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Another Fine Mesh top

► This Week in CFD
  21 Sep, 2018
Everyone in CFD is hiring, or so it seems. Four companies, including Pointwise, are advertising for open positions and there certainly are others. Did CFD visualization need rethinking? Ceetron thinks so. And high-order, curve mesh generation is now available in … Continue reading
► This Week in CFD
  14 Sep, 2018
It must be the season for CFD contests as ANSYS just opened submissions for their hall of fame, EDEM announced winners of their viz contest, and there’s still time to enter your mesh for Pointwise’s The Meshy. Maybe as a … Continue reading
► Mesh With Us: Two Open Positions at Pointwise
  12 Sep, 2018
Pointwise will be expanding our Technical Sales team through the hiring of two professionals who are passionate about mesh generation, CFD, and engineering software.  Why hire one when you can hire two? Both of these new hires will work on … Continue reading
► Motorsport with Wings: Red Bull Air Race
    7 Sep, 2018
The Red Bull Air Race returns to Fort Worth in November 2018 to host their season finale at Texas Motor Speedway. If you’re an aviation nerd and haven’t witnessed this event, I highly recommend you get some tickets and come … Continue reading
► I’m Lorenzo Alba and This Is How I Mesh
    6 Sep, 2018
Hello! I was raised in Richardson, Texas. Although my home town is known as a hub for companies in the information technology and electronics industries, my father’s background as a mechanic resulted in me being raised as a grease monkey. … Continue reading
► Mesh Resolution: Bringing Order to Coarse, Medium, Fine
    5 Sep, 2018
The 2nd AIAA Geometry and Mesh Generation Workshop is taking the qualitative out of mesh resolution by adopting a quantitative definition that will evolve as we move toward the vision of CFD in the year 2030.  When asked about the … Continue reading

F*** Yeah Fluid Dynamics top

► In the latest JFM/FYFD video, we tackle some of the less...
  21 Sep, 2018

In the latest JFM/FYFD video, we tackle some of the less pleasant aspects of summer weather: stopping invasive insects, understanding how plants dispense poison, and looking at the physics behind jellyfish stings. And if you’ve missed any of our previous videos, we’ve got you covered. (Image and video credit: T. Crawford and N. Sharp)

► Although you may not recognize the name, you’ve probably seen...
  20 Sep, 2018

Although you may not recognize the name, you’ve probably seen Kalliroscope (top image), a pearlescent fluid that creates beautiful flow patterns when swirled. This rheoscopic fluid was invented in the mid-1960s by artist Paul Matisse and, over the following decades, became a staple of flow visualization techniques. Kalliroscope contained a suspension of crystalline guanine. Since the crystals were asymmetric, they would orient themselves depending on the flow and, from there, scatter light, creating the beautiful pearlescent effect seen above.

Unfortunately for researchers, the production of guanine crystals was expensive and difficult. The cosmetics industry was their main consumer and over time, they moved toward mica and other cheaper mineral alternatives. The company that produced Kalliroscope gave up production in 2014, leaving researchers scrambling for a suitable alternative.

One contender for a new standard rheoscopic fluid is based on shaving cream. By diluting shaving cream 20:1 with water, researchers are able to extract stearic acid crystals, which form an admirable alternative to Kalliroscope (middle collage). Like Kalliroscope, the resulting fluid is pearlescent and reveals flow features well (bottom two images). Stearic acid crystals are also closer in density to water than guanine, so the fluid remains in suspension far better than Kalliroscope. Plus, the best shaving cream is cheap and widely available, meaning that this is a DIY project just about anyone can do! (Image credits: Kalliroscope - P. Matisse; other images - D. Borrero-Echeverry et al.; research credit: D. Borrero-Echeverry et al.)

► Granular mixtures with particles of different sizes will often...
  19 Sep, 2018

Granular mixtures with particles of different sizes will often segregate themselves when flowing. In this half-filled rotating drum large red particles and smaller white ones create a stable petal-like pattern. As the drum turns, an avalanche of small particles flows down, forming each white petal. When the avalanche hits the drum wall, a second wave – one of the larger, red particles – flows uphill toward the center of the drum. If the uphill wave has enough time to reach the center of the drum before the next avalanche of smaller particles, then the petal pattern will be stable. Otherwise, the small particles will tend to fall between the larger ones, disturbing the pattern. (Image and research credit: I. Zuriguel et al., source; via reprint in J. Gray)

► Inkjet printing and other methods for directing and depositing...
  18 Sep, 2018

Inkjet printing and other methods for directing and depositing tiny droplets rely on the force of gravity to overcome the internal forces that hold a liquid together. But that requires using a liquid with finely tuned surface tension and viscosity properties. If your fluid is too viscous, gravity simply cannot provide consistent, small droplets. So researchers are turning instead to sound waves

Using an acoustic resonator, scientists are able to generate forces up to 100 times stronger than gravity, allowing them to precisely and repeatably form and deposit micro- and nano-sized droplets of a variety of liquids. In the images above, they’re printing tiny drops of honey, some of which they’ve placed on an Oreo cookie for scale. The researchers hope the technique will be especially useful in pharmaceutical manufacturing, where it could precisely dispense even highly viscous and non-Newtonian fluids. (Image and research credit: D. Foresti et al.; via Smithsonian Mag; submitted by Kam-Yung Soh)

► Schlieren photography has an almost magical feeling to it...
  17 Sep, 2018

Schlieren photography has an almost magical feeling to it because it enables us to see the invisible – like shock waves and the tiny currents of heat that rise from our skin. But it can also reveal new perspectives on things that aren’t invisible. Here we see soap bubbles viewed through the lens of a schlieren set-up. Schlieren is sensitive to small changes in density, so instead of appearing in their usual rainbow iridescence, the bubbles look glass-like and filled with tiny currents and bubbles. What we’re seeing are some of the many tiny flow variations across the surface of a soap bubble. They’re driven by a combination of forces – gravity, temperature, and surface tension variations, to name a few. Seen in video, you can really appreciate just how dynamic a thin soap film is! (Image credit and submission: L. Gledhill, video version, more stills)

► There is a constant drama playing out overhead, though most of...
  14 Sep, 2018

There is a constant drama playing out overhead, though most of us do not take the time to watch. Fortunately, a few, like Blaž Šter, do and make timelapse videos that allow us to enjoy hours of atmospheric drama in only a few minutes. This timelapse shows a cloudy and rainy mid-July day in Slovenia, where an unstable atmosphere leads to turbulent and dramatic clouds. In an unstable atmosphere, it’s easier for vertical motion to take place between altitudes. For example, a parcel of warm air displaced upward will continue to rise because it will be lighter and more buoyant than the surrounding air. This is key to the strong convection that can generate thunderstorms.
(Image and video credit: B. Šter, source)


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

► TestCase --> compressible::turbulentTe mperatureCoupledBaffleMix ed
  22 Sep, 2018
Following tutorial shows the usage for:
for thin layers of materials, that can't be modeled with meshing process.
The test case is for solid regions only.

Compatible with OF5.x 6.x dev

./run to select for 1D or 2D not conformal mesh
./clean to reset case folder
Attached Files
File Type: gz thermalBaffles_BC_CONDUCTION.tar.gz (52.1 KB, 3 views)
► Why I contribute to the OpenFOAM forum(s), wiki(s) and the public community
  24 Aug, 2018
Here is the TL;DR bullet list:
  1. To help others (and possibly gain IRL Karma points).
  2. To train/gain/increase/improve my experience, knowledge, patience and user support skills, in a way that no PhD or academic course could ever do.
  3. To feel accomplished, in a way similar to playing video games:
    • If I can do this task, I can do other tasks as well. If not at the moment, I'll try and try again until I can overcome this (semi-artificial) hurdle.
And if you can find the time and are willing to do so, please do your best to also help others as well!


Longer explanation:

I've been meaning to write this blog post for years and for this past week or so I've been getting back to try and help more people here at the OpenFOAM forum (and sub-forums), writing at, as well as tackling OpenFOAM various bugs reported by the community here on the forum, at the OpenFOAM Foundation bug tracker and one or two at ESI-OpenCFD's bug tracker.

So... Why do I contribute so much to the public OpenFOAM community so much?

1. To help others
I've been taught since young that helping others is pretty much a must when living in a family, community and society, specially since not doing so usually results in the demise of said group. Which has many times left me wondering: "Why isn't common sense? Why can some people be so selfish and egocentric and not see the benefits of helping others?"

It feels that in my lifetime I've seen more people acting selfishly, rather than cooperatively. It's part of the human nature, survival of the fittest, but it can also result in the termination of whole groups of people, simply because they didn't choose to cooperate. Several religions and cults have appeared due to this very reason, to combat the selfish behavior of humans... but that is a topic for a whole other blog post...

Back on topic, helping publicly people the forums, wikis and so on, is a way to productively assist many with one effort at a time. Furthermore, eventually the residual cost per person converges to zero, with the increase of the number of people helped by each post; let me explain this with my favorite example:
  1. Look for a thread or post that describes a problem and assess if I can help.
  2. Once found:
    1. Either take the time necessary to solve the problem and write the solution, along with auxiliary material if necessary;
    2. Or write what I know at the moment, preferably pointing the person in the right direction with one or more links as well.
  3. It may cost me 10-20 minutes to answer each post (yes, that's roughly my average), and sometimes take 2, 4, or even 8 hours to solve an issue.
  4. Multiply the time spent on that written post by a cost factor, e.g. if the cost on my part was 20 Euros per hour, this means that spending 8h during a weekend would have cost me 160 Euros in money not earned.
  5. However:
    • If 160 Euros are divided by 100 people that I helped, that means that it cost around 1.6 Euros per individual.
    • If 160 Euros are divided by 10000 people that I helped, that means that it cost around 0.016 Euros... 1.6 cents/person.
    • If 160 Euros are divided by 100000 people that I helped, that means that it cost around 0.0016 Euros... which rounds out to 0.00 Euros/person.
  6. So from a pure discretization perspective (and ironically associated cumulative errors), it will eventually have cost me 0.00 Euros to help others during those 8h.
As a counter example: If I help only a single person on a private message, I lost X.Y Euros/hour it took me to answer that single person... which from my perspective, is rather selfish of that person ;)

As for In Real Life Karma points: Quoting from
Good intent and good deeds contribute to good karma and future happiness, while bad intent and bad deeds contribute to bad karma and future suffering.
Referring to it as points is just a nice discrete way of thinking about it :D

Which leads me to my next reason(s):

2. To train/gain/increase/improve

Knowledge and experience must always be earned. A person may be born with a gift, but honing that gift is always a must, in order to properly make it use. Countless books, video games, TV series, cartoons/anime, movies, and so on, teach us that knowledge and experience must be earned. Simply take a look at how successful people achieved what they have... always through earning knowledge and experience and putting it to good/profitable use... never taking things for granted.

So to train/gain/increase/improve my own experience, knowledge, patience and user support skills, I've taken the liking to do it here on the forums, wikis, issues trackers and so on. From my own past experience, this is something that no PhD or academic course could ever do, if I didn't go and explore on my own, beyond what I was taught.

And there have been countless times where by helping someone during a weekend, it gave me the answers I needed the very next work week at the office: bug X, Y or Z was what blocked us from solving a problem that had seemed to appear from nowhere... but I had already tripped over the solution during the weekend, so I had my cost recovered in little to no time to solve the problem at that moment.

BUT, keep in mind that the answers do not come easily for me either. I have to solve the problem posted in front of me, which means that I will have to grind the problem on my own, dividing the issue in as many smaller parts as necessary and solving one issue at a time. The difference is that I already have the experience needed to tackle many of those smaller problems.
And then I have to do what many engineers/scientists never do: to write down the solution, step by step, and not simply just the end results and subsequent conclusions. That takes time as well! It's not just a matter of prescribing some pills to make a headache go away... as some forum members think this is all about ;)

That said, even after over 10000 posts here at CFD-Online, this does not mean that I am now a "CFD expert". This on its own is not enough to learn all what needs to be learned... it only gave me the capability to overcome hurdles that also presented themselves at work, because learning how to do/use CFD is a lifetime experience and some details can only be learned by studying books and being taught by the experts themselves, in live sessions or webinars, not simply on a forum or a mere wiki page or two.

3. To feel accomplished

Tons of self-help books will tell this in one way or another: to gradually feel better about yourself, you need to solve one small task at a time. Then once you feel accomplished with that task, you take another step to the next task. Let your brain emit the rewarding chemicals that make you feel good and better about yourself.

Which is also what video games can give you one way or another: feel achievements were attained whenever you overcome artificial hurdles. And if you do like playing video games, then you can think that by helping others in real life you are earning Karma points in your real life... and reaching higher experience levels faster than just by doing things on your own.

But do not chew more than your mouth can handle... which you will also only be able to diagnose if you try things for yourself, otherwise you will not know how small should be the tasks that you can do or not.

That said, always challenge yourself and learn what are your limits and study how to overcome them. Although, you know, this is something you were meant to be taught in school since the beginning...

