally, the scalar and chemistry models can help the user understand the changes in the concentration of various waste species in the effluence as it moves from the inlet to the water body.

Capturing all the relevant physics in the minutest of details, particularly for large and complex sewer systems, can be a computationally challenging task. For the largest of these problems, FLOW-3D can be run on hundreds of cores using high-performance computing (HPC). It is also possible to avoid the cost of HPC hardware and maintenance by running the simulations entirely on FLOW-3D Cloud.

All in all, FLOW-3D provides a comprehensive and accurate set of physics models to design an optimal CSO system in an optimal time frame.

► Flow Science Recognized as New Mexico Family-Friendly Workplace
  10 May, 2018

Santa Fe, NM, May 10, 2018 —Flow Science again this year earned gold distinction for its workplace policies by Family Friendly New Mexico, a statewide project developed to recognize companies that have adopted policies that give New Mexico businesses an edge in recruiting and retaining the best employees.

Flow Science offers a best-in-class and extremely rich benefits package to its full-time employees, including excellent employer-paid health insurance, a generous employer match on employees’ 401(k) contributions, 3 to 4 weeks of paid vacation, paid parental leave for the birth or adoption of a child, a wellness allowance for stress reducing and health enhancing activities, flexibility in work arrangements, and much more, said Aimee Abby, Human Resource Manager at Flow Science. Based on independent survey results, we know that Flow Science’s benefits are in the 90th to 100th percentile in our industry, even among much larger employers. We want the best employees and so seriously compete for them by offering these top-of-the-line benefits.

The Family Friendly New Mexico project has taken a positive approach to transforming workplace policies in the state by offering training and resources on how businesses can adopt more family-friendly policies; awards and recognition for companies that offer their employees family-friendly benefits; and a resource and clearinghouse of information for businesses and community leaders as they develop policies on issues such as paid family leave and help with childcare.

As we grow the state’s economy, we have the opportunity to be a national leader in offering New Mexicans workplaces that help companies attract and keep the best workers, said Giovanna Rossi, head of Family Friendly New Mexico. Implementing family-friendly policies can be a simple, concrete investment a company can make to ensure it can compete for highly qualified employees.  Studies have shown that costs associated with creating family-friendly benefits are more than made up for in improved productivity, employee morale and employee retention.

Family-friendly policies include a wide range of options that companies can adopt, including providing access to health insurance; paid vacation; making reasonable accommodations for pregnant employees; flexible leave time for parents to attend to their kids’ medical or school needs; paid family leave; and ensuring workplaces have proper breastfeeding space and storage, among many other family-friendly benefits.

The New Mexico Task Force on Work Life Balance has created an online business award called the New Mexico Family Friendly Business Award, to recognize and celebrate New Mexico businesses that have family friendly policies in place, including paid leave, health support, work schedules and economic support. The statewide task force was created by the New Mexico State Legislature in 2010.  Any New Mexico business is eligible to apply for the New Mexico Family-Friendly Business award.  Go here for a full list of family-friendly policies and to learn more about the New Mexico Family Friendly initiative >

► 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:

Mentor Blog top

► Blog Post: “FloEFD 3D CFD Accuracy in the Aerospace Industry – Unsteady Heat Conduction in a Solid"
  19 Jun, 2018
Continuing the theme of the last several posts we will again look at a heat transfer example with the  Unsteady Heat Conduction in a Solid. To validate heat conduction in solids (i.e., a conjugate heat transfer), let us consider unsteady heat conduction in a solid. To compare the FloEFD predictions with the analytical solution (Reference 1), we will solve a one-dimensional problem. A warm solid rod
► Technology Overview: FloTHERM Quick Tips: Selecting Monitor Points for Profile plots
  19 Jun, 2018

How to select monitor points for profile plots in FloTHERM. Test drive FloTHERM FREE for 30 days in the cloud

► Technology Overview: FloTHERM Quick Tips: Creating Scenarios using Superposition in Command Center
  19 Jun, 2018

Creating scenarios using the superpostion function in FloTHERM command center. Test drive FloTHERM FREE for 30 days in the cloud

► Technology Overview: FloTHERM Quick Tips: Creating Scenarios using the Multiply Variables function in FloTHERM Command Center
  19 Jun, 2018

Creating scenarios using the multiply variables function in FloTHERM command center. Test drive FloTHERM FREE for 30 days in the cloud.

