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Another Fine Mesh top

► Pointwise is 2018 Fort Worth Small Business of the Year
  20 Feb, 2018
It it with both great humility and great pride in our team that I share with you the news that Pointwise was today recognized by the Fort Worth Chamber as 2018 Small Business of the Year in the 11-50 employees … Continue reading
► This Week in CFD
  16 Feb, 2018
This week’s roundup of CFD news includes a major announcement about the SU2 flow solver, a “save the date” notice for an upcoming CFD workshop, a very interesting job opportunity, and more.  Software SU2 v6.0 Falcon, the latest major release … Continue reading
► The Influence of Meshing Strategies on Simulation Efficiency
  14 Feb, 2018
Meshing strategies have a direct influence on the accuracy and efficiency of CFD simulations. Once a meshing decision is made, it affects not only the types, number, orientation, and placement of grid elements, but also simulation stability, convergence, and accuracy. … Continue reading
► This Week in CFD
    9 Feb, 2018
This week’s two-week backlog of news includes, perhaps appropriately, a ton of good reading. A lot of reading about meshes and some bigger issues like consolidated CAE toolsets versus best in class tool chains. Plus there’s a look to the … Continue reading
► Top Posts of 2017 on Another Fine Mesh
    2 Feb, 2018
It was Confucius who supposedly said “Study the past, if you would divine the future.” With that in mind, we looked back at 2017’s posts here on Another Fine Mesh to see which ones got the most views. With that … Continue reading
► I’m Melissa Berry and This Is How I Mesh
    1 Feb, 2018
Hi y’all! As part of the Business & Administrative Services team with Carrie Jefferies and Amy Harris, I find myself in a role that supports those that mesh.  I have great respect for engineers and anyone who works on these intricate … Continue reading

F*** Yeah Fluid Dynamics top

► Curling is a deceptively engrossing sport with some unique...
  23 Feb, 2018

Curling is a deceptively engrossing sport with some unique physics among Winter Olympic events. Athletes slide 19kg granite stones at a target 28 meters away. Along the way, teammates sweep the pebbled ice with brooms, melting it with frictional heating to help the stone slide further. The underside of the stones is concave, so they only touch the ice along a narrow ring. Researchers think roughness in the leading edge of the sliding stone cuts into the ice, leaving scratches that the trailing edge tries to follow. This is what causes the stone’s trajectory to curl. By melting the ice, sweeping also prevents curling, so competitors must know exactly when and how much to sweep. Ice conditions shift throughout a match, and the best players can read the ice to keep their stones where they want them. (Image credit: AP; W. Zhao/GettyImages)

► Cross-country skiing, also known as Nordic skiing, is a part of...
  22 Feb, 2018

Cross-country skiing, also known as Nordic skiing, is a part of many longstanding disciplines in the Winter Games. Unlike downhill skiing, cross-country events typically involve mass starts, which allow athletes to interact, using one another for pacing and tactics. Drafting can be a valuable method to save energy and reduce drag. A following skier sees a 25% drag reduction while drafting; the lead skier gets about a 3% reduction in drag compared to skiing solo. Competitors usually wear tight-fitting suits to minimize drag, but unlike speedskating, for example, cross-country skiers don’t get much benefit from roughened surfaces and impermeable fabrics. Typical race speeds are 4 - 9 m/s, and most of these high-tech fabrics don’t provide tangible benefits until higher speeds. (Image credit: Reuters/S. Karpukhin, US Biathlon, GettyImages/Q. Rooney)

► When it comes to winter sports, not all ice is created equal....
  21 Feb, 2018

When it comes to winter sports, not all ice is created equal. Every discipline has its own standards for the ideal temperature and density of ice, which makes venue construction and maintenance a special challenge. Figure skating, for example, requires softer ice to cushion athletes’ landings, whereas short-track speed skating values dense, smooth ice for racing. The Gangneung Ice Arena hosts both and can transition between them in under 3 hours. Gangneung Oval hosts long-track speed skating and makes its ice layer by layer, spraying hot, purified water onto the rink. This builds up a particularly dense and therefore smooth ice. 

The toughest sport in terms of ice conditions is curling, which requires a finely pebbled ice surface for the stones to slide on. Forming those tiny crystals on the ice sheet can only be done at precise temperature and humidity conditions. This is a particular challenge for Gangneung Curling Center due to its coastal location. To keep the temperature and humidity under control at full crowd capacity, officials even went so far as to replace all the lighting at the facility with LEDs! (Image credit: Pyeongchang 2018, 1, 2, 3)

► In bobsleigh, two- and four-person teams compete across four...
  20 Feb, 2018

In bobsleigh, two- and four-person teams compete across four runs down an ice track. The shortest cumulative time wins, and since typical runs are separated by hundredths of a second, teams look for any advantage that helps them shave time. The size, weight, and components of a sled are restricted by federation rules; for example, teams cannot use vortex generators to improve their aerodynamics. Instead bobsledders work with companies like BMW, McLaren, and Ferrari to engineer their sleds. Both computational fluid dynamics and wind tunnel tests with the actual team in the sled are used to make each sled as aerodynamic as possible. (Image credit: IOC, Gillette World Sports, source)

► These days artificial snow-making is a standard practice for ski...
  19 Feb, 2018

These days artificial snow-making is a standard practice for ski resorts, allowing them to jump-start the early part of the season. Snow guns continuously spray a mixture of cold water and particulates 5 or more meters in the air to generate artificial snow. The tiny droplet size helps the water freeze faster and the particles provide nucleation sites for snow crystals to form. As with natural snow, the shape and consistency of the snow depends on humidity and temperature conditions. Pyeongchang is generally cold and dry, so even the artificial snow there tends to be similar to snow in the Colorado Rockies. Recreational skiers tend to look down on artificial snow, but Olympic course designers actually prefer it. With artificial snow, they can control every aspect of an alpine course. For them, natural snowfall is a disruption that puts their design at risk. (Video credit: Reactions/American Chemical Society)

► Four years ago in Sochi, Under Armour’s suits for the U.S....
  16 Feb, 2018

Four years ago in Sochi, Under Armour’s suits for the U.S. speedskating team took a lot of flak after the team failed to medal. The company defended the physics and engineering of their suits, and an internal audit of the speedskating program ultimately placed blame on flaws in their training regimen, unfamiliarity with the new suits, and overconfidence.

This time around Under Armour has taken a more hands-on approach with the team, helping with training regimens in addition to providing suits. Under Armour spent hundreds of hours testing the suits in Specialized’s wind tunnel, including testing many fabrics before settling on the slightly rough H1 fabric used in patches on the skater’s arms and legs. Like the previous suit’s dimpled design, the roughness of the fabric promotes turbulent flow near it. Because turbulent flow follows curved contours better than laminar flow does, air stays attached to the athlete for longer, thereby reducing their drag. The suit is also designed with asymmetric seams that help the athlete stay low and comfortable in the sport’s frequent left turns.

U.S. speedskaters have been competing in a version of the suits since last winter, ensuring that athletes are familiar with the equipment this time around. Whether the new suits and training program will pay off remains to be seen. After their disastrous experience in Sochi, both the team and the company are shy about setting expectations. (Image credits: D. Maloney/Wired; US Speedskating)

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

► pressure eq. "converges" after few time steps
    1 Dec, 2017
Have a thread about simplefoam convergence.

Originally Posted by maddalena View Post
thank you, however:

corrected is extra, isn't it?

This will no improve the initial convergence, since the solver will stop when the relTol is 0.05. thus it will not run until 1E-12 but stop at 0.05. And pressure equation will be not converged smoothly, I guess. What are your experience on the subject? What do you mean with not efficient?

Somewhere else it has been suggested to use pressure tolerance 2 order of magnitude lower than velocity tolerance. Therefore, as I lower velocity tol, I must lower pressure tol as well. This was suggested as a "remedy" due to the higher difficulty on pressure eq to get convergence.

On the contrary of what reported above, fvSchemes attached on the last post used a linear upwind scheme...
Does it applies with tet mesh or with hea as well?

Hope to get it when published!

Therefore, my next steps are:
  1. use relTol 0.05 on p -> BTW, why not efficient?
  2. lower U tol
  3. use first order everywhere.
One more question: is this setup convergent, but not accurate?

► Controlling y+ values with snappyHexMesh?
  30 Nov, 2017
This can be useful y+ control !

Originally Posted by seaspray View Post

It might have got easier in the more recent versions. In a nutshell, you mesh your volume with a suitable algorithm (tets or hexa i,j,k) and viscous layers are added as an "additional hypothesis" in the 3D algorithm settings. In there, you have the option of excluding some faces in your geometry, like inlet, outlet, but also areas where you don't care as much about flow modeling like in some multiphase flow computations. Layers are not available with all meshing algorithms.

Otherwise you just set your 2D and 1D meshing parameters the normal way.

If you then submesh a face with different parameters (quadrangles for example) and this face has viscous layers, they should retain the structure of the face.

Salome can grow very nice viscous layers, but there is a known bug in the current version 7.3.0 that crashed the layer algorithm at the start for me. It is fixed in 7.4.0 due to come out any time now I was told.

Have a look, and I could put an example together for you if it helps.


► Free YouTube Lessons for Applied CFD in Aerodynamics using ANSYS and OpenFOAM
    9 Nov, 2017
I have decided to share in this Group a series of video Lessons I am making for a CFD course in Computational Aerodynamics at De Montfort University, Leicester, UK. The link for the play list is provided below: sjzLYtBS-

6 Lessons already exists, and with in the next few months (weekly) new videos will be uploaded.
It will consist of
* Geometry Handling Techniques
* Structured Blocked Meshing approaches In ICEM CFD
* Unstructured Meshing approaches in ICEM CFD
* Steady and Unsteady Simulations using ANSYS Fluent
* Introduction and Basic usage of OpenFOAM

Meshing will be carried out for simple airfoils, wings and also full scale aircrafts such as NASA CRM, DLR F6, F11, Fighter Configuration F16, General Transport Model and many more related geometries.

I hope this comes of use to the Fluid Dynamics Society. Even though it is specified course for Computational Aerodynamics it can be rapidly invoked in Meshing and CFD analysis of any other Geometries
► write a field in OpenFOAM
  30 Oct, 2017
This is a good thread on writing an object

Originally Posted by sven82 View Post

I will write out a variable of my own turb. model,
but its doesn't work.
First of all I define a ScalarField in my Header,

volScalarField hybrid_;

for the next step I edit the code file with a new Object

dimensionedScalar("hybrid", dimless, 0.0)

and follow of the definition in the memberfunction

hybrid_ = tanh ( pow( max( scalar(0) , ( I_ / X_ ) - 1/2 ) , 3) );

// function = tanh( ( max( 0 , l/x - 1/2 ) )³ )

the compile shows now errors and the case run fine for me,
except !!
the code cant write out the values of my defined variable (hybrid_).
When I add the line hybrid_.write() its works,
but for every timestep and not for the defined writecontrol!

Hope everyone got a idea !

► I want to use the 'turbulentIn
  25 Sep, 2017
BC need to pay atttention

Originally Posted by chris1980 View Post
I want to use the 'turbulentInlet' boundary condition. As far as I can see the use only inputs a fluctuation scale but what about the integral length scale or other appropiate scales because the flucation scale alone do not define a turublent inlet.

Ok I know it is anyway no real turbulent inlet because there is no turbulence strcuture etc.