However, as I've learned over the years: I can't solve everything nor reach everyone. I'm only one person and the funny thing is that even if I were to abuse time traveling technologies in the current Earth time line, I would not survive in my own time line enough years to help everyone.
So please, if you can find the time and if you have reached this part of this long blog post, please do your best to also help others. Even if it doesn't look like it, the experience you will gain from it can eventually benefit you.
► Reference Thesises
  23 Jul, 2018
► Baffles01 - createPatch & createBaffle
  21 Jul, 2018
Objective: test cases for using following dicts:
  • createPatchDict
  • createBafflesDict

Preliminary steps:
  1. Run the Salome script for the geometry and surface mesh generation
  2. Export walls, inlet outlet inside cad/stl as STL (ASCII)
  3. Run the utility buildRegionSTL, it will create the constant/triSurface folder with buildRegionSTL.stl
  4. Export the interna mesh as interna.stl inside constant/triSurface

************Set up the SHM dict file for create Patch (highlights) ********
        type triSurfaceMesh;
        name regionSTL;
                name inlet;

                name outlet;

                name walls;


        type triSurfaceMesh;
        name interna;



            level (0 0);
                inlet { level (1 1); patchInfo {type patch;}};
                outlet { level (1 1); patchInfo {type patch;}};

            level (3 3);
            faceType baffle;  
            faceZone interna;



    allowFreeStandingZoneFaces true;

snapControls {    .....     }

addLayersControls {    .....     }

  meshQualityControls {    .....     }
************Set up the SHM dict file for create baffles (highlights) ********
geometry { same as above };


       same as above 

            level (3 3);
            faceZone interna;

    allowFreeStandingZoneFaces true;

snapControls { same as above };

addLayersControls { same as above };

meshQualityControls { same as above };
**************createPatchDict set up*********************
pointSync true;//false;

// Patches to create.
        name baffle;
        patchInfo {type baffle;}
        constructFrom patches;
        patches (interna interna_slave);
**************createBaffleDict set up*********************
pointSync true;//false;

// Patches to create.
internalFacesOnly true;

        //- Use surface to select faces and orientation.
        type        faceZone;
        zoneName     interna;

            type            wall;
                    type            fixedValue;
                    value           uniform (0 0 0);

surfaceFeatureExtract -overite
snappyHexMesh -overwrite
createPatch -overwrite simpleFoam (you have to install pyFoam)
surfaceFeatureExtract -overite
snappyHexMesh -overwrite
createBaffles -overwrite simpleFoam (you have to install pyFoam)
► Utility 01 - buildRegionSTL
  21 Jul, 2018
Following code has to be copied into your ./bashrc on linux system to merge toghether several STL files saved into cad/stl folder.

For more details, have a look to this screen cast

thanks Tobi

function buildRegionSTL() {
    if [ -d "cad/stl" ]
        if [ "$#" -ne 1 ]
        mkdir -p constant/triSurface
        cd cad/stl
        for i in $(ls *.stl)
            sed -e 1c"solid ${i%.*}" $i -i
        rm ../../constant/triSurface/regionSTL.stl -f 
        cat * > ../../constant/triSurface/regionSTL.stl
        cd ../../
    echo "folder cad/stl does not exist"
► Open/Public answer to a recent blog post at LinkedIn
  14 Jul, 2018
This blog post provides a complete answer to Tobias Holzmann's following post at LinkedIn:

This is because LinkedIn has a limited number of characters per comment and it required me to login in order to read the complete post... so I guess it's better that I answer here instead.


Hi Tobi and greetings to all!

Since you mentioned me here, I guess I have to answer here as well ;)

My experience on this is fairly simple:
  1. I rarely login into LinkedIn.... so to me this channel of communication is almost cutoff... I login every 3 to 12 months, when and if I find the time. In this case, it seemed at first an emergency, given that LinkedIn would not allow me to read your post without logging in.
  2. Asking me questions about OpenFOAM via email, private messages and other private channels, will be answered to when I have a chance, very likely with a semi-automatic message indicating that:
    1. I can answer public answers for free at - when I find the time to do it.
    2. We can provide paid support at the company where I work at, if you need faster/quicker/better/more-complete answers.
  3. In the past I did end up getting pulled into helping around 1 individual per year with their thesis, due to several reasons:
    1. because there apparently was no one else that could help them;
    2. because there was no support where they were studying at;
    3. because I was able to gain experience in helping them;
    4. and more importantly, because I somehow managed to find the time to help them.
For better or for worse, life has been catching up to me and my free time, therefore my presence online has been reduced to a frequency similar to catching a cold...

I've been meaning to write a blog post on what motivates me to help people online with OpenFOAM et al... with luck I get inspiration to write it sometime this year, to give a bit more clarity on this and hopefully inspire more people to help online, instead of waiting for a core few to answer all questions :(

Best regards,

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
  10 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
  18 Jun, 2017

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

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

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

Thanks for reading.

► Your In-House CFD Capability
  15 Feb, 2017

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

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

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

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

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

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

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

► Avoid Testing Pitfalls
  24 Jan, 2017

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

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

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

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

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

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

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

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

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

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

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

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

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

► Corvette C7 Aerodynamics
    7 Jan, 2017

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

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

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

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

The results for the Corvette C7 model  are summarized below:

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

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

Run time: 7 hours

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

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

Top View

Side View

Bottom View

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

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

Thanks for reading.

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

CFD and others... top

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

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

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

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

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

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

Happy 2018!     

► Sub-grid Scale (SGS) Stress Models in Large Eddy Simulation
  17 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

► Near-instant Simulations Speed Up Prototyping and Additive Manufacturing Labs
  21 Sep, 2018
Additive manufacturing makes designing a lattice structure for a bracket possible. However, does it have a chance of success? Simulation can tell you.

Additive manufacturing makes designing a lattice structure for a bracket possible. However, does it have a chance of success? Simulation can tell you.

Traditional prototype techniques take weeks and cost a considerable amount of a product development budget.

It’s true that additive manufacturing has reduced the time and cost to build prototypes. However, the near-instant simulation capabilities of ANSYS Discovery Live can speed up prototyping labs even more.

“Traditional simulation tools are hard to use, take a long time to produce results and are too expensive to be used on a regular basis,” says Tejas Rao, manager of ANSYS’ Discovery Live Technical Team.

“Discovery Live, however, offers instantaneous real-time simulation in an extremely easy-to-use multiphysics interface,” Rao adds. “It also includes integrated geometry manipulation. This means you can do simulation on your own in seconds.”

So how can Discovery Live help a prototyping lab?

Imagine your design team sends you five designs to print. Printing all five is a waste. Instead, import the designs into Discovery Live. In seconds, you can narrow down which designs are worthy of prototyping.

Tip: Test Lattice Structures in Simulation before Additive Manufacturing

Simulation of this lattice part gives engineers an idea how it will perform before printing and prototyping it.

Simulation of this lattice part gives engineers an idea how it will perform before printing and prototyping it.

Additive manufacturing makes it possible to drastically reduce a product’s weight by creating unique shapes and forms.

Optimizing these designs through lattice structures — and subsequent testing of their performance — is an important step that is often done by trial and error.

Assessing a lattice’s performance using traditional simulation takes a long time to set up and solve. Additionally, spending time printing models with lattice structures without having an idea of the design’s performance and likelihood of success could be a waste of time.

Alternatively, simulating a model containing a lattice using Discovery live will quickly give you an idea of how it will perform. “The goal here isn’t the highest accuracy possible,” says Rao. “The goal here is to understand the basic flow field.”

Understanding that flow field will help the prototyping lab assess the likelihood of success for a design before they print the prototype. They can then concentrate on printing the lattices they know have a chance of success.

Watch ANSYS Discovery Live for Additive Manufacturing Applications to learn more ways to accelerate your prototyping using simulation software and PNY’s NVIDIA graphics processing units (GPU).

The post Near-instant Simulations Speed Up Prototyping and Additive Manufacturing Labs appeared first on ANSYS.

► Simulation Compares How HUD Housings Affect Image Quality
  20 Sep, 2018
HUD informs drivers of their speed, the speed limit, next turn and more — without forcing them to move their eyes off the road.

HUD informs drivers of their speed, the speed limit, next turn and more — without forcing them to move their eyes off the road.

Any fan of Iron Man knows the power of an effective head-up display (HUD). But you don’t need to be fighting Thanos to benefit from a HUD.

In fact, HUDs can become a significant enabler of advanced driver assistance systems (ADAS) in the automotive industry.

An effective HUD can inform a driver without forcing them to move their eyes off the road. It can show:

  • Car speed.
  • The road’s speed limit.
  • The next turn.
  • Traffic.
  • Lane suggestions.

For engineers, the challenge is to design a display that doesn’t force the driver to change focus. As a result, the information must remain legible under all road conditions. Simulation can help engineers design HUDs for any condition.

Simulation Optimizes a HUD’s Housing for a Crisp Image

HUDs can become large. The larger they are the more light can sneak in and affect the perceived quality of the image.

HUDs can become large. The larger they are the more light can sneak in and affect the perceived quality of the image.

Engineers achieve an optimal projected distance for the display by mounting a considerably large device on the dashboard.

The problem is that the larger the device, the greater the unwanted light that manages to creep into the optics.

This unwanted light tends to scatter and cause light artifacts that render the HUD image fuzzy. This fuzzy image could affect the perceived quality of the HUD device.

To address this issue, HUD housings, glare shields and light traps are used to reject these stray beams of light. Optimizing these stray light rejection methods using trial-and-error would likely become too costly. ANSYS SPEOS software can be used to assess these traps and shields during early product development.

HUD housing comparison using classic black plastic (left) and ultrablack coating (right).

HUD housing comparison using classic black plastic (left) and ultrablack coating (right).

As an example, simulation results show that using a classic black plastic housing could still create a fuzzy HUD image.

However, simulation shows that using an ultrablack coating creates a much crisper image.

There are many other factors that can affect the image of a HUD. To learn more, read ANSYS SPEOS Capabilities: HUD and Analysis.

Or, watch the webinar Take the lead on the HUD revolution: windshield as a key optical during its Sept. 27, 10 a.m. (CEST) or 5 p.m. (CEST) timeslot.

The post Simulation Compares How HUD Housings Affect Image Quality appeared first on ANSYS.

► How to Automate Parameter Changes in ANSYS Discovery Live
  19 Sep, 2018
Parameter studies in Discovery Live automates design point creation. The geometry and simulations are then automatically generated in real-time.

Parameter studies in Discovery Live automates design point creation. The geometry and simulations are then automatically generated in real-time.

Real-time design analysis in ANSYS Discovery Live is now more automated. Previously, you needed to manually change each parameter to see a new simulation result.

When you had multiple parameters to adjust or were curious about all possible permutations, the process of manually iterating through the parameters could be time-prohibitive.

This is where Discovery Live’s parameter study feature comes into play. It allows you to rapidly explore numerous design possibilities by automatically cycling though geometric or simulation parameters.

Setting up a Parameter Study in Discovery Live

The parameter study’s interactive chart plots every design point in real time.

The parameter study’s interactive chart plots every design point in real time.

The new feature includes a menu that lets you decide which parameters to adjust and how you want to adjust them.

This menu lets you set each parameter’s:

  • Minimum value.
  • Maximum value.
  • Step size between each iterated value.

You can then select a target output to study based on the changing parameters.

The software will then create a 3D representation of each iterated design, instantly solve the simulation for each iteration and plot the targeted output in an interactive chart. This allows you to determine which variables and values have the greatest effect on the desired outcome.

Interactive charts can quickly reveal trends in the data.

Interactive charts can quickly reveal trends in the data.

The interactive chart can also help you better understand the trade-offs between design goals and the input parameters.

For instance, say you are designing a heat sink. You will need to decide how to make a trade-off between costs and increasing the heat sink’s surface area and resultant material consumption.

Solving problems — like this heat sink example — requires data to adequately analyze all design variations.

Using parameter studies, you can gather that data to determine which variables have the greatest impact on your design goals. This information is critical to finding an optimal design.

To learn how to set up a parameter study in Discovery Live, attend the webinar: ANSYS 19.2: Discovery Live Update.

The post How to Automate Parameter Changes in ANSYS Discovery Live appeared first on ANSYS.

► ANSYS 19.2 Speeds Your Time to Market with New CAE Workflows
  18 Sep, 2018

Engineers are under pressure to optimize designs during ever-shrinking development cycles. In response, computer-aided engineering (CAE) tools — like the latest enhancements in the ANSYS 19.2 release — now facilitate faster and easier-to-use workflows.

New ANSYS Fluent Task-based Workflow with Mosaic-enabled Meshing

A Mosaic mesh of an F1 wing connects hexahedral elements in the bulk and isotropic elements in the boundary, using polyhedral elements.

A Mosaic mesh of an F1 wing connects hexahedral elements in the bulk and isotropic elements in the boundary, using polyhedral elements.

Engineers using ANSYS Fluent can now create more accurate computational fluid dynamics (CFD) simulations.

Fluent’s single-window, task-based workflow for watertight geometries dramatically reduces the time to set up and run CFD simulations.

This new workflow incorporates best practices to help prevent engineers from second-guessing themselves as they set up complex CFD models.

For instance, the workflow guides the engineer through the simulation process and best practices. It does this by reducing the engineer’s options to those that are relevant at each point in the process. Engineers will see only the turbulence models, meshing options and inputs that are relevant to the task at hand.

CFD engineers should also take note of new Mosaic meshing in ANSYS 19.2. This technology automatically combines any boundary layer mesh with high-quality polyhedral meshes for fast and accurate flow resolution.