► Technology Overview: FloTHERM Quick Tips: Selecting Input Variables in Command Centering
  19 Jun, 2018

Introduction to selecting input variables in the FloTHERM command center. Test drive FloTHERM FREE for 30 days in the cloud.

► Technology Overview: FloTHERM Quick Tips: Profiles Viewing Plots
  19 Jun, 2018

Viewing profile plots in FloTHERM V12. Test drive FloTHERM FREE for 30 days in the cloud

Tecplot Blog top

► Tecplot and Convergent Science Announce Partnership
  31 May, 2018

CONVERGE licenses to include complimentary Tecplot 360 license, creating powerful and seamless CFD and visualization.

BELLEVUE, WASHINGTON – May 31, 2018 – Tecplot is pleased to announce the formation of a new partnership with Convergent Science. As of June 1, each CONVERGE license will include a complimentary Tecplot license, giving CONVERGE users access to Tecplot 360’s powerful CFD visualization and analysis tools.

Tecplot for CONVERGE: Interior Isosurface

CONVERGE results in Tecplot 360 showing interior isosurface. See Tecplot for CONVERGE.

“We are very excited to be partnering with Convergent Science, a global leader in CFD,” says Tom Chan, President of Tecplot, Inc. “They have a great team and we look forward to working with Convergent Science to help their user base seamlessly use Tecplot 360 to better comprehend and communicate their CFD results.”

Eric Pomraning, Vice President and Co-Founder of Convergent Science, says, “We are thrilled to have joined forces with Tecplot. Their visualization software is first-class and will provide our CONVERGE users with an effective and user-friendly tool to better understand the results of their CFD simulations.”

About Tecplot, Inc.

An operating company of Toronto-based Constellation Software, Inc. (CSI), Tecplot is the leading independent developer of visualization and analysis software for engineers and scientists. CSI is a public company listed on the Toronto Stock Exchange (TSX:CSU). CSI acquires, manages and builds software businesses that provide mission-critical solutions in specific vertical markets.

Tecplot visualization and analysis software allows customers using desktop computers and laptops to quickly analyze and understand information hidden in complex data, and communicate their results to others via professional images and animations. The company’s products are used by more than 47,000 technical professionals around the world.

Margaret Connelly
Marketing Manager, Tecplot, Inc.
(425) 653-1200

About Convergent Science

Founded in 1997 in Madison, Wisconsin, Convergent Science is a global leader in computational fluid dynamics (CFD) software. Its customers include leading automotive and commercial vehicle manufacturers, tier one suppliers, and professional motorsport teams.

Its flagship product, CONVERGE, is an innovative CFD software with truly autonomous meshing capabilities that eliminate the grid generation bottleneck from the simulation process. CONVERGE is revolutionizing the CFD industry and shifting the paradigm toward predictive CFD.

Tiffany Cook
Media & Partner Relations, Convergent Science
(830) 625-5005


► Size Particles (Parcels) by Variable
  29 May, 2018

Sizing Particles (Parcels) by Variable.

In this video, you will see how to size particles (often referred to as parcels) by a variable in Tecplot 360.

Video Script

In this example, we are using a CONVERGE data set with parcel information. We have previously colored the parcels by values of the film flag.

Sizing the parcels by a variable such as mass or number of particles can be done quite easily.

In the Zone Style dialog, select the zone that represents our parcels, here titled Particle. Right-click on Scatter Size and click Select variable… and select the variable to size by, in this case we’ll use dp_mass. Then, right-click again and select By: dp_mass.