Additionally, I was wondering what I have to set for k and epsilon at this turbulent boundary (using high Re k-epsilon turb model)?
► Utilities: post average turbulence fields and create turbulence fields for LES
  25 Sep, 2017
This seems to be useful

Originally Posted by Hanzo View Post
Hi everybody,

I found the following issues in OpenFOAM concerning some turbulence analysis:

- the utility createTurbulenceFields only works for RAS computations
- there is no standard utility to average fields after computation has been done (to get U-mean, U-rms, R-Mean, Reff-Mean ...)

So I worked on these utilities and after mentioning it in some posts I got replies and private messages asking me if I could publish them. So here they are:


Is a tools which writes out the fields k,epsilon, R, Reff after a RAS or LES simulation has been performed.


Inspired by eelcovv, who wrote the tool postAverage, I extended his tools to be also able to post average the fields R and Reff. Again RAS and LES are supported.
The post can be found here

How to use these utilities

Please find attached the modified tutorials

- pitz_daily_les
- cavity_ras

Including Allrun scripts which show how to use these tools.

How to install

- copy the contents from the archives "" and "" into the folder username-2.1.1/application/utilities

- go into each of the folders postAverageTurbulenceFields and createTurbulenceFieldsLES and type wmake

It also worked under OpenFOAM 1.6.

Known issues

- for some reason, which I did not figure out, averaging the Reynolds stress tensor for RAS computations does not work (but works for Reff)

- if the output setting for the averaging is set to certain values, nothing is written. However, it works for output intervals 1 and 2 (just delete the unnecessary files after averaging)

I am happy about any comments how to improve these tools or if there is a better way to post own code.


curiosityFluids top

► Rayleigh–Bénard Convection Using buoyantBoussinesqPimpleFoam
  13 Jun, 2017

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

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

Simulation Set-up

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

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

Geometry and Mesh

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



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

transportModel Newtonian;

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

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

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

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

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

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

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

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

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

And the turbulenceProperties file is:

simulationType laminar;

 RASModel laminar;

turbulence off;

printCoeffs off;

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

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

Boundary Conditions

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


dimensions [0 0 0 1 0 0 0];

internalField uniform 273;

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


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

internalField uniform 0;

type fixedFluxPressure;
rho rhok;
value uniform 0;

type fixedFluxPressure;
rho rhok;
value uniform 0;

type fixedFluxPressure;
rho rhok;
value uniform 0;


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

internalField uniform 0;

 type calculated;
 value $internalField;

 type calculated;
 value $internalField;

 type calculated;
 value $internalField;


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

internalField uniform (0 0 0);

 type noSlip;

 type noSlip;

 type noSlip;

Simulation Results

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

3D Temperature Contours


Temperature Field – Slice Through xy Plane



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

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


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

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


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

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

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

In the pointDisplacement file:

 type timeVaryingUniformFixedValue;
 fileName "prescribedMotion";
 outOfBounds clamp;

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

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

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

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

The circular motion was defined as:

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

Decaying sinusoidal motion was:

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

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


Circular Motion


Sinusoidal Decay



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


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

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

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

These ratios are given here:

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

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

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

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

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

Some excellent references for these equations are:

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


► Establishing Grid Convergence
    9 Sep, 2016

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

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

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

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

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

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

Velocity contour plots are shown in the following figures:

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

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

What are we examining?

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

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

Calculate the effective order of convergence

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


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

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

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

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

Perform Richardson extrapolation of the results

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


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

Using this equation we get the Richardson extrapolated results:

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

The results are plotted here:



Calculate the Grid Convergence Index (GCI)

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

The equation to compute grid convergence index is:

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

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

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

Minimum pressure

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

Max velocity

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

Check that we are in the asymptotic range of convergence

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

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

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

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

In our example we get:

Minimum Pressure

1.0276 \approxeq 1

Minimum Velocity

1.0154 \approxeq 1

Applying Richardson extrapolation to a range of data

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

This is shown here:

Conclusions and Additional References

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

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

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

► The Ahmed Body
    7 Sep, 2016

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

Image highlights:

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

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

The files for this case can be downloaded here:

Download Case Files

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

Geometry Definition

STL Creation

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

The ahmed body geometry can be found:

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

Preparation for Meshing

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

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

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

surfaceFeatureEdge volume.stl -angle 20

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

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

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

Meshing with cfMesh

Set up meshDict file in the system folder

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

Tell cfMesh what file is to be meshed:

surfaceFile "";

Set the default grid size:

maxCellSize 0.2;

Set up refinement zones:

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

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

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


Set up boundaries to be renamed:

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

Set up boundary layering:

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

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


Run cfMesh

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

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


Boundary Conditions for the Solver

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

The boundary conditions used are summarized in the following table:


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

Simulation Results

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


Streamlines and pressure on surface:


Vorticity surface in the near-wake



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

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

Some references:

For more information on the Ahmed body:

Some papers studying the Ahmed body:

-See the reference on the above CFD Online page!


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





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

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

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

Tutorial Files

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

Download Tutorial Files

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

Mesh Generation

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

Fig: Grid

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

Case Set-up


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

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

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

dynamicFvMesh dynamicMotionSolverFvMesh;

motionSolverLibs ( "" );

solver displacementLaplacian;

      diffusivity inverseDistance 1(cylinder);

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

Boundary Conditions

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

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

      type movingWallVelocity;
      value uniform (0 0 0);

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

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

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

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


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

Here is an animation of vorticity:

Wake of Oscillating Cylinder

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

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


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

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

Hanley Innovations top

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

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

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

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

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

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

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

Thanks for reading.

► Your In-House CFD Capability
  15 Feb, 2017

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

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

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

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

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

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

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

► Avoid Testing Pitfalls
  24 Jan, 2017

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

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

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

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

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

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

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

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

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

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

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

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

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

► Corvette C7 Aerodynamics
    7 Jan, 2017

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

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

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

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

The results for the Corvette C7 model  are summarized below:

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

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

Run time: 7 hours

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

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

Top View

Side View

Bottom View

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

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

Thanks for reading.

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

FlowViz - Fluid Dynamics top

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CFD and others... top

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

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

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

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

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

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

Happy 2018!     

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

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

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

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

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

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

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

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

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

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

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

A Tank outside my taxi

A beautiful night in Zurich

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

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

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

Computational Schlieren and iso-surfaces of Q-criterion

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

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

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

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

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

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

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

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

Good luck!

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

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

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

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

ANSYS Blog top

► The Alternative Energy Challenge – UT Austin Students Innovate with Simulation
  23 Feb, 2018

Since 2010, the Alternative Energy Challenge (AEC), a competition organized by the University of Texas at Austin, has allowed students to gain valuable hands-on experience building prototypes, and to develop both critical problem solving and public speaking skills. AEC 2018 is expected to be the biggest yet, with a large number of teams of 2-5 undergraduate students expected to build and present their prototypes.

Each team is tasked with designing and building an original prototype that mitigates or eliminates one or more risks and/or problems that can arise before, during, or after any kind of natural disaster. The device should fulfill the following requirements as much as possible:​

  • The waste product should be minimal. In other words, the materials that make up the device should be reusable wherever possible (recyclable, compostable, etc.).
  • The device should operate on a clean energy source. The device should not be powered by an energy source which contributes to the greenhouse effect. ​

This year’s competition has been organized by students from the UT engineering student groups Engineers for a Sustainable World (ESW) and IEEE Power & Energy Society.

The theme for AEC is to create solutions for disaster relief. In wake of recent events, such as the hurricanes that hit the US South/East coasts and the Californian wildfires, solutions in this area are in high demand. Our vision is that the final solutions will have the potential to positively impact the communities around us.

Simulation can greatly help the student teams virtually test their design before building them. Students will have access to ANSYS technology and training — not only will that be key for the competition, but it will also allow them to gain key skills for their careers. The students look forward to being able to use simulation to speed-up the design of innovative solutions for disaster reliefs as well as test a large amount of ideas virtually.

We are always looking for corporate partners and if you are interested in joining this great student competition, please visit us at and contact us at:

The post The Alternative Energy Challenge – UT Austin Students Innovate with Simulation appeared first on ANSYS.

► Embry-Riddle Students Crowdfunding to Shatter Land Speed Record
  22 Feb, 2018

Today it’s not uncommon to see electrics cars around everywhere. In fact, I imagine many of you readers might even have one. I wonder how many of you dared to push your car to 250 miles per hour (402.3 kilometers per hour). That’s exactly what our team, Eagle Works Advanced Vehicle Lab from Embry-Riddle in Prescott, Arizona plans to do.

Our goal is to shatter the record in the E-2 Class for electric land speed vehicles.

Land speed vehicles vary from the type of power used to the weight of the vehicle to the design of the body. Land speed vehicles can run on several types of fuel, solar power, electric power, or as a hybrid. Eagle Works envisioned building a Class E-2, 1100 lb electric vehicle.

We are building a Class II electric vehicle and will run it under The Southern California Timing Association (SCTA) regulations with the intent to set a class E-2 record at a speed of 250 MPH. The course will include a two miles to accelerate up to speed, three one-mile flying starts, and two miles to decelerate.


The Eagle Works Team is made up of more than 30 undergraduate students at the ERAU Prescott campus. Most Team members are Aerospace or Mechanical Engineers but the team is open to, and has a number of students from other majors. At this time all of the team members are undergraduate students.

Our team currently uses ANSYS Fluent, Maxwell, HFSS, Discovery Live, and Mechanical for our simulations.

  • Our Aerodynamics Division uses ANSYS Fluent to optimize the aero-shell design. More specifically we are testing different nose and tail profiles in order to reduce drag and increase stability.
  • The Motors and Motor Controllers Team is deep into using Maxwell and HFSS trying to understand the intricacies associated with magnetic fields as well as electronic signal noise created by the two powerful motors up front.
  • Other team members use ANSYS Mechanical and Discovery Live to test the structural integrity of the frame and external components in order to verify that the worst case scenarios yield values within our factor of safety.

Crowdfunding is crucial to the success of this project.

The team is currently renovating and updating the shop to be capable of producing a land speed car that will baffle more experienced engineers and give us the experience and success to boost our careers. Through a crowdfunding project we hope to raise the funds needed to purchase large and indispensable items such as a used Bridgeport milling machine, new air compressor, and more steel to complete the frame. The funding will also be used for smaller items such as tools and safety equipment in the form of welding masks, gloves, respirators, etc.

You can find more information on our team page, as well as this video of our progress so far.

If electric car technology is a passion or if you just want to help a motivated student team, we would be honored if you can help us reach our crowdfunding goals. Our crowdfunding link is:

Thanks for your support!


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► Simulating Eddy Current Testing of Honeycomb Aircraft Panels with ANSYS Discovery AIM
  22 Feb, 2018

Wing of an airplane flying above the sunrise cloudy sky at morningWithin the aerospace and transportation industries, it is always desirable to minimize weight without sacrificing strength. One such method of achieving this in aircraft wings and other aircraft structures is to use honeycomb paneling. Such structures are susceptible to damage from things like hail or tool drop, however, and the damage may not be noticeable from the surface. The face sheet may rebound, leaving crushing to the honeycomb core hidden beneath the surface.

Accurately determining impact damage in aluminum honeycomb aircraft panels is critical to evaluating the remaining lifetime of these structures. The hidden crushed core decreases the residual strength of the panels, so it’s important to detect it. Based on using a dial depth gauge for measuring dents, determining the impact damage in honeycomb aircraft panels can be laborious and subjective. If the impact damage also includes crushing of the internal honeycomb core, a manual tap tester is often used, which is both manual and subjective.