In a benchmark study, the Mosaic mesh had fewer cells, required one-third the memory and delivered a solution two-times faster than a polyhedral mesh of similar accuracy.

ANSYS 19.2 Reduces Time to Market for Designers

A parameter study that is optimizing biker separation to reduce drag.

A parameter study that is optimizing biker separation to reduce drag.

ANSYS Discovery Live has also seen a significant workflow improvement thanks to 19.2’s real-time parameter studies.

While Discovery Live has always been lightning fast with its simulations, all parameter changes were made manually. These manual parameter inputs slowed down development.

Now, design engineers can automate the exploration of their products’ design space using parameter studies. With this information, they can quickly assess the trends and trade-offs between design goals.

ANSYS 19.2 also advances the workflow for physics-aware meshing in ANSYS Discovery AIM. This improvement identifies high-stress concentrations faster than before.

Safety Assessment Workflows for Autonomous Vehicle Development

VREXPERIENCE gives engineers the ability to validate autonomous vehicles and ADAS features in a customizable virtual environment over millions of miles.

VREXPERIENCE gives engineers the ability to validate autonomous vehicles and ADAS features in a customizable virtual environment over millions of miles.

The release offers a series of tools and workflow improvements for to ensure the safety of advanced driver-assistance systems (ADAS) and autonomous vehicles.

For instance, the recent acquisition of OPTIS now brings ANSYS VRXPERIENCE into our portfolio.

With this tool, engineers can validate automotive safety features in a virtual world.

VREXPERIENCE offers engineers an expedient way to test safety features for autonomous vehicles over millions of virtual miles. These virtual drives can be customized to various roads, environments, weather, traffic and pedestrian conditions.

The release also introduces ANSYS SPEOS, a tool that will help engineers set up optical simulations. It empowers engineers to test the optical performance of LiDAR, lights and cameras within a system.

This means that engineers can use SPEOS and VREXPERIENCE to assess the optical sensors of their autonomous vehicles within a simulated world.

Workflow improvements to ANSYS medini analyze will also expediate the development of autonomous vehicles. These enhancements will help semiconductor companies ensure the safety of ADAS and autonomous systems to meet ISO 26262 Standards. These upgrades also streamline functional safety analyses, so semiconductor companies can get to market in half the time. Engineers using medini analyze will also be able to export a required safety analysis.

Exporting a safety analysis that is adapted to a system using medini analyze

Exporting a safety analysis that is adapted to a system using medini analyze

ANSYS 19.2 also includes additions to the ANSYS SCADE Suite’s embedded software design workflows. These tools will make it faster to develop and verify safety-critical embedded code. This is key for any engineer designing a complete safety system for ADAS or autonomous vehicles.

In addition, the release streamlines SCADE’s connectivity to both Simulink and Jama software.

Workflow Upgrades for Multiphysics Simulations Increase Speed and Usability

Another speed boost is available for engineers working on two-way coupled simulations.

ANSYS 19.2 introduces System Coupling 2.0, which improves data mapping and co-simulations. These upgrades will better leverage the speed of high-performance computing (HPC).

The release also enhances text-driven workflows that control multiphysics simulations. This will make it easier for engineers to start and restart a simulation that assesses the interactions between a fluid and a structure.

Structural Workflows for Design Optimization

Turbine designed using inverse analysis

Turbine designed using inverse analysis

ANSYS 19.2 offers workflows to help optimize structural designs.

One example is the inverse analysis. This tool helps engineers determine the unloaded shape of a component so that, when loaded, they get the shape they expected.

Engineers can also take advantage of a new material design feature. This tool allows engineers to create detailed models of materials and calculate equivalent properties for larger-scale simulations.

Additive manufacturing (AM) experts will benefit from ANSYS 19.2’s new physics-driven lattice optimization function. Additional loading options and manufacturing constraints for topology optimization are also included in the release.

Faster Electronic Design Workflows

Finally, the release offers workflow enhancements to the electromagnetic suite. The suite now includes advancements to its multichannel radar system simulations. This upgrade comes in the form of a lightweight geometry modeler that hastens meshing and simulation processing.

Other electromagnetic improvements in 19.2 include:

  • New capabilities in ANSYS Icepack to compute the thermal impact of multiple electromagnetic loss connections.
  • A stackup wizard in ANSYS SIwave that allows for easy definition and exploration of printed circuit board (PCB) stackup layers and impedances.

To learn how to maximize your workflows, find out all that’s new in ANSYS 19.2.

The post ANSYS 19.2 Speeds Your Time to Market with New CAE Workflows appeared first on ANSYS.

► Simulations Prove Composites Can Increase an Aircraft’s Payloads and Service Life
  17 Sep, 2018
Streamline simulations from ANSYS Fluent simulate the aerodynamic loads on composite wings.

Streamline simulations from ANSYS Fluent simulate the aerodynamic loads on composite wings.

Composite materials have a significant history with the aerospace industry due to their light weight and substantial strength.

However, you will still see a lot of metal wings flying around.

Metal wings have a limited service life — often as short as six to eight years — due to corrosion and fatigue.

And, during this period, the wings will require regular maintenance.

When the wing inevitably needs to be replaced, the cost can be so large that many airliners will scrap the whole vehicle.

Aero Composites aims to replace many of these metal wings with composite counterparts.

“The ANSYS Startup Program has helped us to design and optimize lightweight composite wings that increase payload capacity, while reducing weight and improving the overall stiffness of our aircraft,” says Dan Retief, chief engineer at Aero Composites. “Since composite parts do not suffer from corrosion, the aircraft will also requires less maintenance.

“After just 12 months in the ANSYS Startup Program, we can deliver an increase of 14 percent in payload capacity in our designs — by weight reduction — compared with all-metal aircraft,” adds Retief. “In the future, we expect to be able to deliver an increase of up to 36 percent.”

How Aero Composites Optimized Its Aircraft Designs

Composite PrepPost shows the fiber direction (green arrows) on a rib flange.

Composite PrepPost shows the fiber direction (green arrows) on a rib flange.

Aero Composites’ workflow starts in Creo Parametric 3D modeling software.

The geometry is then brought into ANSYS Workbench — via Discovery SpaceClaim to check for part connectivity.

Aero Composites defines all the laminate details of the composites using ANSYS Mechanical and Composite PrepPost.

Next, Aero Composites uses ANSYS Fluent to calculate the pressure distributions. They also apply these loads to static structural simulation within Mechanical.

Finally, Aero Composites uses Workbench to set up a parametric study to assess different load cases, speeds, angles of attack and environmental conditions.

“Simulations have delivered many significant insights – especially in furthering our understanding of out-of-plane loads,” says Retief. “They helped us avoid delamination and increase the service life of key components.”

Simulations Help Aero Composites Certify their Aerospace Designs

Simulations have proven Aero Composites designs can increase payload capacity by 14 percent.

Simulations have proven Aero Composites designs can increase payload capacity by 14 percent.

“If we were only conducting physical tests, the cost of failure to our small startup business would be enormous,” says Retief.

Currently, Aero Composites is using its simulations to achieve certification with the Civil Aviation Authority.

These simulations will support the company’s required due diligence for proving that its designs can handle all the required load cases before moved onto physical tests.

“After correlating our simulations to this physical data, we can then certify new designs based almost entirely on simulation,” explains Retief. “With a qualified simulation model, it becomes possible to make modifications to our certified designs and certify those changes using simulations alone.”

As each physical test could cost upward of $200K, simulation will save Aero Composites a lot of money.

To learn about how simulation can save your startup costs on physical testing, check out the ANSYS Startup Program.

The post Simulations Prove Composites Can Increase an Aircraft’s Payloads and Service Life appeared first on ANSYS.

► Engineering Colleges Need Simulation in the Classroom
  13 Sep, 2018
A class within Western New England University is learning how to use simulation software.

A class within Western New England University is learning how to use simulation software.

Engineering colleges need to get on the simulation bandwagon.

Simulation is a necessity for the automotive, aerospace, consumer goods, electronics, industrial and even health care industries.

So, why do so many engineering colleges not teach the basics of finite element analysis (FEA) or computational fluid dynamics (CFD) software?

I cannot stress enough the importance of learning and understanding simulation tools based on my experience as a recent mechanical engineering grad.

When facing the industrial job market, I can use the simulation skills I picked up from ANSYS to stand out among thousands of applicants. I hope engineering colleges understand the importance of bringing simulation into the curriculum. Students need to generate an industry ready talent for these tools.

How Students Can Learn Simulation in an Engineering College

Deformation of a wall mounting bracket.

Deformation of a wall mounting bracket.

It doesn’t take much to get engineering college students interested in simulation.

Perhaps an exercise as simple as calculating the deformation of a wall mounting bracket by hand and then again through simulation?

Once these students see the power and speed of simulation it could pique their interest.

This can only be done when professors take the time within their lectures to seed the idea that simulation can solve problems. This could trigger students to probe into the meaning of those colorful pictures — it can help them explore simulation resources and work on it as a new skill.

From here, the engineering student just needs to choose a simulation software. I chose ANSYS. ANSYS gave me — and any other students — the ability to:

Effectively, students have access to similar or equivalent resources to those they’d be introduced to on the job. However, instead of learning these lessons during a three-month employment probation, they are learning it in the safety of their engineering college.

ANSYS played a vital part in educating me about the simulation industry and shifted my focus toward FEA and CFD. I suggest students start with a self-paced online course like A Hands-on Introduction to Engineering Simulations. It is sponsored by ANSYS and taught by Dr. Rajesh Bhaskaran from Cornell University.

How Simulation Gives Engineering College Students a Leg-up in Industry

The simulation tools I used improved my studies, capstone projects and early career.

For example, while enrolled in an engineering college, I helped an athletic firm improve the strength and durability of a patented lacrosse stick. I then worked with FloDesign Sonics to develop ultrasonic cell therapy devices to concentrate, wash and separate distinct types of cells.

Generally speaking, most engineering  capstone projects can benefit from insight of simulation in the verification and validation stages. Familiarity with simulation can help engineering college students gain an edge over their peers because they’ll have a mathematical and visual understanding of simulation. This understanding can add value to their project and curriculum vitae (CV).

Any academics or students at an engineering college looking to follow my lead should access ANSYS’ Free Student Software Downloads.

The post Engineering Colleges Need Simulation in the Classroom appeared first on ANSYS.

Convergent Science Blog top

► CONVERGE Workflow Tips
  20 Aug, 2018

As a general purpose CFD solver, CONVERGE is robust out of the box. Autonomous meshing technology built into the solver eliminates the meshing bottleneck that has traditionally bogged down CFD workflows. Despite this advantage, however, performing computational fluid dynamics analyses is still a complex task. Challenges in pre-processing and post-processing can slow your workflow. To streamline the simulation process, CONVERGE CFD software includes a wide array of tools, utilities, and documentation as well as support from highly trained engineers with every license.


  • Although you do not have to create a volume mesh, your surface geometry must be watertight and meet several quality standards related to triangulation and normal vector orientation. CONVERGE Studio includes several native surface repair tools to quickly detect, show, and resolve these issues. With an additional license for the Polygonica toolkit, you can leverage powerful surface repair capabilities from within CONVERGE Studio.
  • For engine simulations, a popular acceleration technique is to use a sector (an axisymmetric geometry representing a portion of the model) instead of the full geometry. In CONVERGE, the make_surface utility allows you to quickly create a properly prepared sector geometry based on the piston bowl profile and just a few more geometry inputs. CONVERGE Studio includes a graphical version of this tool.
  • With any CFD software, the multitude of input parameters to control the complex physical models can be overwhelming. In CONVERGE CFD, we provide several checks to help you validate your case setup configuration before beginning a simulation. In CONVERGE, run the check_inputs utility to write information about missing or improperly configured parameters to the terminal. In CONVERGE Studio, you can use the Validate buttons throughout the application to validate input parameters incrementally as you configure the case. Additionally, the Final Validation tool examines the geometry and case setup parameters and provides suggestions for anything that may need to be revised.
  • A staple of the CONVERGE feature set is the ease with which you can simulate complex moving geometries. One requirement is that boundaries cannot intersect during the simulation. There are several ways to verify that your setup meets this requirement. Running CONVERGE in no hydrodynamic solver mode does not solve the spray, combustion, and transport equations. Instead, this type of simulation checks surface motion and grid creation. In CONVERGE Studio, use the Animation tab of the View Options dock to preview boundary motion and check for triangle intersections at each step of the motion. 
  • Many complex engine, pump, compressor, and other machinery simulations employ the sealing feature to prevent flow between regions at various times during a simulation. To test your seal setup, run the CONVERGE sealing test utility by supplying the check-sealing argument after your CONVERGE executable. This command uses a simplified test with only a single level of cells and most options (including AMR, embedding, sources, mapping, events, etc.) automatically turned off.
  • Full multi-cylinder simulations provide accurate predictions for fluid-solid heat transfer, intake and exhaust flow, and other important engine design parameters. Setting up the multiple cylinder geometries and timing can be a frustrating exercise in bookkeeping. The Multi-cylinder wizard in CONVERGE Studio makes this process painless. The wizard is a step-by-step tool that guides you through the process of configuring cylinder phase lag, copying geometry components for additional cylinders, and setting up timing of events such as spark ignition. After your configuration is complete, the wizard provides a quick reference sheet that catalogs the salient details for each cylinder. 
  • Because surface triangles cannot intersect during a CONVERGE simulation, valves (e.g., intake and exhaust valves in an IC engine) must be set to a minimum lift value very close to the valve seats but not technically closed. CONVERGE Studio includes a tool to automatically and quickly move the valves to this position based on profiles of intake and exhaust valve motion.
  • In compressor simulations, the working fluid is often far from an ideal gas. In addition to multiple equation of state models in CONVERGE, you can directly supply custom fluid properties for the working fluid. CONVERGE reads properties such as viscosity, conductivity, and compressibility as a function of temperature from supplied tabular data, obviating the need to link CONVERGE with a third-party properties library.
  • As CONVERGE is a very robust tool, you can use it for many different types of simulations: compressible or incompressible flow, multiphase flow, transient or steady-state, moving geometry, non-Newtonian fluids, and much more. Each of these regimes and scenarios requires you to configure relevant parameters. CONVERGE Studio includes a full suite of example cases across a range of these regimes including IC engines, compressors, gas turbines, and more. It is as simple as clicking File > Load Example Case to open an example case with Convergent Science-recommended default parameters for the given simulation type. You can use the example cases as starting points for your own simulations or run them as-is while you learn to use CONVERGE. 