The particles do not change size in this case because the scatter Symbol Shape is set to Point. We want to pick a scatter Symbol Shape that will represent size, so we choose Sphere. Now you can see different sizes. Tecplot 360 calculates a default size for the parcels. If you want to adjust the size of the parcels, right-click on Scatter Size and click Select variable… again. Here we can adjust the size particles multiplier.

That is how you change scatter symbols as associated with a size variable.

Thanks for watching!

► Isolating Particles with Value Blanking
  10 May, 2018

Isolating Particles with Value Blanking

In this video, we will see how to use Value Blanking in Tecplot 360 to isolate parcels of a specific value based on their film flag.

Video Script

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.

Film Flag

Film Flag Integer Values

Let’s open the Value Blanking dialog. Select the film flag variable, dp_film_flag.

We will first look at parcels that are in the wall film only. To do this, we remove all the values that are not equal to one. One being parcels that are in the wall film.

Then we toggle on Active and Include value blanking.

Now we see only the parcels that are in the wall film.

Next, we want to look at only the rebounded particles, which have a film flag value of two. We can do this by simply changing the value in the value blanking dialog to ‘2’. 

If you want an easier way to do this, you can use the Quick Macro Panel.

Seen here in the Quick Macro Panel we simply double-click on Show specific parcels, and enter the value for the parcels we want to display. This quick macro has a nice prompt to allow you to specify what you want to see and will adjust the Value Blanking settings for you.

Let’s take a closer look at the quick macro command, Show specific parcels. You can view and edit macros in any plain text editor.

We use a macro command, called PromptForTextString. This allows you to be prompted to enter text values. Zero for parcels not in a wall film, one for in a wall film, and so on.

Macro Command PromptForTextString

$!MacroFunction NAME = "Show specific parcels"
ShowInMacroPanel = TRUE
$!PromptForTextString |film_flag_value|
INSTRUCTIONS = "Enter the parcel type to display:\n0 = Parcel is not in wall film\n1 = Parcel is in wall film\n2 = Rebounded parcels\n3 = Splashed parcels\n4 = Separated parcels\n5 = Stripped parcels"

Below this are the constraints that we’ve set up, which you saw earlier in the Value Blanking dialog.

$!Blanking Value{Constraint 1 {Include = Yes}}
$!Blanking Value{Constraint 1 {VarA = 'dp_film_flag'}}
$!Blanking Value{Constraint 1 {RelOp = NotEqualTo}}
$!Blanking Value{Constraint 1 {ValueCutoff = |film_flag_value|}}
$!Blanking Value{ValueBlankCellMode = PrimaryValue}
$!Blanking Value{Include = Yes}

And that is how you can easily isolate particles based on the film flag.

Thank you for watching!


► 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?

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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

Schnitger Corporation, CAE Market top

► Quick update on PTC + Rockwell
  19 Jun, 2018

I’m at PTC’s LiveWorx user conference this week. Yesterday, CEO Jim Heppelmann gave a bit more detail on the partnership with Rockwell Automation, announced last week. A quick recap: Rockwell is buying 8.4% of PTC’s shares for around $1 billion. PTC will use that money to buy back shares from other investors, to not dilute their investment in PTC.

Yesterday we learned that

  • PTC has factory customers, just not very many right now. Growing that part of the business with its existing sales force is challenging — Rockwell brings thousands of sales people and even more installed customers. PTC’s goal with this partnership is to enable those sales resources and define a joint go-to-market for ThingWorx
  • Rockwell’s automation solutions serve both discrete manufacturing and process industries. PTC doesn’t do much in process, so this gives it access to a completely new set of prospects
  • The deal does limit PTC’s ability to sign similar partnerships with Rockwell’s top competitors but that leaves lots of others to consider — but Mr. Heppelmann was clear: the Rockwell partnership gives PTC the access it wants and should keep it busy for the foreseeable future
  • The GE relationship is still in place — just not as active as GE’s interests shift under its new CE

Mr. Heppelmann said that PTC has typically gone it alone, which he sees as limiting. Partnering with Rockwell (and ANSYS and Microsoft, more to come on those) will enable PTC to better compete against vendors like Siemens, who offer IoT/PLM/CAx and factory automation.