Eddy current testing (ECT) thus represents a promising, semi-automated alternative, which is already a popular non-destructive evaluation (NDE) technique used to inspect cracks in aerospace components. Scanning over dented aluminum honeycomb core covered by a face sheet ultimately revealed that the sizes and locations of crushed core could be detected by scanning with an eddy current array. However, verifying these findings presented some challenges, and engineering simulation helped visualize the eddy current array.

ANSYS Discovery AIM was used to simulate the eddy current testing and frequency response of a coil array hovering above an aluminum honeycomb panel, and ultimately helped verify the experimental electromagnetic findings.

ANSYS Discovery AIM Eddy Current Honeycomb Panel Geometry

Coil and Honeycomb Panel Geometry

Eddy current array (ECA) systems are complex. In reflection-type probes, the driver coils produce eddy currents, while the pickup coils experience essentially no current flow. The current in the core of a honeycomb panel should then mirror only the driver coils. This setup was investigated in the AIM simulation, which modeled two driver coils and two pickup coils, each made of copper with ferrite cores. The status of a coil as either driver or pickup can change during an evaluation, as the coils are multiplexed; however, modeling the coils as shown provides meaningful insight regarding the behavior of currents generated within a honeycomb panel.

Current Densities in the Honeycomb Core beneath the Face Sheet and Coils

Looking at the honeycomb core, hidden beneath the overlying face sheet, the AIM simulation shows the strongest current densities to be around the perimeter of the two driver coils, as the currents can only flow through the cell walls of the core. This is expected, illustrating how current is diverted away from underneath the coils. It also shows the current direction in the honeycomb core, the current densities generated in the face sheet, and the magnetic field intensity generated by the driver coils within the entire system.

Current Directions in the Honeycomb Core showing Current Flowing
around Perimeter of the Driver Coils

Current Densities in the Face Sheet beneath the Driver Coils

Magnetic Field Intensity, H within the System generated by the Driver Coils

Simulation provides valuable insight on how eddy current testing and measurements in honeycomb panels may vary from typical crack-detecting inspections, where eddy currents flow through essentially continuous medium.

The simulation results showing the behaviors of eddy currents generated in complex geometries are important for designing eddy current probes specifically tailored to complex geometries, as might be the case when designing probes to inspect honeycomb structures, or probes to inspect other complex geometries such as aircraft wings or steam generators. These simulation results are also essential when validating experimental electromagnetic data.

As a result, as companies continue to develop advanced NDE technologies such as eddy current testing for industries such as aerospace and petroleum, they may do so using the power of next-generation pervasive engineering simulation.

ansys 18 webinarTo learn more about the electromagnetic, as well as fluid, structural, and thermal simulation capabilities in ANSYS Discovery AIM, we invite you to register for an upcoming webinar showcasing some changes to AIM in ANSYS 19.

Have you tried Discovery AIM yet? Give it a test drive for 15 days free of charge.

The post Simulating Eddy Current Testing of Honeycomb Aircraft Panels with ANSYS Discovery AIM appeared first on ANSYS.

► Thermo-Mechanical Analysis Methods for Printed Circuit Boards: Part 2
  20 Feb, 2018

In part one of this series, we discussed modeling approaches for the complex geometry found in printed circuit boards. Now, we’ll move on to discussing methods for characterizing the thermal properties of integrated circuit (IC) packages.

Analysis of IC packages is critical at many levels of the design process, including package level thermal design, board level modeling including heat sink designs and package viability, as well as system level flow and thermal characterization. Much like a PCB, IC packages are geometrically complex, with disparate length scales that are challenging to explicitly capture in an analysis.

Typical IC Packages 

Among our primary interest when performing a thermal analysis of a system that includes an IC package is determining the junction temperature to ensure that our designs operate at temperatures that will not cause damage to the system. To do so, our models must capture the various heat pathways through a package, including conduction from the die through the internal layers; convection and radiation from the surface; and heat transfer to the board on which the package sits. So, with that goal in mind, how can we efficiently model the thermal characteristics of an IC package? I’ve outlined 5 methods of varying complexity and accuracy below:

Method 1: 2D Source

The most basic approach to modeling an IC package is to apply the heat dissipated by the package as a two-dimensional source on the surface of the board. This approach will provide no explicit information about the die temperature, and therefore requires the analyst to back out the junction temperature based on the predicted local board temperature. This is among the least accurate of all modeling choices, but also requires the smallest computational expense. In large system level models, this approach can be used with success to understand global heat transfer rates and characteristics.

Method 2: Lumped Material Properties

A second simplified approach is to represent the package in 3D using its geometric bounding box while assigning an effective, uniform set of thermal properties. Since most IC packages are comprised of a common set of materials, the analyst can estimate their volumetric proportions to compute effective thermal conductivities and densities, similar to the methodology for PCB’s presented in the first of this blog series (LINK). As with method 1, the lumped material properties method is limited in its accuracy, but may be useful for large system level models in which the obstruction of flow due to components is important.

Method 3: Thermal Resistor Networks

For analyses aimed at predicting the junction temperature of components, the heat transfer pathways within the package must be modeled in some way. As shown in below one approach for doing so is to construct a network of thermal resistors from the die to the exterior of the package, including conduction to the board and convection to the ambient surroundings. These thermal networks can be constructed in great complexity where necessary to capture the intricate pathways within a package. In general, however, most analysts rely on simple two resistor networks that account for the thermal resistance from the junction to case, and junction to board. Regardless of their complexity, network models have the great advantage of being much more computationally efficient than other methods of capturing the heat transfer pathways within an IC package as the mesh requirements are minimal.

Generic Chip Thermal Resistor Network (left),
Simplified 2 Resistor Thermal Network (right).

Many chip suppliers will provide thermal resistance values for their components on their respective data sheets, and some may even provide more complex DELPHI multi-resistor network models. For suppliers that do not publish thermal resistance values, ANSYS Icepak has a macro that can be used to predict these thermal resistances using the detailed package geometry and the JEDEC test standard. My experience has found that supplier provided values tend to be conservative, and that the junction to board values in general are sensitive to the local board properties. However, simplified thermal resistor networks provide an efficient and accurate way of characterizing thermal performance for both system and board level models.

Method 4: Compact Conduction Models

A second approach to characterizing the thermal pathways in an IC package is to simplify the major components (i.e. a solder ball array) into a block with orthotropic conductivity — major pathways modeled, uses simplified geometry. This approach requires knowledge of the package geometric details, but can result in accurate prediction of the package behavior it the simplified model is validated against a detailed representation of the geometry.

Detailed IC Package Cross-section (left) 
Compact Conduction Model Simplification (right)

As with the thermal network approach, ANSYS Icepak offers a macro that can be used to generate a compact conduction model from a detail geometric representation of a package. Since this methodology models the simplified package layers explicitly, the mesh requirements are more cumbersome than the thermal network approach. As such, this methodology is most useful for board level analyses where the demands of increased accuracy warrant the additional computational expense.

Method 5: Detailed Package Models

The most accurate approach to package modeling, as well as the most computationally expensive, is to model the 3D package geometry explicitly. This method allows the analysts to directly capture the heat transfer pathways from the die to the board and ambient. Detailed package geometry can typically be imported from ECAD sources, or developed by the analyst themselves (Note: ANSYS Icepak has macros to build typical package geometries!).

Representative QFP Geometry (left) and Mesh (right).

Due to the geometric complexity, a single IC package may require a mesh of greater than one million cells! As such, this level of detail is typically only practical for package level thermal design analyses.

In our next section of this blog series we will discuss vibration analysis methods for PCB’s — stay tuned!

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► Meet the ANSYS Embedded Systems and Software Team at Embedded World
  16 Feb, 2018

Embedded World 2018 is just around the corner and we’re excited. Embedded World brings together over 30,000 embedded systems and software professionals focusing on new technologies in embedded systems and software, and I’m pleased to let you know that ANSYS will be there again this year in booth 4-631, located in Hall 4.

We talk about product complexity and how the product development process is changing quite a bit here on the blog and the same holds true for the embedded sector. I’m sure that you hear daily about disruptive technologies like autonomous vehicles and digital twins.Did you know that embedded systems and software are what will control these devices?

As we move towards autonomous vehicles, digital twins and other advanced technologies, ensuring the safety and reliability of these products remains paramount.  This not an easy task. In order to make sure that your products and the software that controls them are behaving in the correct manner, there are many things that you must consider. Among there are interactions with the 3-D physics properties of the actual product, systems design, functional safety of the software and the actual code itself that is controlling the device.

As you may know, we’ve recently released ANSYS 19, and there is no better place to see the enhancements and new product features available to you than in our booth at the show. So, what can you learn if you swing by and pay us a visit?

We’ll be demonstrating a number of exciting new demos on application areas and product features that I’m sure you will find interesting. They show the importance of embedded systems and software safety in traditional applications and across disruptive technologies like autonomous vehicles.

Embedded World 2018 Preview

Stop by and see:

  • The ANSYS ADAS/autonomous vehicle open simulation platform — The ANSYS ADAS/autonomous vehicle open simulation platform integrates physics, electronics, embedded systems and software simulation to enable companies developing self-driving vehicles to accurately simulate complete autonomous driving systems. 
  • Electrothermal and Structural Reliability Analysis — learn how the unique ANSYS Chip-Package-System design flow maximizes reliability from structural and thermal effects such as thermal expansion and hot spots from electronic components.
  • Empowering the Internet of Things — see how ANSYS enables the design of reliable IoT devices meeting the high-speed, low-power stringent requirements. Demo of a virtual TDR wizard and RLC extractions of key components.
  • TDR — demo of the virtual TDR functionality. See how to set up the virtual TDR probes, do the circuit analysis and do effective design optimization.
  • SCADE HMI demo — Learn how SCADE supports Khronos OpenGL SC 2.0 for critical avionics and automotive HMI software.
  • ANSYS and KRONO-SAFE integrated software platform for critical aerospace applications — ANSYS and KRONO-SAFE integrate their respective tools SCADE Suite and ASTERIOS to provide a real-time integration flow suitable for safety-critical multirate applications, on single or multicore platforms.
  • Complete Battery Management System for Automotive — As vehicle manufactures move towards increased or total electrification, understanding how the complete system, including functional safety and embedded software, interacts is the only way to ensure reliability and safety.
  • Also, our partner CoreAVI will join us in the booth and will demonstrate our joint capabilities on a glass cockpit.

If you want to delve a little deeper into some of the hot topics facing the embedded systems and software industry right now, join my colleague, Gunther Siegel, for his presentation, “Analysis and Development of Safety-Critical Software for ADAS,” on Wednesday, Feb. 28, 2018, at noon, Hall 3/Stand 3A-610.

We hope that you can visit with us while in Nuremberg, but if you won’t be at the show, you can still learn more about how we’re making developing autonomous vehicles, digital twins and other applications along with the control code and HMIs easier and more efficient.

If you aren’t going to be at Embedded World, take a look at our white paper, Driving Speed and Reliability in Automotive Systems Engineering: The Need for a Model-Based Solution, to learn more about how we are speeding development of the systems and software that control autonomous vehicles.