  • The geometry triangulation for a CONVERGE simulation may differ from that for a finite element analysis (FEA) simulation because the FEA geometry may have higher resolution in areas most relevant to the heat transfer analysis. CONVERGE includes an HTC mapper utility that maps near-wall heat transfer data from the CONVERGE simulation output to the triangulation of the FEA surface. That way, you can iterate between the two simulation approaches to understand and optimize designs.
  • CONVERGE Studio includes a powerful Line Plotting module to create two-dimensional plots. In addition to providing a high level of plot customization, the module is designed to plot some of the two-dimensional *.out files unique to CONVERGE. Also, you can use the Line Plotting module to monitor simulation properties such as mass flow rate convergence in a steady-state simulation. 
  • One of the post-processing tools available in CONVERGE Studio is the Engine performance calculator. This tool automatically calculates engine work and other relevant engine design parameters for 360 degree or 720 degree ranges from CONVERGE output and the engine parameters in your case setup. The results are collated in a table so that you can easily export them to a spreadsheet.


  • Several case setup tutorial video series on the Convergent Science YouTube channel provide step-by-step walkthroughs of full case setups. Refer to these for information on surface preparation, case setup, simulation, and post-processing of some basic CONVERGE example cases.
  • On our CFD Online support forum, you can interact with other CONVERGE CFD users and our knowledgeable and approachable support team for assistance.

Performing CFD analyses can be difficult due to the number of unknowns, uncertainty of boundary conditions, and complexity of flows. CONVERGE CFD helps you by removing the necessity of meshing and giving you auxiliary tools to simplify your workflow.

► Machine Learning for Automotive Engine Design
    7 Aug, 2018

In the last five years, “machine learning” has become a veritable buzzword. From applications as diverse as traffic forecasting and the virtual assistant on your smartphone to genome sequencing, researchers employ machine learning across a broad array of fields to improve predictions based on big datasets.

Beyond adding convenience to everyday life, machine learning can contribute to technology development as well. In a recent collaboration between Argonne National Laboratory, Aramco, and Convergent Science, Moiz et al. applied machine learning techniques to automotive engine research, enhancing computational fluid dynamics (CFD) studies performed in CONVERGE CFD [1]. Machine learning leverages existing datasets to optimize and predict new designs that have improved performance, higher efficiency, and reduced emissions. In light of market competition and increasingly strict emissions requirements, the union of machine learning and engine CFD is a promising development.

Machine Learning Overview

At a very basic level, machine learning means leveraging data to make accurate predictions. An example of this that we encounter every day is targeted advertising. Marketers use machine learning to take information about our demographics and interests and provide relevant product recommendations. More often than not, these recommendations are startlingly accurate.

The first step in developing a machine learning model is for scientists to collect large datasets. Next, the machine learning model applies computational statistics to the data, detecting relationships between inputs and outputs. This process is known as training the model. To evaluate the accuracy of the model, scientists often supply to the model a test dataset that was not included in the training dataset and examine the accuracy of the predictions. The more accurate the algorithm, the lower the risk of inaccurate predictions. Many machine learning algorithms exist (decision tree, support vector machines, neural networks, etc.), some of which have been in development for decades.

CFD Applications

A popular optimization technique for engine designers is the genetic algorithm (GA). CONVERGE includes such a tool, CONGO, which takes a “survival of the fittest approach” to optimize a design. That is, the method pits individuals (designs) against each other in a population with a set of user-defined parameters that vary. Each individual includes characteristics of the various parameters to optimize, such as combustion phasing, combustion shape design, etc. The goal of a GA study is to optimize a result such as indicated specific fuel consumption, while staying within certain constraints such as emissions or peak cylinder pressure.

By definition, a genetic algorithm study needs to run for many successive generations. One of the primary drawbacks of this technique is that the generations can run for a long time, sometimes in the range of months. This is because most engine CFD simulations require between a day and a week for individual results. Engine researchers often require a faster solution than a GA optimization of CFD. To address this, Moiz et al. combined machine learning with genetic algorithm optimization to quickly develop gasoline compression ignition (GCI) engine designs. The engine analyzed in the work uses a low-octane gasoline fuel in partially premixed compression ignition.

First, scientists ran a large (2048 individual CONVERGE simulations) space-filling design of experiments (DoE) to create a training data set. Since the DoE can be defined all at once, the simulations ran concurrently. With the advent of large HPC clusters like the Mira supercomputer at Argonne National Laboratory, the entire DoE of CFD simulations ran in a few days. The authors also investigated using smaller subsets of the training dataset to see if a less expensive DoE would be sufficient. They found that the learning curves were promising down to a DoE with sample size of 300.

An emerging combustion technology like GCI has ample room for optimization to maximize efficiency and minimize emissions, and computational studies are ideal for this task. In the current work, the authors employed a machine learning genetic algorithm approach to reduce the design cycle for optimizing a GCI engine and overcoming the above-mentioned obstacles. The general procedure is as follows:

  1. Ran over two thousand high-fidelity CFD simulations in CONVERGE to create a large dataset on which to train the machine learning model,
  2. Trained and tested the machine learning model on the CFD data,
  3. Used the machine learning algorithm as an emulator of the design space for optimization to optimize the engine designs.

The machine learning (ML) GA procedure poses a speed advantage over a traditional GA optimization. First, engineers can run the initial CFD simulations in parallel, generating seed data very quickly. Second, the ML GA emulator can evaluate an individual design in a few seconds in comparison with a high-fidelity CFD simulation, which can take around 12 hours on 128 processors.

In a GA optimization with CFD results as the objective function, sequentially running the CFD simulations is a bottleneck in the process. The ML GA approach, however, reduces the time significantly, allowing a full optimization in approximately a day. An additional benefit of this technique is that engineers can use the initial space-filling DoE datasets for future design space interrogation or uncertainty analyses.

Machine learning is a powerful tool which is now becoming ubiquitous in software applications. It is only natural that, when combined with CFD, ML GA methods help designers more rapidly optimize engine efficiency and performance.


[1] Moiz, A., Pal, P., Probst, D., Pei, Y., Zhang, Y., Som, S., and Kodavasal, J., “A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing,” SAE Paper 2018-01-0190, 2018. DOI:10.4271/2018-01-0190

► CONVERGE for Compressors: Proven Tools, New Application
  27 Jun, 2018

Computational fluid dynamics tools such as CONVERGE CFD offer the ability to analyze and optimize compressors without the difficulty and expense (both time and money) of generating and testing physical prototypes.

With CONVERGE, several core technologies make your compressor simulation workflow easier, faster, and more accurate.

AMR Strategy

A staple of the robust feature set in CONVERGE is Adaptive Mesh Refinement (AMR). This feature refines and coarsens the mesh on the fly in response to criteria you specify before starting the simulation. AMR helps maintain resolution in the tight gaps between the moving parts in a compressor. In this way, you can trust CONVERGE to automatically capture relevant flow features.

For compressor simulations, AMR is particularly applicable for resolving flow structures around valves. As there are tight clearances in these small gaps, CONVERGE increases mesh resolution automatically in response to large gradients in velocity, temperature, and other quantities of interest.

Additionally, you can modify the sub-grid scale (SGS) parameter for fine-grain control of the AMR algorithm sensitivity. As shown in the video below, AMR allows you to accurately resolve the jets of fluid traveling through the valve in a reciprocating compressor.

A grid convergence study further demonstrates the advantages of AMR. In this study, we successively refine the grid until quantities of interest reach a converged value (in this example, and as shown in Figures 1 and 2 below, for discharge valve lift and cylinder pressure). One way to perform a grid convergence study is to reduce the size of the base grid (and thus increase the cell count) for successive runs. A better option is to modify the AMR embedding scale and CONVERGE will create finer grids in the vicinity of high gradients, reaching a converged solution faster and with fewer total cells. Table 1 below compares cell count and wall clock time for the base grid and AMR grid refinement studies shown in Figures 1 and 2. Both the finest base grid and the finest AMR level result in a converged solution, but the simulation with AMR takes less time and uses fewer total cells than the simulation with the finest base grid.

Figures 1 and 2: Discharge valve lift and cylinder pressure compared between refined base grid and increased AMR embed scale
Cell count Wall clock time (hrs)
Base grid 1 285,614 0.69
Base grid 2 1,431,153 6.76
Base grid 3 7,577,619 15.80
No AMR 285,600 0.78
AMR level 2 670,359 2.16
AMR level 3 2,138,322 9.55
Table 1: Cell count and wall clock time for base grid and AMR convergence study

Reed Valve Deformation (FSI)

To further increase the accuracy of compressor calculations, CONVERGE includes fluid-structure interaction (FSI) modeling. This capability allows you to model the interactions between the bulk flow and reed valves (e.g., in reciprocating compressors). This way, you can accurately resolve the physical behavior within the compressor machinery to predict failure points.

The reciprocating compressor shown in the video above employs the 1D clamped beam model in CONVERGE to predict the fluid-structure interaction. Notice how the valve deforms realistically in response to the flow through the valve.

Custom Fluid Properties

In many cases, the working fluid within compressor machinery is far from an ideal gas. In CONVERGE, you can select from several different equation of state models to accurately represent the physical properties of your working fluid. Beyond the ideal gas law, CONVERGE includes cubic models such as Redlich-Kwong and Peng-Robinson to suit your application.

Also, you can directly supply custom fluid properties for the working fluid. Instead of linking CONVERGE with a third-party properties library, you can provide tabular data files that contain the fluid properties. These custom properties include viscosity, conductivity, compressibility, and more as a function of temperature.

For many applications, such as with air as the working fluid, the ideal gas law is an appropriate choice for the equation of state (as shown in Figures 3 – 6 below).

Figures 3 to 6: Examples in which the ideal gas law works well for air

Figures 7 – 10 below compare various fluid properties of supercritical CO2 calculated via several different methods. In these examples, the tabular fluid properties match very closely with NIST data. The Peng-Robinson equation of state model provides the next-best match.

Figures 7 to 10: Comparisons of various EOS and tabular data to NIST data

CONVERGE offers several technologies that address the difficulties of compressor CFD while making your workflow easier and more accurate. Want to learn more about integrating CONVERGE into into your simulation workflow? Get in touch with us here.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

An early version of the user manual

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

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

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

Our website circa 2009

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

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

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

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

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

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

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

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

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

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

Gas Turbine Relight with GE and Honeywell

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

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

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

CONVERGE-ing in Europe

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

Motor City Hosts Fourth Annual U.S. User Conference

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

IDAJ’s Continued Success in Asia

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

Assorted Consortia

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

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

Convergent Science: India

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

Convergent Science Turns 20

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

2018 and Beyond

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

Numerical Simulations using FLOW-3D top

► Mitigating Total Dissolved Gas at Boundary Dam
  17 Sep, 2018

This article was contributed by Nikou Jalayeri, Water Resources Engineer at HATCH

The Boundary Dam is located on the Pend Oreille River in northeastern Washington. The project consists of a 340 ft. high concrete arch dam, seven low level sluiceway outlets, two high level overflow spillways (Spillway 1 and Spillway 2), and an approximately 1,003 MW authorized capacity powerhouse. The spillway and sluiceway discharge at the Boundary Hydroelectric Development have been shown to produce high total dissolved gas (TDG) concentrations in the tailwater of the spillway and the river reach downstream. Studies were commissioned to determine modifications to the project’s spillway structures to help mitigate this gas production. Resolution of many of the hydraulic design issues for the study relied heavily on the results of numerical hydraulic models. These modifications were constructed and tested in the field. The CFD model that was developed in support of these studies was used to simulate flows through a number of the project’s seven sluice gates and two overflow spillways. This model was also used to simulate the entry and movement of these flows through the project’s downstream plunge pool and powerhouse area.