Of course, it all comes down to execution. How well PTC and Rockwell align their strategies for specific customers, markets and geos — and then how well the individual sales teams sell products that may be outside their typical — will determine the success of the partnership. Mr. Heppelmann said that Rockwell has made revenue commitments but didn’t share those with us. For now, it seems that PTC is taking an all-in approach on the go-to-market but a very conservative “wait and see” for revenue.

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► Hexagon snaps up SPRING’s NCSIMUL for machining
  19 Jun, 2018

Hexagon just announced that it is acquiring SPRING Technologies, whose NCSIMUL optimizes the machining workflow from CNC programming to CNC simulation, managing cutting and tool libraries, real time machine monitoring and technical content publication.

Hexagon CEO Ola Rollén said of the acquisition,“[m]anufacturing must be ‘smart’ if it’s to produce the next generation of products at reduced costs. The acquisition of SPRING Technologies further strengthens our Autonomous Connected Ecosystem (ACE) strategy which will ultimately enable the smart factory. Machining simulation is essential to connecting the physical world with the digital and achieving autonomy – both of which are prerequisites to delivering smart factory solutions.”

SPRING will become part of Hexagon’s Manufacturing Intelligence, joining Vero, MSC Software and other brands. Hexagon didn’t disclose terms of the deal but expects it to close as soon as regulatory approvals are obtained.

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► HxGN LIVE merges old and new, urges epic leaps forward
  17 Jun, 2018

HxGN LIVE, the Hexagon group’s annual userfest, brings together thousands of people from manufacturing to fire/rescue/police to construction to mapping professionals. Walking the halls, it’s hard not to run into someone who does something I know nothing about — truly an interesting mix of technologists, end-users, industries and geographies.

Lots of stories to come, but the main news out of this year’s event was the launch of Xalt (pronounced “exalt”), a combo of current and future technologies that create what Hexagon calls its Autonomous Connected Ecosystem (ACE). Hexagon bristles when you call it a platform; they see it as a mechanism that delivers industry-specific solutions for Industrie 4.0. Depending on the end-industry, this may be a combo of  sensing technologies (via its own brands or third parties), software from its divisions, and data orchestration. While the name Xalt is new, bits and pieces have been in development for years, and some of it already has installed customers. CEO Ola Rollén says that Xalt has 50 to 60 customers, some operating  hundreds of NC workcells that embody what he believes Xalt will eventually bring to all of Hexagon’s end-industries: smarter, connected, digitally-enabled decision making that operates faster than humans can. His example: a Hexagon metrology device is embedded into a production line, measuring parts as they are produced. The metrology device has some analytical ability via edge computing, and can tell the line manager that a measured flaws means a tool is about to fail — replace it soon! This is faster than in today’s typical workflows, where metrology happens in a Quality Lab, offline, feeding back into production perhaps too late to be truly useful. Of course, Xalt will also feed data to a cloud analysis engine to enable queries such as “is this tool failing across all machines?” to help optimize production even further. That’s just one example — Hexagon CTO Claudio Simão says his team has identified over 500 applications for Xalt,  and that’s before Hexagon’s 8 divisions start adding in their specific applications.