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► ANSYS 19 Delivers Accurate CFD for Very Large and Very Small Details
  15 Feb, 2018

ANSYS Fluent can simulate really big models (1)Engineering problems can be quite straight forward when confined to a single size scale. For example, designing an elephant-proof fence is simply an exercise welding together enough big steel bars. But what if it also has to confine mice? By mixing the very large and very small size scales, the mouse introduces a whole new set of problems that will greatly complicate the design and construction of the fence. Tiny gaps irrelevant to the elephant can be escape ways for the mouse!

ANSYS Fluent can simulate really small models (1)

Fuel Pumps Present Big Problems With Meshing Tiny Gaps Between Sliding Bodies

Automotive engineers are having similar problems as they try to reduce emissions by minimizing leakage of engine oil into fuel through fuel pumps. The high operating pressures in modern designs can deform materials to widen the gaps between the plunger and cylinder walls and allow unexpected oil leakages.

Fluent in ANSYS 19.0 accurately simulates fluid flow within small gaps between the plunger and cyninder wallsHigh pressures in fuel injectors cause plunger and cylinder walls to deform. ANSYS Fluent can now accurately simulate fluid flow within these very small gaps.

Until now, this type of fluid-structure interaction case, where a thin fluid film is separating two deforming solids sliding past each other, meshing tiny gaps has been a challenge to model. The sliding motion would tend to skew the fluid elements in the gap, requiring a fine tetrahedral mesh in the gap and frequent remeshing to prevent mesh folding. New technology in ANSYS Fluent automatically ensures that a high quality mesh is maintained in the deforming gaps as identified by coupling with ANSYS Mechanical. The resulting simulations provide highly accurate oil leakage flow rates that account for the structural deformations and can be used to optimize pump performance.

ANSYS 19.0 Ensures High Quality Mesh Dynamically Fills Tiny Gaps for Accurate Simulations

In ANSYS 19.0, we introduced an engineer-ready workflow that includes two new features that together address this need:

  1. System coupling loads can now be transferred at a sliding mesh interface and,
  2. Nodes on one side of an interface can be projected onto the other side of the interface.

In the case of a fuel injector, the motion/deformations of the plunger would be sent from Mechanical to a copy of the plunger in Fluent. 

animation of meshing tiny gaps using ansys fluentANSYS Fluent dynamically projects nodes across the interface to ensure high quality mesh.

This copy of the plunger would be interfaced with the fluid, and the fluid nodes would be projected normally onto the plunger during the simulation, such that they only inherit the normal displacements and not the large axial displacements. As a result, the fluid elements would not be sheared while the interface connection maintained.

The node projection feature is not restricted to system coupling simulations, and likely has many applications beyond what I’ve listed. I’m excited to see how our customers make use of it to tackle meshing tiny gaps in challenging FSI simulations and better their products.

 Want to learn more?  

ansys webinarsJoin us for the webinar: Fluids Innovations in ANSYS 19.0 where we review usability enhancements, speedups and new capabilities in each ANSYS CFD product including Fluent, CFX, Polyflow, FENSAP-ICE and Chemkin-Pro.

The post ANSYS 19 Delivers Accurate CFD for Very Large and Very Small Details appeared first on ANSYS.

Convergent Science Blog top

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

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

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

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

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

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

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

Gas Turbine Relight with GE and Honeywell

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

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

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

CONVERGE-ing in Europe

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

Motor City Hosts Fourth Annual U.S. User Conference

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

IDAJ’s Continued Success in Asia

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

Assorted Consortia

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

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

Convergent Science: India

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

Convergent Science Turns 20

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

2018 and Beyond

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

► Convergent Science India LLP
  29 Nov, 2017

Same Face, New Name

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

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

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

Opportunity Abounds in India

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

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

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

Ashish outside new Convergent Science India office location.

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

Beyond India

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

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

The Real Motivation

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

Renovation of the Convergent Science India office space.

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

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

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


Ashish Joshi

Principal Engineer & Manager

Indian Operations | LinkedIn

► Designing Wind Farms with CONVERGE
  21 Nov, 2017

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

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

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

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

Figure 1

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

Figure 2
Figure 3

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

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

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





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

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

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





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

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

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

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

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

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

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

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

CONVERGE simulation of von Karman vortex shedding from a cylinder.

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

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

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

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

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

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

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

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

Figure 1: The aircraft cabin geometry.

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

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

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

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

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

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

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

Figure 3: Animation of the flow field.

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

► A Bidirectional Spray Modeling Approach: Eulerian-Lagrangian Spray Atomization in CONVERGE 2.4
  31 Aug, 2017

In CONVERGE, the standard technique for modeling sprays (such as for liquid fuel injection) employs an Eulerian approach for the continuous fluid domain and a Lagrangian approach for the spray parcels.

With the Eulerian approach, CONVERGE treats the fluid as a continuum while with the Lagrangian approach, CONVERGE tracks the discrete spray parcels. Lagrangian particle tracking is computationally efficient because it does not require as fine of a mesh to represent spray physics as required for an Eulerian approach.

One application of spray modeling is to simulate liquid fuel injection such as in an injector at the Engine Combustion Network (ECN) Spray A condition. Engineers are interested in capturing the dynamics of the nozzle fluid flow, which can be quite computationally expensive.

In CONVERGE 2.4, one computational method is VOF-spray one-way coupling. This method consists of two steps: a VOF simulation to model the liquid fuel in the nozzle, and a spray simulation in which the spray is initialized with information at the nozzle exit from the VOF simulation. While this method is appropriate in some situations, it does not capture the effect of ambient flow conditions and droplet motion in the exit chamber on the fluid flow in the nozzle.

Figure 1: Injector and exit chamber
Figure 2: Close-up of injector

Enter ELSA. The Eulerian-Lagrangian Spray Atomization model in CONVERGE 2.4 provides high accuracy by accounting for the downstream effects of ambient conditions and droplet motion via bidirectional coupling.

The method is this: CONVERGE leverages the previously-proven volume of fluid (VOF) model to accurately represent the liquid fuel dynamics in the sac and nozzle. ELSA tracks the liquid in the exit chamber and, when dilute enough, transitions the Eulerian spray to Lagrangian parcels.

As with any Lagrangian spray simulation, you can apply CONVERGE’s physical models for collision, break-up, and evaporation.

Figure 3: Injector mesh

Consider an injector at the ECN Spray A condition. The geometry (shown in Figures 1 and 2) consists of an injector and exit chamber with fuel and ambient conditions as described in the ECN database.

Figure 3 shows the mesh around the nozzle-exit chamber interface. The smallest cell width is 10 microns. Finally, Figures 4 and 5 show the liquid penetration and spray shape for the injection. By capturing the physics of the injection process, the ELSA model produces good agreement with experimental data.

When you need high fidelity resolution of spray physics, employ the ELSA model in CONVERGE 2.4.

Figure 4: Liquid penetration compared between simulation and experiment
Figure 5: Spray shape

Numerical Simulations using FLOW-3D top

► ALL NEW FLOW-3D CAST v5 Released
  23 Feb, 2018

SANTA FE, NM, February 23, 2018 — Flow Science, Inc. has announced a major release of their metal casting simulation software, FLOW-3D CAST version 5.0. FLOW-3D CAST’s new tools for predicting casting defects provide insights that shorten design cycles and reduce cost. Featured developments include thermal modulus and hot spot identification output for solidification simulations, filling defect tools for identifying trapped gases and predicting venting efficiency, and faster and more robust pressure and stress solvers.

The ALL NEW FLOW-3D CAST v5 comes in Suites, including Permanent Mold, High Pressure Die Casting, Low Pressure Sand Casting and Lost Foam. Within each Suite, FLOW-3D CAST’s process-based workspaces substantially simplify simulation setup by allowing metal casters to choose the process they want to simulate. The software will then provide the appropriate process parameters, geometry types, and industry defaults.

The release of FLOW-3D CAST v5 puts the power of the industry’s most accurate casting simulation tool into the hands of everyday users. Our new defect prediction tools, coupled with a more robust simulation engine, a redesigned What-You-See-Is-What-You-Need (WYSIWYN) interface, and porosity analysis tool, make simulation a reality for every casting project, said John Ditter, VP of Software Engineering.

A live webinar will present the new models, features and design workflows of FLOW-3D CAST v5. The webinar will take place on March 14 at 1:00 pm EST. Register online >

Go here for an extensive description of the v5 release improvements >

The post ALL NEW FLOW-3D CAST v5 Released appeared first on FLOW-3D.

► 2018 Internship Opportunities
  27 Jan, 2018

Flow Science is a small, diverse software company with a global presence and a distinguished history. Originally an offshoot from Los Alamos National Lab, the company is now based in Santa Fe. We are offering internships in our technical sales group to both identify future hires and to create opportunities for area students to know there is a place for them in New Mexico.

Our suite of products, under the trade name FLOW-3D, is computational fluid dynamics (CFD) software used by engineers, designers, and scientists at top manufacturers and institutions throughout the world to simulate and optimize product or project designs and manufacturing processes. Many of the products used in our daily lives, from automobile components to the paper towels we use to dry our hands, have actually been designed or improved through the use of FLOW-3D.

Ideal Candidates

Undergraduate or graduate students in Metallurgy or Foundry Technology, or graduate students in Civil, Mechanical, Aerospace, or Chemical Engineering, graduated in December 2017, or graduating in May or December 2018. Candidates should have exceptional oral and written communication skills, exceptional presentation skills, excellent interpersonal skills, and the ability to work both independently and as part of a team.

Interns will focus on one of these three areas:

  • Metal Casting Track
    Recent or imminent metallurgy/metal casting/foundry technology degree (Bachelors or Masters), prior foundry experience very welcome. Strong 3D skills required.
  • Water Civil Infrastructure Track
    Recent or imminent master’s in civil engineering with hydraulics/water civil infrastructure emphasis, physical oceanography, maritime or coastal engineering. Prior modeling experience (1D, 2D methods acceptable, 3D/CFD experience highly desired). Strong 3D CAD skills and GIS skills also highly desired.
  • Mechanical, Aerospace, Chemical Engineering/Microfluidics Track
    Recent or imminent master’s, very strong understanding of fluids mechanics, thermal fluids and computational methods. Some prior use of commercial CFD tools required. Strong 3D CAD modeling skills required.

Interns will contribute and learn by:

  • Developing advanced CFD modeling skills with the CFD tool FLOW-3D
  • Developing an understanding of most of the available physics models available to FLOW-3D users
  • Developing an in-depth understanding of CFD methods applied to industry-relevant problems
  • Developing an in-depth understanding of the physics and processes associated with industry-relevant problems
  • Developing a broad overview of key market segments relevant to free surface CFD applications
  • Interacting on a daily basis with the solver, GUI and marketing teams at Flow Science
  • Having an opportunity to fine tune their presentation skills, through internal presentations of their work as well as possibly live presentations to potential prospects


Resumes should be submitted to Applicants must be available to attend an onsite interview in Santa Fe, New Mexico, at their own expense.

The post 2018 Internship Opportunities appeared first on FLOW-3D.

► FLOW-3D Workshops: Water Civil Infrastructure
  23 Jan, 2018
FLOW-3D CFD Workshop Water Civil Infrastructure

Curious about FLOW-3D? Want to learn about how we go about modeling the most challenging free surface hydraulic applications? Interested in digging deeper and consolidating your gains?