FLOW-3D model spillway roughness elements
Figure 1. 3-D View of Spillway 1 Roughness Elements

FLOW-3D was selected for the analysis given its ability to simulate free falling jets, and its unique algorithm for simulating air entrainment by turbulence at the free surface. These capabilities make the program very well suited for simulating the varied and complex flow conditions in the project tailrace. The FLOW-3D models developed for the Boundary Dam study have primarily been used to develop an understanding of the governing hydraulic and hydrodynamic processes driving gas exchange in the tailrace of the existing project under spill conditions. In addition, these models been used to develop the designs of structural TDG mitigation alternatives (including estimation of the hydraulic loads expected on proposed appurtenances), and in combination with the TDG predictive model, to predict the TDG performance of proposed TDG mitigation alternatives.

Boundary Dam Hatch FLOW-3D
Figure 2. 3D View of the Unmodified Spillway 1 Jet: 10,000 cfs Flow (left), 13,000 cfs Flow (right)

To do so, representative air bubbles were released on the spillway in the model and tracked as they were entrained into the plunge pool and tailrace, circulated within the plunge pool, and eventually exhausted at the surface.  The model tracked the pressure- and time-histories associated with each of these representative air bubbles.  This data was then used as input to a TDG predictive tool to help predict total dissolved gas production in the tailrace. The overall predictive performance was successfully calibrated and validated to actual prototype (field) TDG data.  TDG predictions were made for the project using a two-step process:  the CFD model was first applied to assess the plunge pool hydraulics and flow patterns, and then the hydraulic output of the CFD model was imported into the Plunge Pool Gas Transfer (PPGT) model, which was developed using Excel.

The model was first run to simulate flow conditions for the existing or base case scenario with flows of 10,000, 13,000, and 20,000 cfs through each of the Project Spillways. The simulated hydraulic conditions for this test were analyzed. Bubble particles were then added to this model, the run was re-started, and the particles were tracked until they were able to reach the surface, and exhaust back into the atmosphere.

Following the base case runs, various CFD simulations were conducted to assess the hydraulic conditions that would result from the introduction of Roughness Elements (REs) on the downstream end of the spillway chute.  The introduction of these REs helps to break up the jet at the end of the chute more quickly and efficiently, accelerating boundary layer growth and resulting in the formation of small “packets” of water entering the plunge pool rather than coherent streams/jets.  This accelerated breakup of the jet will help to reduce overall plunge depths, and reduce gas transfer. Given concerns for potential cavitation damage on the spillway chute floor and on the REs themselves, additional runs were undertaken to test the effect on flow conditions at the REs if a ramp were to be installed immediately upstream of the roughness elements. The Spillway 1 RE geometry is presented in Figure 1.

Modified spillway FLOW-3D design
Figure 3. 3D View of the Modified Spillway 1 Jet : 10,000 cfs Flow (left), 13,000 cfs Flow (right)

The final model results were used to help assess the impact that the addition of these modifications would have on TDG levels downstream of the project under a range of flows.  CFD runs were made with identical flow releases through the spillways under both existing and modified conditions, bubble histories were extracted from the CFD results and input to the TDG predictive spreadsheet model. The results showed that the proposed RE configuration for Spillway 1 is effective at reducing TDG production, but appears to deliver the greatest TDG reduction when operating at a flow of approximately 10,000 cfs. For higher flows, the ability of the roughness elements to break up the jet appears to be reduced, since the jet begins to override the roughness elements. This results in the formation of a more competent jet core that is able to penetrate the plunge pool to a greater depth. Figure 2 illustrates the difference between the baseline (existing) case and the modified Spillway 1 for flows of 10,000 cfs and 13,000 cfs respectively.

► Director of Accounting and Finance
  11 Sep, 2018

The Director of Accounting and Finance is responsible for managing and performing all aspects of the Company’s treasury and accounting functions, financial & tax planning and budgeting. The incumbent reports directly to the President & CEO of the company.

Education, skills and experience

  • Bachelor’s degree or higher in accounting or finance
  • Minimum of 7 years of accounting experience in private industry utilizing GAAP
  • 2+ years of experience managing the accounting function for a company
  • Experience with QuickBooks and/or other ERP accounting systems is required
  • Experience with multiple currency billing processes, available state job training and tax incentive programs, and ERP accounting systems preferred


The ideal candidate for this position will be self motivated, independent, and professional, and will have strong communication and presentation skills, as well as strong persuasion, negotiation and conflict resolution skills.

Principal duties


  • Ensure that accounts payable are processed and paid and accounts receivable are invoiced and collected in a timely manner
  • Process payroll in a timely manner, including payment of payroll taxes, transmission of all benefit deductions and filing of multi state payroll reports as required
  • Maintain the chart of accounts, an orderly accounting filing system and a system of controls over accounting transactions


  • Perform monthly close and issue timely and complete financial statements
  • Recommend benchmarks against which to measure the performance of company operations; calculate and issue financial and operating metrics
  • Manage the production of the annual budget and tracking reports


  • Forecast cash flow positions, related borrowing needs, and available funds for investment to ensure that sufficient funds are available to meet ongoing operational and capital investment requirements
  • Advise management on the liquidity aspects of its short- and long-range planning
  • Oversee the extension of credit to customers


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


Resumes may be submitted via e-mail (, mail (Attention: Human Resources, 683 Harkle Road, Suite A, Santa Fe, NM 87505), or fax (505-982-5551) and should include a cover letter detailing how candidate meets the requirements of the position.

Learn more about careers at Flow Science >

► Developer
    6 Sep, 2018

Flow Science is searching for a CFD Developer to join its Research and Development Department. The work of a Developer focuses on research, development, implementation and documentation of new additions and modifications in the numerical methods and computational physics in our flagship CFD software, FLOW-3D. Continuous, cutting-edge research and development remains the cornerstone of Flow Science’s business model in order to meet the increasing demands of FLOW-3D’s commercial and academic user base and maintain its competitive advantage in the CFD industry.

This position will be focused on FLOW-3D’s civil, hydraulics, water & environmental and coastal engineering markets. Applicants with experience in model development in these areas in academic or industry are encouraged to apply.

Experience, skills and knowledge

  • Good understanding and proven experience in numerical methods used in CFD, including heat and mass transfer, and free surface modeling
  • Good understanding of software development process for large projects and an excellent command of modern FORTRAN
  • Experience in hydraulics, water and environmental engineering, and/or coastal engineering CFD modeling: depth-averaged shallow water equations, waves, sediment transport and scour, multi-phase flows, turbulence and air entrainment, and reaction kinetics
  • Experience in implicit and iterative solvers for incompressible flows
  • Experience in developing and debugging large computational codes
  • Good understanding and proven experience in high performance programming using OpenMP and MPI


A PhD in the discipline of civil engineering, mechanical engineering, applied mathematics or physics with focus on CFD is required.


The ideal candidate for this position will have excellent oral and written communication skills, excellent interpersonal skills, and the ability to work both independently and as part of a team.

Principal duties

  • Research, development, and implementation of new additions and modifications in numerical methods and computational physics to Flow Science’s flagship CFD software, FLOW-3D.
  • Insuring that the new developments correspond to the company’s general development goals and are completed in a timely manner.
  • Insuring that the newly developed algorithms are robust, efficient and of high quality, using consistent programming style throughout, ensuring readability and clarity of the newly added/modified coding.
  • Documenting the new additions and modifications by writing technical notes and amending the User Manual.
  • Maintain accuracy, stability and efficiency of the main solver program, including debugging and finding solutions for issues with the existing and newly added numerical and physical models.
  • Providing assistance to users, primarily through the Company’s support staff, but also directly when necessary.


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


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

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

Learn more about careers at Flow Science >

► Exploring Additive Manufacturing and Microfluidics during my CFD Internship
  29 Aug, 2018

By Ruendy Castillo, Summer Intern at Flow Science

“I’ve never heard of it.” That is what my answer would have been before participating in this internship if I’d been asked what additive manufacturing or microfluidics meant. And truly, I had never heard of such terms before. I still remember on my first day when my supervisor, Paree Allu, started referring to Additive Manufacturing as ‘AM’. I just kept staring at him thinking about the fact that up until that point, I had only been applying the term ‘AM’ to a time convention or a transmitting signal on the radio.

Working at Flow Science meant learning something new every day. Whether it was something simple like connecting to metal casting companies over LinkedIn or something complex, such as understanding parameters in a software simulation; I was able to expand my knowledge and professional skills in a real job setting.

Ruendy Castillo, CFD intern

I am currently working to obtain a Bachelor of Science in Civil Engineering at the University of New Mexico (UNM). You might ask, why would a civil engineer want to work as a computational fluid dynamics (CFD) intern? But then, why not? The level of knowledge gained could not have been greater, and let’s say, knowledge really takes you places. This year I was able to be part of the Science, Technology, Engineering, and Mathematics Talent Expansion Program (STEP) funded by the National Science Foundation. This program provides UNM students an eight-week summer internship with a company/agency or a faculty member at the University. I was also fortunate to be part of the National Migrant Scholars Internship (NMSI) Initiative operated by the Migrant Student Services at Michigan State University. This program identifies and recruits College Assistance Migrant Program students from across the nation, and then matches them with experiential learning opportunities preparing them for future careers.

My internship at Flow Science was split into two sectors: Additive Manufacturing and Microfluidics.

Additive Manufacturing: Adding a Layer of Complexity

Additive Manufacturing or AM is a process by which three-dimensional objects are built using a layer-upon-layer approach. Some additive manufacturing processes include direct energy deposition, binder jetting and laser powder bed fusion. FLOW-3D is a CFD software that simulates additive manufacturing processes that help users understand the underlying physics at the micro and meso-scales. Using experimental data such as molten pool dimensions, it is possible to calibrate FLOW-3D models and once calibrated, the software allows users to develop process windows for different kinds of alloys, minimizing the number of experimental runs.

Paree and I worked together on calibrating FLOW-3D models using experimental data for laser powder bed fusion processes from the National Institution of Standards and Technology (NIST). The NIST experimental data consisted of a series of tests of laser scan tracks made on a bare nickel-based superalloy, IN625 metal surface. Once we calibrated the models using experimental data from NIST, our objective was to compare how FLOW-3D did against test data for newer process parameters. Daily, I would set up and run these simulations in the software, then analyze and record the results to capture any trends in our data. A total of 40 simulations were run, each taking around 3-4 hours for the calibration and test studies. At the end of the internship, I had an opportunity to give a presentation on our findings to the entire company. Not only were we close in matching the simulation results to the experimental data for the test cases, but we also uncovered useful trends in the data that showed how the laser power and scan speeds can affect melt pool length, depth and width in laser powder bed fusion processes.

The company also sells FLOW-3D CAST, which is a specialized version of FLOW-3D for metal casting simulations. I had an opportunity to research die casting companies in the Michigan area since our company was heading there to give a presentation at a North American Die Casting Association workshop. I would study the company websites, find contact information and call to see if they would be interested in meeting Paree to talk about how FLOW-3D CAST could bring great value to their company. I still remember how my first call went horribly wrong. Even if it sounds funny, I had to mentally prepare myself. I also wrote a script with the exact words that I was going to use when calling the companies. Let me tell you, I was nervous. I was also able to connect with over 300 users on LinkedIn that specialize in Additive Manufacturing, which for me, sounds ‘kinda’ impressive. Through this exercise, I learned how to communicate technical knowledge effectively over the phone and email since communicating is an important skill for engineers to master.

Spinning into Microfluidics

The second part of my internship focused on the research of pneumatic pumping in centrifugal microfluidic platforms. At this point, I started working with Adwaith Gupta, a CFD Engineer at Flow Science. Adwaith is part of the marketing team and oversees the microfluidics industry. Essentially, the same objectives applied as to what I did in AM- validating FLOW-3D results with experimental data.

For over 40 years, the centrifugal microfluidic platform, otherwise known as compact discs (CD), has been a research topic in both academia and industry. Currently, CD microfluidics is emerging as an advanced system for lab-on-a-chip (or, lab-on-a-CD) applications primarily geared towards the biomedical industry. Many applications use CD microfluidics, such as rapid diagnostics using immunoassays and plasma separation from blood. Most CDs rely on either application of special surface treatments on the CD surface or on active forces (generated by magnetic or electric devices). These approaches to strictly control the behavior of liquid inside these miniature CDs can be cumbersome because of extra chemicals (surface treatments) or too many devices. Inherently, CDs allow uni-directional flow of liquid due to rotational forces, limiting the real-estate for designing complex immunoassays while maintaining the compactness of the disc. To overcome all these limitations of traditional CDs, the researchers presented an alternate technique for centrifugal microfluidics that uses pneumatic compression.

The research consisted of a specially designed fluidic manifold with different compartments. Initially the compartments have air, but on increasing the rotational frequency of the CD, pneumatic compression would increase. By slowing the velocity of the disc, this same pneumatic energy (the energy contained in the compressed air) would be released to then pump fluids back toward the center of the CD, thus overcoming the uni-directional fluid movement. (Wow, I feel amazed for having the ability to explain this process!) Let me remind you that these are very tiny (a few centimeters in diameter) discs with the ability to minimize the many applications normally done in lab, therefore it’s difficult at times to obtain exact data.

I set up the simulations, recorded the simulation data, and compared it to the experimental data. This project was presented at the end of the internship to the company. While working with Adwaith, I also had the opportunity to complete a list of about 200 companies around the world that work in the microfluidics industry. The goal of this list is to reach out to these companies and showcase the applicability of FLOW-3D to their work.