And that, the domain expertise from the divisions, is what Hexagon believes will make Xalt different from everything else on the market. Mr. Rollén sees huge potential in the vast amount of data created every day by comms-enabled devices, but a huge lag in what is actually used. He sees Xalt as a framework that accelerates customers’ ability to harness that data simply, even autonomously, where that’s appropriate. Rather than being a toolkit, Xalt will be embedded in the hardware and software Hexagon sells, so that the customer decision becomes where and when to use it, and not so much how. The “how” will be enabled by the solutions from Hexagon Process, Power & Marine, Manufacturing Intelligence (as in the example above), Mining or or or… The experts in the divisions will leverage the core Xalt tech from Mr. Simão’s group, add to it as needed and serve it out to customers. Rather than being an IT/OT research project at each customer site, Xalt will jumpstart the realization of business benefit from all of that data. Action, rather than IT.

A couple of other quick thoughts –again, longer posts to come:

  • MSC Software, acquired by Hexagon a bit more than a year ago, shared their development priorities for the next year. UI improvements, adding more solvers to Apex, closer coordination with sister companies in the Manufacturing Intelligence division — all good ideas. What amazed (and heartened, to be honest) me was the level of energy. Hexagon is investing in MSC, boosting resources in R&D, support and sales.
  • And that’s true across brands and divisions. Hexagon told investors that it plans to spend 10% to 12% on R&D in 2018 and beyond. For a company that still gets over 40% of revenue from hardware, that’s a lot. And in real terms, it’s a lot, too: about €350 million in 2017.
  • There were product announcements across the divisions, more that I can cover here in this brief post. But if you’re a PP&M customer, look into HxGN SDx, SaaS solution that aims to help owners create and maintain a “digital twin” of an industrial facility and EPCs run projects. SDx is based on SmartPlant Foundations, so migration is simplified — but the end-result is modern, work-process and role-centric.
  • The least surprising thing I learned? I spent as much time as I could in PP&M sessions and it still seems as though oil & gas/chem/etc. is still stuck in patterns technology can’t fix. Why doesn’t design talk to purchasing? Why don’t owners pay EPCs for digital models? Why are contracts to adversarial? These issues haven’t changed in the many years I’ve been in the industry, and it’s truly a !@#$ shame. We keep talking about it but little changes.
  • The most interesting thing I learned? I tend to thing of  Hexagon as a holding company, with each division doing its own thing. That’s changing, as Mr. Simão’s team works with CTOs from across the divisions to reduce duplicative efforts — and, as a huge side-benefit, expose cool concepts from one industrial area that can benefit others. Hexagon has typically been slow to create cross-division offerings, but it sounds like we’ll see an acceleration there.

More to come. In the meantime, I suggest you check out Mr. Rollén’s keynote about epic leaps and the risks of being left behind — and hang in there because the baby bird makes it (yup, spoiler but necessary):

Note: Hexagon 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 Hexagon CEO Ola Rollén, courtesy of Hexagon.

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► Nemetschek NEVARIS addresses SMB construction with acquisition
  15 Jun, 2018

I am at HxGN Live, the Hexagon user event that addresses the needs of many industries, including construction. From measuring how well a concrete floor has been poured to planning, scheduling and tracking progress, Hexagon’s customers span the gamut in construction. We’ll cover all that is HxGN LIVE in a later post buttrust me:  construction IT is truly a hot topic right now. So it’s fitting that today, Nemetschek announced that its NEVARIS subsidiary has acquired GmbH for on-site construction management, such as time tracking, project management and construction site documentation.

Many people I spoke with here at HxGN LIVE need to do just those things as part of systemic efforts to improve efficiency, reduce risk, cut cost and/or time. Constructors need to improve their services delivery to asset owners and the owners themselves want more predictability across their project portfolio.

Mobile is a big part of this. Hexagon offers Smart Build (more on that to come);123erfasst does as well. The concept of an app connected to a data store that’s constantly updated isn’t new, but it is finally making it onto the job site. According to Nemetschek, the 123erfasst app records and transmits performance, location, materials, equipment usage, and photos to document in real time what is happening on a job site. This can simplify and speed up the costing, billing and payroll.