Our workshops are designed to deliver focused, hands-on, yet wide-ranging instruction that will leave you with a thorough understanding of how FLOW-3D is used in key water infrastructure industries. In the morning, you will explore using hands-on examples, the hydraulics of typical dam and weir cases, municipal conveyance and wastewater problems, and river and environmental applications. In the afternoon, you will be introduced to more sophisticated physics models, including air entrainment, sediment and scour, thermal plumes and density flows and particle dynamics. By the end of the day, you will have set up six models, absorbed the user interface and steps that are common to three classes of hydraulic problems, and used the advanced post-processing tool FlowSight to analyze the results of your simulations. This one-day class is comprehensive yet accessible for engineers new to CFD methods. There is no need for an advanced degree in numerical methods; practicing engineers in the water industry are the best audience for the materials presented.

All workshop registrants* will receive a 30-day license of FLOW-3D (a $2,000 value).

Register Online

  • American Express

Cancellation policy: For a full refund of the registration fee, attendees must cancel their registration by 5:00 pm MST one week prior to the date of the workshop. After that date, no refunds will be made.

2018 FLOW-3D Workshops

  • March 7 – New York, NY – Hazen and Sawyer
  • March 8 – Philadelphia, PA – O’Brien & Gere
  • March 9 – Charlotte, NC – HDR
  • March 21 – Cincinnati, OH – Cincinnati Metro Sewer District
  • March 22 – Baltimore, MD – WSP
  • March 29 – Houston, TX  – Freese and Nichols
  • April 13 – Atlanta, GA – FERC
  • April 17 – Portland, ME – GEI
  • April 18 – Holden, MA – Alden Lab
  • April 26 – Denver, CO – Knight Piesold
  • May 23 – Toronto, ON – Parsons
  • May 25 – Montreal, Canada – AECOM
  • May 29 – Vancouver, Canada – Golder
  • May 30 – Seattle, WA – Northwest Hydraulics
  • June 20 – San Diego, CA – moffat & nichol
  • June 21 – Sacramento, CA – Wood Rodgers

Workshop Details

  • Registration is limited to 12 attendees
  • Cost: $499
  • 9:00 am – 4:00 pm
  • Lunch provided by Flow Science
  • Bring your own laptop (and mouse!) to follow along or just watch

*This offer only applies to prospective or lapsed customers.

Interested in hosting a workshop at your company? Request a CFD workshop >

About the Instructor

John Wendelbo, Director of Sales

John Wendelbo, Director of Sales, focuses on modeling challenging water and environmental problems. John graduated from Imperial College with an MEng in Aeronautics, and from Southampton University with an MSc in Maritime Engineering Science. John joined Flow Science in 2013.

About our workshops

Our workshops provide attendees with a valuable opportunity to learn about FLOW-3D and its powerful multiphysics modeling capabilities. These workshops are designed to cover the fundamentals of specific modeling simulations, provide hands-on learning, and allow attendees to test drive the software by building a model from scratch. Additionally, each participant receives a free 30-day license and access to tutorial videos and practice examples.

FLOW-3D Workshop
A very successful FLOW-3D workshop for water and environmental applications in Bangkok, organized by our Thai partner DTA and hosted generously by King Mongkut's University of Technology Thonburi (KMUTT). Special thanks to Prof. Chaiyuth Chinnarasri.

You have completed the one-day workshop, now what?

We recognize all will not be absorbed in one day, and you may want to use FLOW-3D on one of your own problems or compare CFD results with prior measurements in the field or in the lab. After the workshop, your license will be extended for another month to use on your workstation. During this time you will have access to our technical staff in order to help you work through your specifics: we are here to help you at every step.

Who should attend?

  • Practicing engineers working in the water resources, environmental, energy and civil engineering industries
  • Regulators and decision makers looking to better understand what state-of-the-art tools are available to the modeling community
  • All modelers working in the field of environmental hydraulics

Participants will learn

  • How to import geometry and set up free surface hydraulic models, including meshing and initial and boundary conditions.
  • How to add complexity by including sediment transport and scour, particles, scalars and turbulence.
  • How to use sophisticated visualization tools such as FlowSight to effectively analyze and convey simulation results.
  • Advanced topics, including air entrainment and bulking phenomena, shallow water and hybrid 3D/shallow water modeling, and chemistry.

Past Workshops

February 9, 2018
Hazen and Sawyer
Raleigh, NC

November 29, 2017
3901 Calverton Blvd, Suite 400
Calverton, MD 20705

November 28, 2017
Schnabel Engineering
1380 Wilmington Pike, Suite 100
West Chester, PA 19382

October 20, 2017
1670 Broadway Suite 3400
Denver, CO 80202

October 12, 2017
Wade Trim
25251 Northline Road
Detroit, MI 48180

October 10, 2017
1600 Blvd. Rene-Levesque West, 16th Floor
Montreal, QC H3H 1P9

October 9, 2017
2nd Floor, 1 Pennsylvania Plaza

New York, NY 10119

October 5, 2017
Michael Baker
100 Airside Drive, Airside Business Park
Moon Township (Pittsburgh), PA 15108

October 6, 2017
226 Causeway St
Boston, MA 02114

September 8, 2017
Freese and Nichols
10431 Morado Circle, Suite 300
Austin, TX 78759

September 7, 2017
Freese and Nichols
2711 North Haskell Ave. Suite 3300
Dallas, TX 75204

September 6, 2017
Tetra Tech
1899 Powers Ferry Rd SE #400

Atlanta, GA 30339

June 22, 2017
Brown & Caldwell
701 Pike Street, Suite 1200
Seattle WA 98101

The post FLOW-3D Workshops: Water Civil Infrastructure appeared first on FLOW-3D.

► FLOW-3D Water and Environmental Training Webinars
  22 Jan, 2018

In these FLOW-3D water and environmental training webinars, we will be reviewing the setup for various types of applications commonly used in the water and environmental field. These monthly webinars will cover a range of basic and advanced topics that will be of interest to both new and experienced FLOW-3D users. The webinar objectives will be to:

  • Discuss relevant and interesting uses of FLOW-3D
  • Review basic steps for setting up and running a variety of problems in FLOW-3D
  • Introduce advanced modeling techniques
  • Evaluate various setup options in FLOW-3D and how they affect simulation results
FLOW-3D water webinar series

FLOW-3D Water and Environmental Webinars Schedule

Overview of free-surface modeling setup
Thursday, March 15, 2018
1:00 – 2:00 pm EST

Piano Key weir discharge analysis
Thursday, April 12, 2018
1:00 – 2:00 pm EST

Modeling fishway passages
Thursday, May 10, 2018
1:00 – 2:00 pm EST

Density flows and heat transfer: plumes and stratification
Tuesday, June 12, 2018
1:00 – 2:00 pm EST

Municipal hydraulics: aeration tank modeling
Thursday, July 12, 2018
1:00-2:00pm EST

Complex culvert hydraulics
Thursday, August 2, 2018
1:00-2:00pm EST

Air entrainment analysis
Thursday, September 13, 2018
1:00-2:00pm EST

Municipal hydraulics: chemically reacting tanks
Thursday, October 4, 2018
1:00-2:00pm EST

Tailing analysis
Thursday, November 8, 2018
1:00-2:00pm EST

Register Online

You can register for as many or all of the webinars that you would like to attend using the form below. Use Ctrl + Click to select multiple webinars.

  • No multiple registrations! Use Ctrl + Click to select the webinars that you would like to attend.
  • Please tell us if there is anything specific that you would like for us to address in this webinar series.

About the Instructor

Brian Fox, CFD Engineer

Brian Fox is a Water and Environmental applications engineer with Flow Science. Brian received an MS in Civil Engineering from Colorado State University with a focus on river hydraulics and sedimentation. He has over 10 years of combined experience working within private, public and academic sectors in water and environmental engineering applications. His experience includes using 1D, 2D and 3D hydraulic models for projects including fish passage, river restoration, bridge scour analysis, sediment transport modeling and analysis of hydraulic structures.

The post FLOW-3D Water and Environmental Training Webinars appeared first on FLOW-3D.

► FLOW-3D for Coastal Applications
  22 Jan, 2018
FLOW-3D coastal applications webinar


Computational Fluid Dynamics (CFD) is a useful tool to improve coastal resilience and for the engineering of coastal infrastructure. Applications ranging from wave run-up/overtopping, coastal erosion and estuary breaching/aeration to artificial reefs, floating docks, ship locks and tide gates can all be analyzed using FLOW-3D. This webinar will showcase examples of such applications and demonstrate how computational analysis can greatly benefit the coastal engineering community.

Date: February 28, 2018
Time: 1:00 pm EST
Register online >

The post FLOW-3D for Coastal Applications appeared first on FLOW-3D.

► Good Hardware Means Improved FlowSight Performance
  18 Dec, 2017

In order to take full advantage of FlowSight, our advanced state-of-the-art postprocessor, it is essential that you have good hardware. In this blog, Stephen Sanchez, senior GUI developer, gives his two cents on how you can obtain improved FlowSight performance by following these hardware recommendations.

Get a really good graphics card

We highly recommend that you start with a graphics card with at least 3GB of VRAM. This is especially important if you will be doing a lot of volume rendering. Volume rendering is an advanced capability of FlowSight that visualizes the details of a variable throughout the fluid domain, instead of just the iso-surface. This feature is quite insightful, but requires good hardware to be effectively used during post-processing.

Volume rendering FlowSight performance
Volume rendering (left) of bubbles in FlowSight.The image shows the strain rate magnitude of each bubble and the surrounding liquid.

Next, you should not use Intel integrated graphics as your primary graphics hardware. Much of FlowSight’s functionality does not work with this configuration, and as a result, we do not support Intel integrated graphics. FlowSight performs best when used with NVIDIA graphics cards, particularly the Quadro family. While high-end AMD cards should work, we have found that they are not as reliable as NVIDIA hardware and drivers, so we always recommend NVIDIA over AMD.

Nvidia graphics card

Dual graphics cards on laptops – A simple but hidden solution

Many laptops now come with the ability to switch between a NVIDIA card and an Intel Integrated graphics card. It is important that you make sure FlowSight (as well FLOW-3D) is being launched with the NVIDIA card. Forcing your laptop to launch with the NVIDIA card can be done through the NVIDIA control panel.

Switching graphics card to Nvidia

Update your video driver

We recommend that you check to make sure that your video driver is up-to-date. We have had reports of artifacts and display issues in FlowSight that have been easily resolved by simply updating the video driver. Keeping your video driver current is a good way to avoid such issues.


It is important to be aware of memory requirements, as insufficient memory can lead to as high as a 10x performance decrease! The amount of RAM needed depends on a number of factors, particularly the size of your simulation. In order to provide users with the most flexibility, we have the following RAM recommendations based on simulation size:

  • Extra-large (200 million+ cells): At least 128GB
  • Large (Between 60-150 million cells): 64-128GB
  • Medium (Between 30-60 million cells): 32-64GB
  • Small (30 million cells and below): At least 32GB

However, you should always get the most RAM as you can, irrespective of the problem size in order to maximize flexibility and ensure the smoothest user experience.

In a later blog we’ll expand on how to get the most out of FlowSight by discussing core features that are typically the most time and resource intensive, and how they can be used more efficiently.

The post Good Hardware Means Improved FlowSight Performance appeared first on FLOW-3D.

Mentor Blog top

► On-demand Web Seminar: An Introduction to MotorSolve: Rapid Electric Motor Design
  22 Feb, 2018

Is it possible to design an electric motor in less than 20 minutes?  Watch this web seminar to find out how!