The Takeaways

Certainly, it was a very intense summer. The level of work and commitment at this internship was very high. But, even when it was expected that I put in all my possible time, the willingness to help me was tremendous. As I remember Paree telling me during my interview, “We don’t want you to fail in this program,” and that motivated me to work even harder. During the coming year, I will be part of El Puente Research Fellowship. This program supports and promotes undergraduate research to prepare students for graduate level education over the course of two semesters. This internship gave me confidence for my future work. I feel more prepared, and now, the only thing left to say is thank you!

► CFD, C++ and Soccer: Summer Internship at Flow Science
  16 Aug, 2018

One of our summer interns, Lizeth Anaya, writes about her experience at Flow Science. Lizeth recently graduated from Española Valley High School with a 4.0 GPA and has just begun her first semester at New Mexico Institute of Mining and Technology.

My goal after graduating high school was to find an internship within my field of study, Technical Communication. I hoped for the best and submitted my resume and cover letter to John McDermon, the Los Alamos Employees’ Scholarship Fund scholarship manager. He then referred me to Flow Science and a few weeks later I had an interview. I was offered the eight-week internship as the marketing intern and my start date was June 12th. The process from start to finish was easy and I was always asked for my input when making decisions about my schedule or projects I’d be working on. During these eight weeks I met some of the most dedicated people. Many of my co-workers volunteered their time to help and train me. My mentor, Amanda Ruggles, introduced me to multiple concepts within marketing but never limited me to that and always looked for engineers willing to work with me. Here are my experiences at Flow Science week by week.

Week 1

I met my co-workers, they were nice and welcoming. I settled into my desk and was introduced to some of my projects. Flow Science was preparing for an Open House to connect New Mexico’s tech companies, non-profit groups, academic institutions and political leadership. The first project I worked on was the Open House guest list. Another one of my projects was the Teaching Assistance (TA) project which was assigned by Adwaith Gupta, a CFD engineer. For the TA prospecting project, I researched professors who may benefit from using FLOW-3D in the classroom. If students learn FLOW-3D in school it is easier for them to transition into the industry knowing how to use CFD software. The first week also happened to be the most exciting because I was invited to a Ping-Pong Tournament and to play tennis.

Week 2

I was introduced to multiple platforms that the company uses to communicate and track progress on projects. Adwaith volunteered his time to train me in the basics of JIRA, an agile development project management system. He showed me everything I’d need to know in order to track my projects. Flow Science also has a branch in Colombia for which I translated some brochures from English to Spanish. Some engineers from different branches put together a User Manual for FlowSight and I started proofreading it. I also helped Amanda with MailChimp, an email marketing platform. The World Cup was on and the office gathered at the cafe every day at noon to watch the game and eat lunch (my favorite part of the day).

Open house registration interns
Greeting guests with Ruendy at the Flow Science Open House.

Week 3

I played tennis with my co-workers: Karthik, Adwaith, and John. John and I (but mostly John) beat them in a doubles match that ended in a tie-break. On Tuesday, we were preparing for the Open House. I updated the RSVP list and made a sign in sheet. I was introduced to WordPress, a content management system, and the behind the scenes of the company website. In WordPress, I worked on forms and set up conditional notifications. Mexico was eliminated from the World Cup and the next couple of days were…rough (lol).

Week 4

My week started with changing thumbnails on all the webinars through Wistia, an online video hosting service. I also got an intro to Adobe Photoshop, which I used to modify the thumbnails. At lunch, Colombia lost and I cried at my desk afterward. I went on JIRA and saw that an issue was created, “Create LinkedIn account.” I decided to update my existing account since I had created one while looking for jobs in high school. The Open House was on Friday, I steamed tablecloths and printed sign in sheets. I also met some of the other interns that day, Ajit and Ruendy. Ruendy and I were in charge of the registration table that afternoon.

Week 5

C++ training with Edgar was fun and challenging. I finally wrote code that I could actually build. Harold from Colombia reviewed my translation of the Lost Foam Casting brochure and made some comments which allowed me to translate the rest of the brochures. Engineering Spanish is harder to translate than regular Spanish, but I used as a resource to make it easier.

Week 6

The World Cup is over. On Monday I had the opportunity to meet the President of New Mexico Tech, the university I will be attending in the fall. The first assignment of the week was to review all C++ concepts so that I can start on the GUI translation project. A couple lines of code were giving me a hard time on the practice assignment Edgar gave me. Amanda asked me to make some reservations for an upcoming sales associates meeting and like the distracted human being that I am, I set the arrival times to three in the morning. So I called and made the change to FIVE different reservations. I also started writing this blog that you’re reading right now.

Week 7 & 8

The last two weeks I wrapped up some projects. I added a chain selects add-on for the forms on several of the websites. Edgar sent over the translation project and I wrote some of the code for it. Everyone in the office met Congressman Ben Ray Lujan and it was an awesome opportunity for us interns. Amanda took some coworkers and I out for lunch, it was fun and very nice of her. Definitely a good way to end my internship.

Lizeth meets Congressman Ben Ray Lujan
Saying hello to Congressman Ben Ray Lujan

My experience at Flow Science was positive and encouraging. The projects were relevant to my major but I was also introduced to other projects in different fields. While working here I learned that I actually like coding and it’s not a headache or a foreign language. My internship was fun and challenging. Now it’s the end of the summer and I move out of my parents’ house in two weeks. As I start the next four years at New Mexico Tech I am confident because of the tools I’ve acquired here.

► CFD Engineering Intern, Water & Environmental Applications
    9 Aug, 2018

FLOW-3D specializes in transient, free-surface flows and is recognized as the premier tool for full 3D free surface modeling in applications related to the water & environmental industry. Common areas of application include the design of dam spillways, river hydraulics, fish passages, wastewater treatment, and sedimentation/scour evaluations. We have a large global user base that includes governmental agencies, private consultants, and academic research institutions. In this position, the intern will focus on water & environmental applications.

Ideal Candidate

Recent or imminent Master’s degree in Civil Engineering with hydraulics/water civil infrastructure emphasis, physical oceanography, maritime or coastal engineering. Strong understanding of fluid mechanics and open channel flow principles is required. Prior hydraulic modeling experience using 1D, 2D, or 3D/CFD methods strongly preferred. Strong 3D CAD skills and GIS skills also highly desired. Candidates should have exceptional oral and written communication, presentation, and interpersonal skills. The candidate should have the ability to work both independently and as part of a team.

Principal tasks and responsibilities

  • Validations: Indentify appropriate experimental or analytical results against which to test FLOW-3D for specific application areas. Develop and document validations comparing FLOW-3D to the experimental or analytical results.
  • Support: Assist Flow Science’s permanent staff in providing timely and complete responses to requests for technical support from FLOW-3D customers and associates.
  • Training: Attend training classes for FLOW-3D.

Interns will contribute and learn by:

  • Developing advanced CFD modeling skills with the CFD tool FLOW-3D
  • Developing an understanding of the available physics modules 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, sales 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 may be submitted via mail (Attention: Human Resources, 683 Harkle Road, Suite A, Santa Fe, NM 87505), fax (505-982-5551) or e-mail (

Mentor Blog top

► Technology Overview: Importance of CFD at NIAR for Aviation Research
  20 Sep, 2018

NIARs Director of Virtual Engineering Laboratory Dr. Gerardo Olivares, discusses the importance of CFD for aviation research.

► Technology Overview: CFD & CHT Analysis for NIAR´s Tailless Drone Project Using FloEFD
  20 Sep, 2018

NIAR’s Research Engineer, Harsh Shah discusses their Tailless Drone project and how FloEFD allowed them to perform many design iterations in a short space of time helping them to stick to the aggressive time schedule.

► Training course: STAR-CCM+ On-Demand Training Library
  13 Sep, 2018

Introduction to STAR-CCM+ multiphysics simulation, geometry processing, meshing, solver settings, post processing, and other aspects of the simulation workflow. 

► Blog Post: Article Roundup: Embedded Device Security, EV Battery Design, UVM 1800.2, Outlier Bugs & Smart City Mobility
    7 Sep, 2018

Device security: when ‘how’ becomes just as important as ‘what’ Improve EV Battery Pack Designs With CAD-Embedded Thermal Simulation Updated UVM Cookbook Supports IEEE 1800.2 Standard And Emulation When Bugs Escape Smart Cities’ Head Start On The Mobility Future Device security: when ‘how’ becomes just as important as ‘what’ Embedded

► Event: Best Practice in Transformer Design for Reliability
    4 Sep, 2018

How can we leverage virtual prototyping as the best practice for designing reliable transformers?

Watch this web seminar to find out!

► Blog Post: Article Roundup: Functional Safety for AVs, Parallelization Challenges, IoT Edge, Electric Motor Models & RTOS Mailboxes
  31 Aug, 2018

Evaluating EDA Functional Safety in the AV Era Why Parallelization Is So Hard Living on the (IoT) Edge A Faster, More Cost-Effective Way to Model High-Performance Motors Mailboxes: utility services and data structures Evaluating EDA Functional Safety in the AV Era EE Times ICs destined for automotive applications must meet strict functional safety and reliability standards such as ISO 26262. To

Tecplot Blog top

► Tecplot Acquires Genias Graphics
  16 Aug, 2018

BELLEVUE, WA (Aug. 16, 2018): As part of its ongoing growth and expansion, Tecplot, Inc. (Tecplot) is excited to announce the acquisition of their distributor in Europe and Western Asia, Genias Graphics GmbH & Co. KG (Genias). Tecplot customers in that region will now have direct support and access to the full extent of product knowledge that Tecplot offers.


“This acquisition reinforces our commitment to our European and Western Asian customers, giving them the best support and service possible.” says Tom Chan, President of Tecplot. “We are now in a position to directly communicate with more than 90% of our customers, ensuring their voice is heard when we make important product decisions.”

At the helm of European operations will be Lothar Lippert, who has been promoted to Regional Manager. Lothar has years of leadership experience, a depth of knowledge and experience with Tecplot products, and has served as the Manager of Business Development at Genias. During his time at Genias, Lothar has developed close relationships with clients in the region.


“I am honored to have the opportunity to lead Tecplot’s European operations.” said Lothar “I am excited to start my new role and to continue to foster and deepen relationships with clients new and old. This acquisition opens up new opportunities for growth by giving our clients close and direct access to all solutions and services that Tecplot has to offer.”


About Tecplot, Inc.

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

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


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

► History Match Bubble Plots
  18 Jul, 2018

Bursting the Bubble on Traditional History Matching

Blog by Raja Olimuthu, Tecplot RS Product Manager

The big problem with traditional history match workflows is that they are cumbersome. Doing a well by well analysis is time consuming, and it can be difficult to get a holistic view of your reservoir.

A better way is to examine the history match spatially – that is in one view.

I want to show you a method that ties the production and historical data to the grid solution, giving you a deeper understanding of what is happening with the history match.

Stay with me here as I walk you through a real-world problem set using our solution, Tecplot RS.

History Match Dataset

Those of you who have seen me in person or on customer visits know this dataset all too well!
I am using the Eclipse SPE_EX simulation deck which includes:

  • A grid solution
  • A unified restart (dynamic variables)
  • Initialization file (static variables)
  • A unified summary file (XY production data)

In this particular dataset, the unified summary file includes the historical data, which I will use during the analysis. This workflow is applicable for any simulation source, not just Eclipse.

Bubble Pie Settings dialog

Bubble Pie Settings dialog

Download the simulation deck ( to follow along

Bubble Pie Chart Setup

To spatially understand the accuracy of my history match, I’m going to use Bubble Pie Charts, a feature in Tecplot RS.

After I load data my data, I will activate the Bubble Pie Charts by checking the “Bubbles” checkbox, and set them up. For this particular dataset, I output the h variables in the unified summary file. I want to set up the bubbles to compare the simulated water production total with the historical totals. The production data from the unified summary file is populated into the Bubble Pie Settings dialog.

Settings walk-through:

  • Assign WPT and WPTH for Bubble Pie Segments
  • Assign WPTH Pie Segment color as pink
  • Change bubble size to dynamic, and size each bubble by the size of the bottom hole pressure at each time step
  • Size bubbles appropriately to match the data

Download a Free Trial of Tecplot RS

Interpreting the Results

At this particular time step (Jan 1, 1971), the view shows the proportional magnitude of WPT vs. WPTH at each well. If pink is dominating the bubble, the well is underproducing at that particular time step. If blue is dominating, then it is overproducing. The relative size of the bubble represents the magnitude of the bottom hole pressure. I am able to quickly understand which wells are over or underproducing while animating my grid solution through time.

 Proportional Magnitude of WPT vs. WPTH at Each Well

Proportional Magnitude of WPT vs. WPTH at Each Well

Tying in the Raw Results

I can understand this even more by taking advantage of the Quick XY functionality in Tecplot RS. Already, I have a spatial view that helps me accurately understand my history match. Using Quick XY, I can also understand the raw data – well by well – and link together quantitative and qualitative results.

Quick XY

Quick XY

Delta Bubbles

In case I haven’t output the h variables in my results, or if I’m viewing results from a completely different simulator, I can still take full advantage of this workflow. This is an alternative setup from the one mentioned earlier in this post.

I can load in my grid solution, production data, and auxiliary historical data into Tecplot RS from any source. I’m going to Activate the Show Deltas checkbox and select my auxiliary history data from the Delta File dropdown.