Patrik Heider of the Nemetschek Group said of the acquisition that “with t[his] software, construction projects can be planned, conducted and completed in 5D, i.e. including the dimensions time and cost. By acquiring, NEVARIS is further expanding its leading market position and becoming an indispensable part of the digital construction site.”

Most of the solutions on the market today either target the global construction companies, doing mega projects or very specific workflows. Neither is ideal for a project that’s a single school or small shopping arcade. NEVARIS says this acquisition will expand its customer base to the small and medium-sized construction companies that carry out these more modest (but far greater in number) projects. Daniel Csillag, CEO of NEVARIS, said that “the acquisition of is a milestone in the company’s history and an important step towards a digital construction site. Our focus is on creating a first-class, end-to-end solution for our customers. Mobile construction site management complements our market-leading technologies for construction management tasks perfectly and seamlessly. This is unique in the market “.

Terms of the deal were not disclosed, but it appears to be completed.

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► DS takes majority position in Centric Software
  14 Jun, 2018

Dassault Systèmes just announced that it will acquire a majority stake in Centric Software, the privately-held maker of PLM solutions for the fashion, apparel, luxury and retail sectors. DS said that, “with this investment, Dassault Systèmes aims to accelerate the digital transformation of companies seeking solutions for the increasingly complex development of collections that respond to today’s on-trend and on-demand consumers, representing a multi-billion dollar total addressable market”. Centric’s platform is used for planning, product specifications, materials management, sourcing, costing, quality, collection and calendar management — many areas that are fundamentally different from the PLM used in auto, aero and other discreet manufacturing industries.

DS also said that In 2017, Centric had revenue of $61 million, up over 60% on the prior year, and generated positive net income.

The deal is unusual: DS will acquire between 58% and 69% ownership of Centric, “depending on the Centric Software shareholders’ election” in exchange for cash and also give an advance payment for the remainder of the shares, which will change hands in 2020 and 2021. When all is said and done, and DS owns 100% of Centric, DS expects to pay between 4x and 6x 2019 and 2020 revenue, depending on growth and profitability.

More to come on this, too.

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► Quickie: Historic AVEVA surged in FY18
  14 Jun, 2018

AVEVA had an awesome fiscal 2018 (ended March 31). You’ll recall that the AVEVA merged with Schneider Electric Software earlier this year to create a much bigger company with software that reaches from plant design to operations. That combined group reported revenue £705 million, up a respectable 9% if compared to the theoretical merged businesses in fiscal 2017 as well. The historic AVEVA reported revenue of £248 million, up a strong 15% as reported and up 13.5% in constant currencies. Historic AVEVA benefitted from one mega deal that added 3% of that growth, to me an endorsement of both AVEVA’s technology and the forward-looking potential of the larger group. The historic Schneider Electric Software saw revenue growth of 5% to £456 million — but half of that growth was from a commercial agreement with Schneider Electric, so external customers contributed maybe 2.5% of growth.

In prepared remarks, CEO Craig Hayman said that his “initial focus has been ensuring we remain on track with the integration of the two businesses while spending time with customers, investors and employees around the world. I’ve established new leadership teams to drive the business including an Executive Leadership Team and Strategy Leadership Team, which work hand-in-hand with the regional sales teams and the business units … In the current year, we are focused on integrating the businesses whilst driving performance through improved execution. In the medium-term we expect to drive stronger growth assisted by the positive trend of the ongoing digitalisation of industry, an optimisation of our products and go-to-market strategies, and capitalising on the synergies outlined above.”

Clearly still a work in progress.

AVEVA doesn’t give guidance but all of the broker reports I’ve seen indicate growth of at least5% for the overall group, led by stronger historic AVEVA and slightly weaker historic Schneider Electric. AVEVA has/is hosting a web briefing on this today and will hold an investor event in September — both of those should start laying out strategies to capitalize on the trends Mr. Hayman laid out above.

Much more after I see the replay of today’s investor call.

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