► White Paper: Formal Verification: Not Just for Control Paths
  22 Feb, 2018

Whether it is a full formal verification environment, or a complementary piece to an existing testbench targeting critical or difficult to reach functionality, the secret to enabling datapath verification is managing state space and constraining dynamically sized transactions. An RTL based formal testbench responsible for capturing and simplifying transactions holds and deconstructs the static pieces of the transaction. A synthesizable relationship can then be formed between the static and dynamic packet components and state space is reduced via a collection of assumptions built around the packet defining RTL.

► White Paper: Understanding The UPF Power Domain and Domain Boundary
  22 Feb, 2018

This paper focuses on the fundamental construct of UPF and its methodologies for defining and distinguishing a power domain and domain boundary. The mainstream techniques adopted today, as shown in this paper, are mostly based on design type and the complexity demands for system-on-chip, ASIC, microcontroller unit, or processor core design implementations.

► White Paper: Tools + services accelerate automotive electrical design
  19 Feb, 2018

Scott Majdecki and Andrew Macleod explain why services offered in conjunction with design tools matter more than ever in automotive E/E design. Indeed it’s no stretch to say that advanced automotive design software combined with proven deployment services is the surest path to success. Take out that services expertise and the path suddenly gets that much longer and more uncertain, precisely at the moment when falling behind will be more costly than ever. That’s because the industry’s well-documented evolution in the direction of electrification, autonomy, services and ride sharing, together represent one of the most lucrative business opportunities in history.

► Event: Electromagnetic Simulations for Virtual Prototyping and Design
  16 Feb, 2018

MagNet 2D/3D & MotorSolve are used by designers in automotive, aerospace and many other industries as their preferred computer-aided engineering tool for electromagnetic field simulations. This complimentary seminar is a great opportunity to learn more.

► Technology Overview: Team Bath Racing
  16 Feb, 2018

Formula Student Racing team member, Marios Mouzouras, of Team Bath Racing discusses how they used FloMASTER for the thermal management of their car for the competition.


Tecplot Blog top

► Appending Data to Tecplot 360 SZL Files
  16 Feb, 2018
“Time is an Illusion.” ― Albert Einstein

High fidelity CFD solvers are generally iterative in nature. They move the solution incrementally forward in time (or pseudo time for steady-state solutions) computing each new solution based on the previous solution. CFD engineers often write a snapshot of the solutions at periodic time steps so that they can follow its progress.

CFD engineers want to verify that:

  • The boundary conditions are behaving as expected.
  • The solutions is stable.
  • They want to do this while the solver is still running.

The latest improvements to the TecIO library make this much easier!

Learn more about TecIO »

Previous Limitations

Earlier versions of TecIO had limitations when writing out a sequence of time steps to a SZL (.szplt) file. It worked OK if each time step was written to a separate file; but appending data to the same file caused it to cache a large amount data in memory. In addition, writing to separate files limited the use of variable sharing (which minimizes file size by sharing, for example, a common set of grid coordinates).

On many high-performance computing systems, the total number of files is limited. On these systems, appending all time steps is the preferred option since it reduces the number of files created.

As of Tecplot 360 2017 Release 3 (November 2017), all of these limitations have been solved!

New TecIO Capabilities: Appending Data to SZL Files

The 2017 R3 version of TecIO (released November 2017) has a new function, TECFLUSH142, that will write the currently cached TecIO data to a set of intermediate files. These files are then recombined into a single SZL file when TECEND142 is called to complete the write.

Appending SZL Data

This accomplishes three things:

  1. It eliminates the excessive memory usage of previous TecIO versions when writing multiple time steps.
  2. It allows the sharing of variables across multiple time steps. This is commonly used with unchanging grids to share the same set of grid coordinates across all time steps.
  3. It allows the user to view the data before all time-steps have been written to the file.

Item 3 is a new capability in TecIO 2017 Release 3. It is accomplished using a shell utility called szcombine, which reassembles the temporary files into a single SZL file.

With these new capabilities, TecIO is far more usable for writing multiple time steps than it has been in the past. Give it a try!

Learn more about TecIO »


Scott Imlay
Scott Imlay
Chief Technical Officer
Tecplot, Inc.

► Upcoming Webinar: Getting Started-Tecplot 360 Basics
  30 Jan, 2018

Getting Started with Tecplot 360Do you want to learn the basics of post processing with Tecplot 360 in 30 minutes? This is your chance to ask those questions you’ve been waiting to ask!

Upcoming Webinar:
Getting Started with Tecplot 360: The Basics
February 20, 2018, 10 AM Pacific Time

Reserve your spot by registering now.

Getting Started: Tecplot 360 Basics

20/2/2018j/n/Y10:00am PST2018-02-20T18:00:00Zg:ia T30 minutes
Do you want to learn the basics of post processing with Tecplot 360 in the shortest amount of time? This 30-minute webinar may be for you.

Tecplot brings you a great advantage: The ability to quickly and accurately make and communicate engineering decisions.

Learn how to use:
• Zones and variables
• Isosurfaces, slices and streamtraces
• Extract over time
• Line plots
• Image exports

Register and receive a link to the recording after the Webinar.
► Tecplot Names KFour Metrics as New Software Distributor in India
  15 Jan, 2018

KFour Metrics to distribute Tecplot 360 and Tecplot Focus in India for the commercial and government sectors.

BELLEVUE, WA— January 15, 2018—Tecplot, Inc. has signed a distributorship agreement with KFour Metrics of India for sales and support of Tecplot 360 and Tecplot Focus in the commercial and government sectors.

KFour Metrics distributor in IndiaBased in Hyderabad, KFour Metrics has evolved over the 30 years the principal partners, Venkatesh and Shaila Chavaly, have been in the engineering design business. They began as Krittika Software Pvt. Ltd., an engineering design company providing engineering services and 3D CAD and CAE training, as well as distributing MCAE (mechanical computer aided design) software. In 2002 the company merged with DesignTech Systems Pvt. Ltd. and added Altair products to their distributorship. In 2007, Venkatesh and Shaila sold their interest in DesignTech Systems and started KFour Metrics.

We want to provide the highest level of service in a region where the use of CAE/CFD is growing,” said Tom Chan, Tecplot CEO. “KFour Metrics has natural synergies in pre- and post-processing, and their experience and philosophy is similar to our own: to provide the tools and expertise required for engineering analysis, design, implementation and evaluation. We are happy to welcome KFour Metrics to our team.

KFour Metrics partner with companies that have a similar set of values and provide solutions to technology challenges. They also represent these products in India.

  • Pointwise whose grid generation software (earlier known as Gridgen) is solving the top problem facing computational fluid dynamics (CFD) today – reliably generating high-fidelity meshes.
  • Sculptor, the family of products that provides real-time morphing technology for a wide range of 3D models including STL, point clouds, CAD data, and analysis mesh data

Tecplot 360

Tecplot 360 is a suite of visualization and analysis tools that can load data from 27 different sources, handle large data sets, automate workflows, and visualize parametric results. Engineering decisions can be more quickly made with Tecplot 360’s integrated XY, 2D, and 3D plotting capability. The ability to export publication-quality images and animations help engineers and scientists present and communicate their results to others.

Tecplot Focus

Tecplot Focus is engineering plotting software that allows quick and precise plotting of engineering and test data. Featuring extensive XY, 2D and 3D capabilities, Tecplot Focus is designed for measured field data, performance plotting of test data, mathematical analysis, and engineering plotting in general.

About Tecplot, Inc.

Tecplot, Inc., an operating company of Toronto-based Constellation Software, Inc. (CSI), 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.

More information: KFour Metrics, Tecplot 360, Tecplot Focus

► Top 3 Reasons to Post-Process COMSOL® Results with Tecplot 360
    3 Jan, 2018

Having a reliable, accurate, flexible and easy-to-use method to post process your results is key to effectively analyzing CFD, other simulation and test data results.

Tecplot 360 is a suite of CFD visualization and analysis tools that can handle large data sets, automate workflows and visualize parametric results.

Learn More About Tecplot 360 »

Why Visualize and Analyze Your Data With Tecplot 360?

    1. Load Simulation And Test Data From Many Different Sources

      If you aggregate data from multiple simulation sources, in addition to test data, you need the ability to load dozens of file types, as well as to cohesively view and compare all of your results. With Tecplot 360, organizing data into one or many frames is easy, intuitive and quick.

      Powerful visualization options give you the ability to scale, sort and overlay images and data. Extracting data from 2D and 3D visualizations to match with 1D test data is fast and can be easily automated.

      Read Details In The Tecplot 360 Datasheet (PDF) »

    2. Have Complete Control Over Your Plots

      Communicating your results with professional, report-quality plots means you need the right format for the right publication – whether for a printed report, a presentation or a website. You will also want control over every aspect of the way your plot looks.

      With Tecplot 360, you can output plots as raster and vector images, and in multiple video formats for animations. You can also:

      • Annotate with LaTeX fonts
      • Gain precise control over axes, plot backgrounds, labels, and legends
      • Overlay multiple contour lines and variables on the same plot
      • Export high-quality PNG, PostScript or EPS

      You will not find a more flexible post-processor.

    3. Analyze Your Results Quickly And Accurately

      Creating high-fidelity models of complicated applied physics interactions mean lots of data to handle. You need a tool that can reduce time-to-first-image and all-around processing time. If you are working on a laptop or other mid-tier computer, running out of RAM and huge file sizes can be a major headache.

      Tecplot 360’s SZL file format loads and processes data faster, uses less memory and produces smaller file sizes. Integrating software, like MATLAB, into common workflows is a requirement for engineering tools today. Tecplot 360 is extensible through our Python API and through the Macro language.

Whether you are working with CFD, other simulation or test data, you need accurate results, robust analyses and seamless communication. Tecplot 360’s suite of visualization and analysis tools have unique features that help you explore large data sets from multiple simulations, compare results and evaluate overall system performance.

The Team here at Tecplot is dedicated to helping you get your work done more quickly, easily and efficiently. Contact us if you have questions.

Alan Klug
Alan Klug
Vice President, Customer Development
Tecplot, Inc.

► Tecplot at AIAA SciTech 2018
    2 Jan, 2018

American Institute of Aeronautics and Astronautics (AIAA) Conference

AIAA SciTech 2018

Join Tecplot at AIAA SciTech at the Gaylord Palms in Kissimmee, Florida, January 8-12.

Tecplot engineers will be on hand to answer your questions and give you a 1-on-1 demo of the recently released Tecplot 360 2017 R3. The benefits of this release include support for LaTeX fonts, VTK data loader support for VTU and VTM file formats, recording and playing back PyTecplot scripts from the Tecplot 360 interface, and enhanced slicing and iso-surface capability.

Exhibit Hours, Booth #406

Tuesday, January 9 1:00 pm – 4:30 pm
Wednesday, January 10 8:45 am – 4:00 pm
Thursday, January 11 8:45 am – 4:00 pm

Join Tecplot at AIAA SciTech 2018

Tecplot Invitation-only Reception

Fly on over to our Reception on Wednesday, January 10, 6pm. Pick up your invitation at exhibit booth #406

Relax and enjoy pizza and beer with your favorite Tecplot engineers. You will also want to pick up your 2018 Tecplot T-shirt (while supplies last)!

Higher Order Element Workshop: January 6-7

Dr. Scott Imlay, Tecplot Chief Technical Officer, will be attending the Higher Order Element Workshop.