Now I’m viewing the same data (SPE_EX simulation deck) but in a slightly different way. In this view, blue Bubbles indicate overproduction, white Bubbles indicate underproduction. The size of these bubbles reflect the normalized difference between the historical and simulated water production at each time step.

Delta Bubbles

Delta Bubbles

Modify Grid Properties On-the-Fly

Bring the workflow full-circle by making necessary grid property modifications on-the-fly. In this case, the area in green is severely underproducing in water. I can use the Property Modifier tool in Tecplot RS to make the appropriate modifications to specified regions. I will choose to increase the permeability by 2000 in this region.

Modify Grid Properties

Modify Grid Properties

Output File

Output File

The resulting output file can then be used as an include file in your next simulation run. In this example, I have output only the multipliers. I could have chosen to output the entire array. The user has the option of outputting in different simulation types. See the video tutorial on Modifying Grid Properties.

Rinse and Repeat

Once you have run your next simulation, you can walk through this workflow again to analyze the accuracy of your history match.

Jim Gilman walks through these workflows in his Webinar History Match Efficiency with Tecplot RS.

And here is a 6-minute video tutorial on History Match Workflows.

Try Tecplot RS for Free

► Tecplot and CONVERGE Q&A
  20 Jun, 2018
Tecplot for CONVERGE images

Tecplot for Converge Images, left to right: Isovolume, Stream Ribbons, Interior Isosurface, Slice Mesh at Spark Plug.
Request a quote for the full version of Tecplot 360.

Earlier this year, we held a Webinar with Convergent Science on post processing CONVERGE data with Tecplot 360. These are the questions that came out of that Webinar (plus a few more we have been asked). Our goal is to answer the question… How can you quickly post-process CONVERGE output?

What is Tecplot for CONVERGE?

Tecplot for CONVERGE is a nearly full version of Tecplot 360. A complimentary copy is included with every CONVERGE license. For more information, see Tecplot for CONVERGE.

How do I get Tecplot for CONVERGE?

Convergent Science distributes and licenses Tecplot for CONVERGE. To obtain a Tecplot for CONVERGE license, contact Convergent Science.

How do I get the full version of Tecplot 360?

We have special pricing available for CONVERGE users. Call us at 425-653-1200 or 1-800-763-7005, email, or request a quote.

What is the memory requirement per million cells of CONVERGE?

Memory consumption depends on your data file format, how many variables you’re viewing in the plot, and the plot elements you have.

Tecplot 360 avoids loading variables that are not used for plotting or calculations. It will also unload data when it is no longer needed. This helps increase speed and reduce the memory foot print.

For a concrete example, a 12 million cell Gas Turbine result from CONVERGE consumes ~2.5Gb of RAM when displaying the outer boundary and displaying one slice through the data.

How do I load CONVERGE files into Tecplot 360?

CONVERGE creates post*.out files. These files must be converted to Tecplot format using the post_convert utility. You can then load them directly into Tecplot 360.

Convergent Science intends to release an update to CONVERGE which will export CGNS files directly. CGNS files can be loaded directly into Tecplot 360. This will eliminate the need to run post_convert.

Is it possible to overlay two contour or scatterplots from different times on the same plot?

This is possible by using two separate frames. Each frame in Tecplot 360 can display only a single time value. By using two frames and turning off the frame background you can overlay two frames to achieve this effect.

Can Tecplot automatically find the min/max values of a particular time to create contours?

When defining contour levels, Tecplot 360 defaults to using the contour values from the first, last, and middle time steps.

To find the true min/max values of a variable through all time, use Tecplot 360’s Python API, PyTecplot. A simple PyTecplot command will return this value.

This command will return the min/max values of the Pressure variable over all time:


You can also use this macro: ContourVarMinMax.mcr

Does Tecplot have keyframe animation?

Yes. This is available in the Animate menu. Key Frame animation allows you to set up multiple view positions and it will perform a linear interpolation between the view positions.

How do I load CONVERGE output files?

A CONVERGE output file loader will be available in Tecplot for CONVERGE in summer 2018. If you have a fully licensed version of Tecplot 360 you can use the General Text Loader.

Does Tecplot 360 load all time steps at once?

Well, yes and no. Tecplot 360 will scan through all of the files to understand how many time steps there are and what the time values are.

On initial load Tecplot 360 will only load the surface zones for the first time step.

As you advance through time, Tecplot 360 will load the data needed to generate the plot at each time step. This data is cached in RAM as long as possible.

Tecplot 360 will unload unused data as the RAM limit on your machine is reached.

How is the rendering performance of Tecplot 360? Can it also use multiple threads to render?

The rendering performance is highly dependent on your graphics card. We have found that the engineering-grade graphics cards, in particular Nvidia Quadro cards, give very good performance.

Tecplot 360 will use multiple threads for data processing, such as variable calculations. It also uses multiple threads for generation of objects like slices, iso-surfaces, and streamtraces.

For animations, can you save individual frames to images?

This capability is not built in to Tecplot 360 but can easily be done with a Tecplot macro. To step through time and export to individual images, use this macro: export_over_time_to_images.mcr

► Tecplot and Convergent Science Announce Partnership
  31 May, 2018

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

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

Tecplot for CONVERGE: Interior Isosurface

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

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

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

About Tecplot, Inc.

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

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

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

About Convergent Science

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

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

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


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

Sizing Particles (Parcels) by Variable.

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

Video Script

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

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

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

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

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

Thanks for watching!

► Isolating Particles with Value Blanking
  11 May, 2018

Isolating Particles with Value Blanking

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

Video Script

The film flag is an integer with values from zero to five, which represent the different parcel properties. Shown here are the meanings for each film_flag integer value.

Film Flag

Film Flag Integer Values

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

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

Then we toggle on Active and Include value blanking.

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

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

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

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

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

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

Macro Command PromptForTextString

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

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

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

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

Thank you for watching!


Schnitger Corporation, CAE Market top

► Siemens to acquire Lightwork Design
  18 Sep, 2018

In transit, so this has to be brief:

Siemens just announced that it is acquiring Lightwork Design — the people you likely know for their gorgeous rendering software, Lightworks. Siemens says that this type of realistic imaging will “help customers address potential product problems early in the lifecycle, saving both time and cost”. Lightworks has application in simulation, marketing, collaboration and other areas and, as such, will not be part of any specific Siemens brands. Siemens intends to put Lightwork Design into its PLM Components business, which includes Parasolid, D-Cubed and Kineo, “where more than 240 companies integrate Siemens PLM Software technology into 350 commercial applications for the benefit of six million end-users”. So there.

No details were announced — but if I learn more, I’ll update.

Cover image is of a tiara from Lightwork Design’s image gallery, here. I’m not a tiara kind of person, but if I were …

The post Siemens to acquire Lightwork Design appeared first on Schnitger Corporation.

► Totally OT: #MVGasFire
  17 Sep, 2018

First, to all of you who reached out to check on me and my family: thank you. We are fine, in our home and sustained no damage. But it’s been an interesting ride. Many people remain in shelters, one person has died and dozens were hurt. But not us. We were incredibly fortunate. Let’s backtrack:

On Thursday at around 5PM our town’s alert system sent out a very strange message, something like “There are gas explosions all over town. Evacuate your home now.”  These alerts had always been requests to be on the lookout for seniors who had wandered away from their carers or notices that trash pickup would be delayed. But “Evacuate now”?? So I called the police department’s routine number and they confirmed: not a hoax, not a drill. Get out. So we did.

Then came a second message: “Turn off your gas.” My neighbors and I stood in the street outside our homes and tried to figure out how to do that [Stacy rocks — her dad knew.] But how do we evacuate? In a hurricane, you go away from the storm’s path. Where do you go in this kind of case, where anything could blow up? And how do you do that in a car, basically a moving gas tank? How long would we be gone? What did we take? Where could we go with pets?

While trying to figure this out, we heard sirens in all directions, building in number as more and more of the 50-plus fires the Merrimack Valley saw that evening kicked off. Clearly a real danger, all around us. One of our neighbors loaded up and headed out, only to come back: the streets were gridlocked as every resident tried to evacuate.

But eventually we did go because it just got too panicky to do nothing. We drove for a while, trying to figure out where we could go with pets –none of the shelters just opening at that time took pets and many of our friends who offered a bedroom had pets of their own– and decided to return home because we saw nothing dangerous in our part of town. We thought that since the power company hadn’t turned off our electricity, there wasn’t as much spark danger as in other parts of the affected area. [I don’t recommend that reasoning in case you’re not safe, but it made sense to us at the time.]

So here we sit. It’s now 9AM on Monday and we have power but no gas, and are safe at home while hundreds are still displaced in shelters. School will happen in my town today, but not in all towns affected by this. Compared to many, we are so so so lucky.

What have we learned from this? Many things, including:

  • If your product fails (looking straight at you, Columbia Gas), own it and communicate about how you’re dealing with it. It’s now day 4 and we have no info on how and when we’re getting gas back. Is gas life-critical? No. But it means no hot water, no cooking (for those with a gas stove) and no certainty that things won’t go boom when you DO turn our supply back on.
  • The Interwebs are amazing and amazingly evil. Everyone is still learning how to use these tools to communicate critical info but OMG the vitriol, poison and lies that come in the midst of good and useful info. Just check #MVGasFire to see what I mean. Before the National Transportation Safety Board (because gas lines transport gas) even arrived, people had “proof” that this was the result of a Russian hacker attack. Because this part of Massachusetts is home to a lot of immigrants, there was a theory that this was some convoluted plot to expose illegal migrants. All hogwash but stoking itself into a frenzy.
  • Human beings are amazing. My neighbors, all looking out for one another. Random strangers donating everything to get the shelters running. A shelter tweeting that it needs coffee and getting dozens of java boxes. Calls going out for baby supplies, pet supplies, pillows, canned goods, you name it, and regular people loading their cars … Amazing.
  • But not all. There are !@#$ in every situation. In our case, a guy named Steve. Steve came rushing down the street, cell phone pressed to his ear to tell us the explosions were working their way up a nearby street and would be here in minutes. Scared the daylights out of all of us, especially the kids who saw the adults freak out. We later drove that street. Nothing. Crickets. Saw him sitting in a car with an out-of-state license plate, probably waiting for everyone to evacuate. We and another neighbor stood watch over empty houses because we thought (and still do) that he was casing the neighborhood. No matter how helpful and kind the vast majority of people are, there are always a few who bring the good vibe crashing down. [There was a report of minor looting but not on our street. We had no proof, so couldn’t call the police — and they were frantically busy anyway.]
  • IT is a true barrier in this case. Columbia Gas so bobbled their response that the Governor of Massachusetts handed leadership of the response to another utility, Eversource, and brought in crews from around the region. None of their systems talk to one another, which really complicates relief efforts. Maps, lists, contact info all have to be retyped from system to system. That’s nuts and so easily solvable.
  • First responders and their helpers, who worked long shifts with minimal sleep, do a job I couldn’t. There aren’t enough thank yous possible for the fire crews who came from as far away as Maine, the police and ambulances from all over the MA/NH/ME region and the utility crews from as far as Ohio. None of them created this situation and all are doing as much as they can to help.

It all comes back to this: How did we get here? The NTSB is still investigating but it appears that 48 miles of pipe are so old that stretches simply failed when the gas lines somehow were over-pressurized. This over-pressure made its way through the system, into gas meters and homes, blowing out protective mechanisms and, when exposed to a spark, causing an explosion. And there’s a lesson, too: if we don’t pay attention to maintenance, infrastructure failures are catastrophic. I don’t know what the ultimate total bill will be to Columbia Gas but it’s got to be more than just replacing these pipes as part of a maintenance plan. They’re on the hook now for meals eaten out, hotel bills, childcare an, of course, the damage caused by the explosion. And it’s not clear how the over-pressure came to be; sensors were on the wrong pipes, for example. Still lots to learn.

But thanks again for checking in. We’re fine. Lots of people aren’t. If you’d like to help, the Boston Globe has a great list, here.

UPDATE: Our gas was restored mid-day on Monday, but many aren’t so lucky and will spend more time in shelters and in unheated homes, when they can go home. In the greater scheme of things, this was no big deal –hurricane Florence and typhoon Mangkhut caused much more harm– but this was as local to me as it gets. Thanks again for your concern, it means a great deal. I am now going to take 5 hot showers in a row. Maybe 10. Because I can.

The cover image has nothing to do with anything. It’s just pretty and we need something pretty!

The post Totally OT: #MVGasFire appeared first on Schnitger Corporation.

► Altair: Bells, clouds, platforms and units bring simulation to the masses
  12 Sep, 2018

If you’ve ever watched the news in the US, at some point you will have seen a company ring the bell at the New York Stock Exchange (Wall Street) or the Nasdaq (further up the island of Manhattan, in Times Square). I’ve never given much thought to who or how or why that all happens — but now I have at least one company’s answer. And, honestly, it’s one of the cooler things I’ve ever experienced.