Technical Presentation: January 10, 10:30-11:00 AM, Location: Tampa 1

Dr. David Taflin is one of our senior software engineers who has been working on SZL Server (released with Tecplot 360 2017 R1). Dave will be presenting “A Subzone-Based Client-Server Technique for I/O Efficient Analysis and Visualization of Large Remote Datasets“, S.T. Imlay; D.E. Taflin; C.A. Mackey.

Request an individual or group meeting at AIAA SciTech

If you do not see the form below, send your request to

► New Ways to Visualize Increasingly Complex and Unsteady Data
  21 Dec, 2017

As we look forward to 2018 and beyond we’re excited to take part in an industry that never rests and is never satisfied. We’re eager to bring new ways to visualize increasingly complex and unsteady data, and provide new analysis features, while maintaining the Tecplot promises of giving engineers complete control over their output, fast post-processing speed, report-quality XY, 2D and 3D plots, and excellent stability, service and support.

A Look Back at the Porsche 956

On the first day of my first aeronautical engineering job after graduate school, I opened my desk drawer and found a small package of 35mm Kodachrome slides dating back to the early 1980’s (it was an old desk). The image below, likely pre-dating Tecplot entirely, is from those slides. Racing fans will know that it’s a model of a Porsche 956, a famous beast of a racecar from the Group C era of endurance motorsports.

porsche 956

Flow simulation of a Porsche 956, circa 1982

Fluids experts will infer that it’s not even a “real” CFD solution. It’s from a potential flow code, and at that time, this simulation was likely run on a Cray X-MP and took hours, if not days, to complete. As I write in the waning days of 2017, I think of this image frequently and ponder how far our industry has come over the ensuing 35 years, and specifically how far Tecplot has come in 2017.

Visualize Increasingly Complex and Unsteady Data

In January we released Tecplot 360 2017 R1, with PyTecplot and SZL Server, and made Chorus available to all Tecplot 360 clients with active maintenance. This was the biggest change to Tecplot software in the past several years. Throughout the year we’ve added to PyTecplot, making it possible to control the Tecplot GUI and streamline workflows.

Additionally, in 2017 we have:

It has been a big push by everyone at Tecplot, and the reason for this effort is clear: we don’t live in 1985 anymore and our clients need better tools with enhanced capabilities. Engineers create tens or hundreds of gigs of data overnight, both in the form of hundreds or thousands of simulations, and also extremely large simulations.

That isolated panel method solution is largely history, and in its place are ensembles that you can analyze in Chorus, billion-cell runs you can visualize with SZL (and remotely with SZL Server), and data of all forms that can be automated with PyTecplot to meet the increased expectations placed on engineers everywhere.

A big thank you to all our users and supporters for a great 2017, and stay tuned for much more.

Alan KlugBlog by:
Alan Klug
Vice President, Customer Development
Tecplot, Inc.
Join us at AIAA SciTech 2018, January 9-12 in Kissimmee, Florida »

Schnitger Corporation, CAE Market top

► Quickie: Topcon acquires ClearEdge3D to move into software
  22 Feb, 2018

Topcon announced earlier this week that it has acquired ClearEdge3D, a maker of 3D modeling and construction verification software. Unless you’re into AECish topics, you may not have heard of Topcon. Topcon is a Japanese company that makes positioning instruments such as survey stations, laser scanners and similar gear (among other things that aren’t relevant here). ClearEdge3D is a US software and services company best known for its EdgeWise and Verity brands. EdgeWise isn’t BIM (building information management), but ties into BIM models and workflows by manipulating laser scan point clouds to create objects for use in a BIM product like Autodesk’s Revit. By scanning an existing asset and then feeding that directly into BIM, the CAD modeling step in an AEC project can be significantly shortened. And since one of Topcon’s product lines is laser scanners …. you can see where this is going.

Verity, ClearEdge3D’s other main product, compares a point cloud to design or fabrication models, identifying anything that doesn’t match –maybe floors that aren’t level or items that have been installed in the wrong place. This used to be done manually, by humans who walked a construction site to make these comparisons. Verity automates the task, and laser scans remove any ambiguity or personal interpretation.

Why am I telling you this? Because whether you’re in AEC or not, this is about data. About using laser scans, in this case, for as many things as humans can think of. And it’s about applying techniques like object recognition (does that cloud of points represent a door or a window?) to new areas. One can see this combination having application far beyond the construction site, as the BIM model moves through the lifecycle of that building. Want to move a wall? A laser scan of its construction can reveal exactly where the electrical lines are. Need to train someone in a dangerous task? Have them move through a BIM model to practice virtually.

It’s also about hardware plus software. ClearEdge3D has said that it plans to maintain its hardware agnostic view of the market, so not disadvantaging users of competing laser scanners. Topcon has said it doesn’t see any immediate changes to Verity or EdgeWise but one can theorize that they might be bundled with Topcon laser scanners in the future. It’s also about software margins: hardware is notoriously low-margin because each item has a fixed cost. Software is the opposite: with downloads, the first license bears all the cost of development and every other license is pure profit.

Financial details of the transaction weren’t released.

The post Quickie: Topcon acquires ClearEdge3D to move into software appeared first on Schnitger Corporation.

► 10 things: Digitalization at AVEVA World Conference, Houston edition
  21 Feb, 2018

I spent the last week with AVEVA at its North American user conference. I haven’t attended one of these in years, mostly going to the international Summit events (here, here … search the site for more). The Summit is strategic; it’s about IT and economic trends, forces shaping the end-industries AVEVA addresses. The Conference has a bit of this but also very specific roadmap sessions, down to what’s in coming point releases. It was fun and interesting to hang with users and department managers — their interests are specific and often short-term.

The keynotes were focused on the theme of the event, digitalization. Yes, it’s hard to say, but it’s oh so important to consider. Tom Singer of Aker Solutions, Justin Weaver of Southern Company and Terje Maanum of Statoil spoke about how digitalization changes their enterprises. Aker, for example, creates template-based design concepts that enable them to quickly and accurately generate quotes and preliminary designs, and then feed directly into more detailed work if they win the contract. Southern Company is working to replace job boxes (the steel container in the title photo) full of paper drawings with boxes that contain touchscreen monitors to enable the same view and markup but also walkthroughs and other techniques designed to help trades better understand what they’re working on. Finally, Statoil is really impressive, managing all of the data on over 70 assets with a team of 8 people — but most gee-whizzy was the fact that this single-source concept leads to downstream technologies like augmented reality, which helped identify equipment that had been installed incorrectly. Statoil’s EPC was able to find and fix the problems before the plant commissioning, averting a disaster.

This kind of forward-looking technology use was present in all of the sessions I attended, whether on how to become an EPC 4.0 (a cute play on the whole Industry 4.0 thing) to how handover standards can and will change what and how we model. But there was some really detailed tactical stuff in the roadmaps, too, to show how AVEVA plans to move its products forward.

With this background, here are ten things I think I think about AVEVA World Conference, North America. In no particular order:

  1. Unlike many of its peers, AVEVA invites prospects to this event. That’s very confident of the company, since no one can control who these prospects speak with or what they, therefore, learn. I sat with one gentleman who was interested in one of AVEVA’s lesser-known offerings and was surprised by the sheer size of the rest of the portfolio.
  2. The elephant in the room, AVEVA’s merger with Schneider Electric’s software assets, wasn’t addressed all that much — the deal is about to close, likely on 1 March 2018. A new CEO starts this week. More than that, no one seems to know. But expectations are high and the excitement was real. As in Cambridge, people are looking forward to seeing what this much larger software company can do.
  3. That much bigger product portfolio will address the entire life of a production plant, from front-end conceptual design via process simulation, through detailed design, operations and ultimately, into decommissioning. It takes the AVEVA toolset from short-term to lifecycle-long relevance — and many attendees are thinking about how that will change what they do.
  4. Will they design differently if that model can/will be used in operations? There are systemic problems in the process industries that make that far from commonplace right now, but the greater scope of AVEVA’s offering has the potential to finally shift that — and the keynotes examples highlight why it’s such a good idea.
  5. Many in the audience in Houston were tactical–users who needed to get a job done. In many cases, EPCs who use many if not all of the products on the market. They were there to talk to product managers about what’s in the next point releases — and told me that, in general, they’re happy with their AVEVA product(s) and that it stacks up well against the competition. They use whatever tools their clients specify and try to keep their skills sharp across the products.
  6. AVEVA has taken a slow roll approach to cloud, reasoning that many of its users and projects are in areas where WiFi is uncertain or where security concerns prevent adoption. The company showcased partner Orinox, which offers a one-stop shop for cloud virtualization of AVEVA’s products. Joint customer Southern California Gas spoke about spinning up an AVEVA installation for a project in 2 weeks. They had more users than anticipated, which meant tapping into the flexibility of cloud licensing, using 1D, 2D and 3D products as well as incorporating laser scans into their workflow. it was ambitious and ultimately successful: the company plans to roll out its AVEVA cloud to more projects.
  7. Another presentation in that same session could not have been more different — and showcases how AVEVA has been working to add cross-vertical products for longer than many people realize. Northwest Fabricators told how they transformed their business via AVEVA’s Fabtrol. They’re a 40,000 shop that cuts steel and welds pipe, serving customers on large and complex projects via Excel spreadsheets. Can you imagine? A 30 MB spreadsheet that opened so slowly, people were afraid to breathe while it did in case they jinxed it. They had problems in procurement, material tracking, progress tracking and reporting, billing — all of the things that one could presume Excel to handle just fine, but not at this scale. Implementing Fabtrol to manage estimating, drawings, materials and production was a big deal, since they needed new IT infrastructure, training, changed processes to be more modern— the speaker said it was “sheer terror”. Today, Fabtrol does estimating, procurement, material receiving and nesting, and they’re adding Bocad to streamline detailing. Lots of cost and time savings, lower waste on materials, and the security of a modern IT infrastructure. I hope AVEVA turns this into a case study, because the speaker highlighted truly business-changing effects of the implementation.
  8. This theme, that AVEVA is more than 3D (MT3D) has been a cornerstone of AVEVA-speak for several years. The company really got its start with its PDMS CAD offering —always underpinned by its Dabacon database— but has struggled to make a name for itself outside that arena. For those not following so closely: Fabtrol for managing steel fabrication. Procon for contract management. ISM for standards management. ERM (Enterprise Resource Management) to track materials, labor and other project assets. AVEVA Net for information management — and to feed into AVEVA Engage for a visual front-end to Net’s data. I may have missed some products, but my point is: there’s much more then PDMS and E3D.
  9. This plays out well in the Americas, where AVEVA’s business sounds fundamentally different from inother parts of the world. Americas EVP Amish Sabharwal told attendees that revenue in the Americas is growing at 25% per year (as opposed to 15% for the group overall); MT3D accounts for 50% of revenue (versus 25% overall) and that oil and gas represents 20% of the business in the Americas, compared to 50% for AVEVA as a whole. That’s a point to watch as the Schneider combination takes hold: how can the new AVEVA capitalize on what the Americas are doing to move beyond AVEVA’s traditional upstream oil and gas?
  10. Finally, Southern Company’s job box is awesome. Mr. Weaver was speaking on stage, powerpointing away, and then walked down to the steel thing no one had really paid much attention to until that point. He opened the doors of the box, and we were all expecting a pile of paper drawings. Maybe a tablet. Nope. A huge, gorgeous display stole the scene. It was a great reveal, but with a serious purpose: doing what you’ve always done, in ways that are comfortable and familiar, won’t work much longer. Will digital technology replace all paper, everywhere? No. But the digital job box Southern Company showed us enables a lot of new ways to interact with design intent–and that can only lead to better outcomes, on this case, on the construction site.

Digitalization is a huge topic. It means many things, as each implementing company takes stock of its current situation and figures out its strategic directions. Do we want to bid more quickly and reliably on projects? Steal Aker’s templates idea. Digital job box? Manage CAD models and associated data with a view to making them available at the construction site (or plant operating floor). Giant spreadsheets too risky in your steel fabrication shop? Fabtrol. It’s a change in mindset that requires thinking beyond the immediate task to the downstream uses of the data being created, and being willing to take extra time to add in what those processes might require. The AVEVA user crowd at the event was at many different points in that thought process, but events like this move the needle forward every time.

Note: AVEVA 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 Southern Company’s job box — it’s not great, too backlit, but the best my iPhone could do. If I get something better, I’ll update.

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► Quickie: Altair nabs ElectroFlo CFD for electronics cooling
  21 Feb, 2018

Altair just announced that it has acquired TES International’s technology and intellectual property and hired founder Ben Zandi to lead its thermal solutions software development efforts. Dr. Zandi founded TES to address electronics cooling and today (yesterday?) the company provides solutions for thermal design and packaging, custom programming, general heat transfer, stress and vibration analysis, and CFD. TES’ main CFD product, ElectroFlo, is an electronics cooling package designed for high power-density applications. According to Altair, ElectroFlo uses coupled thermal/electrical algorithms, improving results accuracy for systems containing wires and traces and can be used to simulate everything from electrical components and printed circuit boards to full systems.

Dr. Zandi is quoted in Altair’s press release as saying that ElectroFlo will be combined with AcuSolve to “provide thermal solutions for applications with complex flow interactions, while coupling with Altair’s Electromagnetic Compatibility (EMC) and Electronic Design Automation (EDA) technologies will provide users a broad set of solutions for electronic applications.”

No financial details were announced but since Altair is now publicly traded, we’ll see if their coming Q4 2017 results offers more information.

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► 10 things I think I think: SOLIDWORKS World 2018
  12 Feb, 2018
SOLIDWORKS World is always a spectacle of maker-ness, designers competing to out-SOLIDWORKS one another in design competitions and learn from one another. It’s, as one speaker put it, nerd-heaven. This year was the twentieth anniversary event and many attendees proudly displayed buttons from past events. It’s hard to capture in a blog post but here are ten things I thought about as I talked to attendees, SOLIDWORKS and Dassault Systemes (DS) people and their partners:
  1. I never did find out how many people were at this year’s event (5,000? 6,000?) but everyone I spoke with had a specific goal for attending: functionality to uncover, product strategy to learn, business to conduct. But without question, the main and common reason was community. A lot of attendees work in small shops where they’re the only SOLIDWORKS user; meeting others like them is a big deal, a chance to unwind, compare notes and connect. it’s also a chance to network for that next job, which brings up
  2. Certification is a big deal. Quite a few of the attendees I spoke with were there to take a certification exam (for the first time, or to be part of the 20th anniversary of the certification program). It’s a big deal — nervous-making until it’s over, then a little happy-dance while the SOLIDWORKS crew takes the picture and hands over the certificate and swag bag. Without exception, the exam-takers see it creating legitimacy at their current employers and positioning for the future.
  3. The flip side of these conversations: why aren’t more of you using simulation? It’s available in many of the SOLIDWORKS packages you already have! This CAE sessions I attended had 20-30 people, so there are some simulation users, but very few of the casual conversations turned up CAE as part of the typical design process. People over-design, think they don’t have the hardware, feel they don’t know enough to credibly explain the results … Lots of reasons but, honestly, none that really stand up today. It mostly comes down to already having too much to do, and a belief that simulation is for other people.
  4. But the SOLIDWORKS team keeps trying: SIMULIA Structural Simulation Engineer (which I had been referring to as SIMULIA SimDesigner) is for the top end of that crowd and, while not yet generally available, demos well. SOLIDWORKS Simulation can do many things today, but not complex non-linear cases. Enter SIMULIA Structural Simulation Engineer, which aims to connect SOLIDWORKS and Abaqus NonLinear. It’s currently in Lighthouse (ie Beta) mode, but the demo I saw begins to address some of the differences between SOLIDWORKS’ way of doing things like connections and Abaqus’ — it’s a work in progress rig now but with a lot of promise.
  5. xDESIGN, SOLIDWORK’s entry into browser CAD, also demos well (and CEO Gian Paolo Bassi showcased a number of user projects) but I didn’t talk to any current SOLIDWORKS users who were thinking of switching, even if they were interested in trying out xDESIGN. In part, people aren’t convinced that they’ll always have suitable WiFi, even in their offices, and are concerned about lost productivity. One important point: SOLIDWORKS staff typically talked about coming enhancements being in the desktop version and/or xDESIGN — it sounds like both versions will co-exist for the foreseeable future.
  6. xDESIGN, the MySOLIDWORKS community, the cloud-compute component of SimDesigner and many other products discussed at SWW18 run on the 3DEXPERIENCE platform, parent company Dassault Systemes’ way to connect applications, users, data and external resources. The SimDesigner demo highlighted both the good and bad of the platform: collaborating around simulation results was easy, fast and intuitive —when it worked. WiFi died during the demo and so did the collaboration session. Not DS’ fault at all, but a reality of expecting to be connected at all times. There’s also still the problem that products on the 3DEXPERIENCE platform use the CGM CAD kernel while SOLIDWORKS uses Parasolid. Bridging the two requires work, a hard sell for long-time SOLIDWORKS users with a legacy of parts and assemblies
  7. The Marketplace is another 3DEXPERIENCE offering that I think can, in time grow to serious significance. It aims to connect modelers to suppliers. Right now, 3D printing bureaus and some types of parts suppliers can offer their goods and services to SOLIDWORKS and CATIA users via in-CAD add-ons. Designers can compare prices, schedules and service options. I’ve asked DS for more details on this and will report back once my questions are answered.
  8. Speaking of, 3D printing was huge at SOLIDWORKS World. Lots of vendors of both hardware and materials, cool debuts of more affordable production printers, in-CAD model checking and design assistance fueled many conversations. In a session on new technologies, the speaker asked for a show of hands of who was interested in 3D printing; just about everyone raised their hands (as opposed to a tiny handful for simulation, sigh.) I spent time with several printer makers and have to say: we’re getting to the point where part orientation, segmentation, supports and other print-specific factors are becoming less relevant. I hope to write in more detail about one supplier, Raze, sometime soon. Cool stuff there.
  9. IoT was represented at SOLIDWORKS World, but not as heavily as I had thought it would be. Perhaps this is an audience of doers rather than new-business-opportunity-seekers, but the concepts of connectedness leading to preventative maintenance still seemed somewhat new. Surprising.
  10. Also surprising was the PLM-ness of the event. DS used to keep separate the PDM-ness of the SOLIDWORKS message and the PLM-ness of its 3DEXPERIENCE/CATIA programs, but there’s more crossover than ever before. Yes, many SOLIDWORKS users use PDM-branded products yet their goals are very PLMish. SOLIDWORKS PLM Services and SOLIDWORKS Manage both start crossing the line into PLM. And that’s not  bad thing, since the benefits of PLM extend beyond CAD model/data management and are applicable to enterprises of all sizes.
  11. (Yes, it’s 11. Still working on the format.) Finally, the users I spoke with are impressed by SOLIDWORKS 2018. Not everyone adopts every version and quite a few people have opted to stay on 2016 rather than move to 2017. But 2018 seems like it has enough in it to warrant the jump. And 2019  promises even more new stuff — it feels like the when to switch calculation is going to get more difficult as we learn more about the 2019 release.
Last thing: Special shoutout to the breakfast guys from Monday. We commiserated about the Patriots loss to the Eagles in a hard-fought Super Bowl and then got down to it: If you’re not a member of a user group, why not? SOLIDWORKS makes it as easy as possible to start or join a group and you get to influence SOLIDWORKS development — while networking with peers and eating pizza. Just GO!
Note: Dassault Systemes 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. 

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► This time it’s Siemens, acquiring Sarokal for IC testing
    7 Feb, 2018

The pace of acquisition really doesn’t jibe with the news that capital is getting harder to come by. Either that, or all of these companies have seriously deep pockets. Today. Siemens announced that it place to acquire Sarokal Test Systems to continue the buildout of its solutions for the electronics/integrated circuits/telecommunications world.

According to Siemens, Sarokal Test Systems makes “test solutions for fronthaul networks that are comprised of links between the centralized radio controllers and the radio heads (or masts) at the “edge” of a cellular network. Sarokal products are used by chipset vendors, fronthaul equipment manufacturers, and telecom operators to develop, test and verify their 4G and 5G network devices from the early design stages through implementation and field-testing … Sarokal’s tester product family addresses the entire development and maintenance flow for cellular and wired transmission system testing. The technology is especially designed to detect radio frequency (RF) problems. With Sarokal’s foresight into the requirements of 5G testing, their testing models were created from the beginning for both the virtual (digitalization) environment as well as the physical testing environment.” I have a tenuous understanding of what that means and will update once I am briefed and more knowledgable.

Harri Valasma, CEO of Sarokal, said in the announcement that “[b]ecoming part of Siemens and integrating our technology into [Mentor’s] Veloce emulation platform will give us greater visibility into early customer adoption of 5G, which can help us maintain our leadership as this segment is forecasted to grow rapidly.”

Terms of the transaction, which is expected to close before the end of March 2018, were not disclosed.

As I said, I understand the words but not the meaning. More soon.

Do you use Sarokai’s products? Why? What’s unique about them? Does it strike you as positive that they join the Siemens portfolio? Comment below or send me an email.

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► Quickie: Aras acquires for PLM + MRO capabilities
    6 Feb, 2018

Aras announced last week that it has acquired infoTRAK MRO from Infospectrum and renamed the product Aras Impresa MRO. MRO stands for Maintenance, Repair, and Overhaul and is a key component in many asset operators’ strategies: planning who, what, when and how to do maintenance to maximize uptime and profitability.

In making the announcement, Aras said, “With the Impresa acquisition, Aras will deliver PLM and MRO on a single, modern platform that extends the Digital Thread to the field and provides the foundation for Digital Twin.” The company said that its customers increasingly want to offer capacity-as-a-service, which means that they “need to transform how they develop products and plan to service them in the field. Connecting their PLM to MRO gives these companies a path to achieve both goals with a closed loop between product development and field data”. That’s absolutely key to many equipment makers’ thinking about reinventing their products in an IoT/Industry 4.0 age.

infoTRAK MRO managed everything from planning and scheduling maintenance events, to their execution with operational activities such as inspection and work card generation. From reading through the InfoTRAK MRO website, it seems targeted to the aerospace industry, but I would imagine that Aras will quickly make it more general-purpose.

No financial details were given, but Aras did say that it is acquiring technology, intellectual property, and subject matter expertise (ie. people). Aras plans to incorporate Impresa MRO onto the Aras PLM Platform. According to Aras, Impresa MRO will be available as part of Aras enterprise subscriptions and is immediately available at no charge for current Aras subscribers. So, go!

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