Altair, the CAE company, went public last year and decided to sell its shares on the Nasdaq exchange. One of the fringe benefits of selecting the Nasdaq is that companies get to stage events at the Nasdaq’s swanky space in the middle of the middle of the middle of glitziest, touristiest New York City. Listed companies can choose to “ring the bell” (really, push a button) which starts the trading day*. Why do this? According to the monitors in the studio, when Altair CTO James Dagg did his thing at the podium, it was broadcast into Times Square and carried live on the major financial networks like CNBC, Bloomberg TV and Fox Business News. Later when anchors summarize the news of the day, that footage may be used again. Millions of potential viewers will see Altair’s logo. The Nasdaq people even put some Altair CAE video on the big monitors in Times Square — I’m pretty sure the tourists had no idea, but perhaps someone will go home, think about their job and say “huh, that looked useful. What was it?” Or an investor will wander by and see how useful simulation can be, and invest in the stock.

This is Mr. Dagg getting ready for the opening ceremony, in the studio and below is an image of the ceremony taken from inside the studio of the giant billboard in Times Square:

But what, you reasonably ask, did Mr. Dagg talk about in his remarks before pushing the magic button? Three things: the company’s Enlighten award winners, its new Inspire Platform and the Cloud 365 delivery option. All were presented to investors and journalists at an event the evening before the market bell ceremony.

The Altair Enlighten awards celebrate companies and individuals who innovate in the name of vehicle lightweighting. You can see a full list of this year’s winners and runners-up here; the ones who joined the bell ceremony, like US Steel and Faurecia, spoke about design concepts, material science and advanced manufacturing techniques that couldn’t even be conceived just a few years ago, and about how Altair’s technology is key in defining, analyzing and ensuring strength to weight. Faurecia even brought a piece of tailpipe to show off its Resonance Free Pipe, where a piece of perforated material (looks a bit like a cheese-grater) is inserted around the inside of a tailpipe to break the standing sound waves in the pipe and vent acoustic pressure along the pipe. Quieter, lighter, smaller. Smart.

Before the Award winners spoke, Mr. Dagg unveiled Altair Inspire and Altair 365. Altair Inspire starts with the company’s solidThinking brands and expands on them to create a platform that brings together concept design, generative techniques, analysis, and manufacturability in a single environment — but still sticking to the brand values that appeal to designers and engineers with little simulation experience. Mr. Dagg spoke about simulation-driven design, from studio to manufacturing, with Altair building out this portfolio over the next year using solvers from HyperWorks where appropriate. Some of this is a rebranding, as Click2Cast becomes Inspire Cast — but new functionality will be added, too. Inspire Studio, a polyNURBS and subdivision sculpting solution based on solidThinking Evolve, will debut in 2019. Bottom line: Altair sees that its solidThinking customers do more than concept design — they ultimately need to manufacture these products. And with additive manufacturing playing such a strong role in ideation today (especially when it comes to lighweighting), many companies need a single solution that wraps around the concept-simulate-manufacture ideation loop in a single platform. No moving data around. No breaking the chain.

To make this solution set more accessible, Mr. Dagg also announced the Altair 365 engineering collaboration platform, hosted on Microsoft Azure. Using Altair 365 sounds a lot like using Microsoft Office 365: customers can access Altair Inspire and the entire solidThinking suite in the cloud, using browser-based tools. Mr Dagg said that Altair has worked hard to ensure that Altair Inspire customers have the same user experience and simulation capabilities whether in the cloud or on the desktop. And, because it’s cloud-based, Altair 365 also offers visual collaboration, version control, secure data management as well as access to scalable high-performance computing resources.

But all of this wouldn’t be possible without the other recent announcement made by Altair: solidThinking units. Altair has offered HyperWorks units  (HWUs) for a long time, a mechanism which allows metered usage of Altair’s entire suite of products as well as those from selected partners. Why do this? Because simulation use is lumpy: lots of topology optimization early, perhaps manufacturing simulation later, and structural simulation all along. The Units enable customers to have on-demand use as needed, up to the purchased units limit. It’s been possible to buy HyperWorks units and apply them to solidThinking; solidThinking has also been sold on a perpetual basis. Now solidThinking has its own set of units. solidThinking Units (sTUs) gives buyers access to all software titles available in the solidThinking catalog and Inspire platform,  as well as the ability to run these applications locally or in the cloud. Altair’s team at the Nasdaq event told me that sTUs are less expensive than HWUs — and that the company plans promotions to ease customers onto the new scheme. Check with your reseller.

So what did we learn at the Nasdaq? That James Dagg is unflappable in the face of cameras that project him, far bigger than life, into Times Square and into possibly 30 million homes and offices. That Altair works with customers at the very cutting edge of material science and manufacturability in its pursuit of lighter, stronger designs. And that it is trying very, very hard to get more simulation into more hands, via the usability improvements, the Inspire platform, no-desktop access and innovative licensing. Lowering these barriers to simulation for design teams with limited IT and compute resources enables them to ramp up their simulation-driven design capabilities and compete with far more established companies.

*Altair’s Dave Simon and I were trying to figure out what actually happens when someone “rings the bell” and I think he’s right. It signals the start of trading but doesn’t actually DO anything. We were cheering and clapping so I don’t even know if a bell sounded! Dave thinks there’s correlation (bell –> trading) but not causation.

Note: Altair 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. These pictures are ones I took at the event.

The post Altair: Bells, clouds, platforms and units bring simulation to the masses appeared first on Schnitger Corporation.

► #SIAC18 showed that Siemens is more than the sum of its parts
    6 Sep, 2018

Every year in late August or early September, Siemens throws a big information-fest for industry analysts and journalists where they explore the latest industry trends and technologies, and highlight Siemens’ place in its customer/partner ecosystem. It’s tough to summarize because there are so many moving parts but here are my key takeaways from last week’s event:

This year, rather than an NX session, one of Teamcenter, etc. Siemens elected to focus on industrial themes and how their offerings support companies working in that area. For autonomous vehicles, for example, we covered sensors, systems and simulation. This gets to one of the company’s key themes for this event: that the traditional labels and boundaries are disappearing, and that Siemens will best serve its clients by creating integrated solutions.  After all, most technology buyers start with a problem to solve, and not a technology to buy.

This is bigger than the PLM business. PLM’s products are being used across the AG, as NX, Simcenter, Teamcenter et al are used in the design and production of medical devices, eAircraft, autonomous and guided vehicles, energy systems — all across the business. And those are tough, tough customers for the PLM team, pushing to make their solutions ever more hardened and fit-for-purpose. It’s a strong proof point that the Siemens businesses rely on PLM’s tools and can show how well those solutions work outside the demo environment.

Analyst events usually involve two kinds of speakers: company and customer. The company speakers talk about technology and the customers about how they apply it to solve real-world problems. Company management talks about the business and its overall direction — and those were really solid this year. The CEO of Digital Factory, PLM’s parent within the current Siemens organization, Jan Mrosik, talked about how software-focused Siemens is today — it employs over 24,000 software engineers. And with software revenue of nearly $5 billion across the AG (€3.4 billion in the PLM business), he said it’s one of the top 10 global software companies.

Tony Hemmelgarn, CEO of the PLM business, talked about integrated platforms and solutions that solve problems — like the merging of CAD and simulation for topology optimization, and then perhaps adding in business logic for true generative design. Tony also gave a brief update on the PLM business, highlighting that revenue is growing at over 10% per year, not including acquisitions — far faster when adding those into the total. This is the slide he used to summarize the business:

Acquisitions came up early and often (and you can see it in the bottom row of the image above) and so did BentleySiemens and Bentley announced that they’re expanding their partnership by doubling their joint investment to €100 million, and Siemens now owns 9% of Bentley’s non-voting common shares. But what I really liked was the tease in the technology showcase of how they’re putting together Bentley’s Projectwise and Teamcenter to enable the companies to go more/bigger/further in infrastructure projects. The intention (demonstrated to some extent last week, but not really ready for early visibility until year-end and for broader distribution in mid-2019) will takes plant engineering data from Bentley OpenPlant, Siemens COMOS, or a third party and use Teamcenter for enterprise-level data management and collaboration. The point: infrastructure assets are designed and built in a couple of years and then operated for decades. Tying together the data frameworks means that projects can be optimized from design through construction to operations/maintenance and eventual shutdown. It’s a big step towards creating a digital twin of an intelligent plant.

Speaking of acquisitions, we were introduced to Mendix, the company Siemens is buying. Mendix makes a low-code application development platform that enables companies to quickly create software solutions of many different types: cellphone apps for customer self-service. Connections between systems that enable sales people to configure and give production estimates. Connect IoT devices to MindSphere. Their platform supposedly does app development 10 times faster and with 70% fewer resources — I would imagine that actually depends on the complexity of the application and the skill level of the developer — but the point is that Mendix will enable companies to think differently about and implement technology.

And while I could keep going, it’s fitting to end on Mendix because I think Mendix embodies what SIemens wants to become: agile, collaborative, building customer/client communities, solving gnarly problems in an elegant and intuitive way. And I don’t mean that just for PLM; I think the AG’s leadership wants those entrepreneurial qualities to percolate through the AG.

We’ve come so far since the first analyst meetings, which were all about the latest NX/Teamcenter/Tecnomatix functions and features, user interfaces improvements and faster mouse movements. Nothing like that this year (though there was a bit of that in the Solid Edge University keynote — more coming on that when I get time). Instead, Siemens showed off its prowess in creating cross-product solutions that fade into the background, letting the user solve a problem not master a technology.

Note: Siemens graciously covered some of the expenses associated with my participation in the event but did not in any way influence the content of this post. The cover image and slide in the text are from Tony Hemmelgarn’s presentation at the event, supplied by Siemens. 

Update: An earlier version of this post had the wrong hashtag for the event. It is #SIAC18 (Siemens Industry Analyst Conference). Oops.

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► Quickie: Nemetschek acquires MCS Solutions for building ops
  28 Aug, 2018

Really quickly, since I’m at the Siemens analyst event: Nemetschek just announced that it has acquired MCS Solutions. maker of building management solutions. MCS Solutions currently serves over 1.5 million active users in over 60 countries, across private and public assets. MCS Solutions currently employs over 200 people and anticipates revenue of around €15 million for 2018. Nemetschek purchased 100% of the shares at a revenue multiple of “between 3-3.5x”, which I calculate at around €50 million.

What are building management solutions? Software that enables landlords, managing agents and others keep tabs on their real estate portfolios and orchestrate facility management, such as assigning office spaces; workplace management, which includes maintenance scheduling and other tasks related to keeping the facility up and running.

Interestingly, MCS also developed a smart building platform called COBUNDU that uses sensors and data analytics to optimize the “occupant experience and facility service delivery”.  It’s likely that this was a major factor in Nemetschek’s decision; the push towards sensor-enabled monitoring and analytics is gaining traction in the AEC world, given the distributed nature of assets and staff.

“MCS Solutions is a perfect match for our solution portfolio that opens up a new market segment in which we will build to a leading market position … With the integration of MCS Solutions, we drive digitalization through the entire building process and are going to realize a seamless exchange of information before and, continuously, during the usage of a building,” says Patrik Heider, Spokesman and CFOO of the Nemetschek Group. “With the strong and global market position of MCS Solutions, we are taking on the role of being an innovator for the entire lifecycle of buildings.”

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► Siemens and Bentley further cement relationship — no acquisition
  27 Aug, 2018

Siemens’ analyst event hasn’t started yet but we’ve already had 2 important announcements about its relationship with Bentley. One is business/financial and the other is … product. BIG product.

In the financial realm, Siemens and Bentley Systems are expanding the partnership announced in 2016, pumping another €50 million into their joint research and development efforts for a total of €100 million.

You might remember that Siemens held an investor event a few weeks ago, where Bentley was mentioned for no obvious reasons — just snuck into remarks about how well Siemens is doing in expanding its software footprint and software-enabled hardware offerings. Today, Klaus Helmrich, a member of the Managing Board of Siemens AG, said in a press release, “I’m very pleased with how strong our alliance started. Now we are investing in the next collaboration level with Bentley, where for instance we will strengthen their engineering and project management tools with Siemens enterprise wide collaboration platform Teamcenter to create a full Digital Twin for the engineering and construction world.”

The companies gave the following example: “In the energy and utility industries … companies need to work more efficiently and cost-effectively when implementing capital improvement projects. Extending Teamcenter through project delivery, [Bentley’s Connected Data Environment, CDE] enables visibility along digital threads of connected 2D and 3D models, dynamically managed to reflect project status. This allows for the continuous assimilation of design and engineering data, to be visually and analytically accessible as appropriate by team members across the wider enterprise and supply chain. Incorporating capital project engineering and construction models in this integrated way enables diverse simulations throughout the project management process to anticipate real-world issues, and more informed decision-making by virtue of real-time understanding of the impact of any design change. The solution will be available to the market beginning in early 2019.”

Exactly what this means and how this integration will be done, are going to be huge topics over the next few days as I sit with Siemens management — and I’ll get Bentley’s take on it, too, when there’s time. There are so many potential points of intersection: Bentley’s expertise in AEC project management, construction workface planning, asset operations and how that feeds into new projects combined/linked/hooked into Teamcenter’s bill of material management, a detailed CAD focus on mechanical systems and knowledge of the manufacturing processes many want to bring to construction  … this could be huge. But we shall see what hits the market in early 2019.

The doubling of investment likely means that this project is going well and there’s more on the horizon. For now, Greg Bentley, Bentley Systems CEO, said in the announcement: “In our joint investment activities with Siemens to date, we have progressed worthwhile opportunities together with virtually every Siemens business for ‘going digital’ in infrastructure and industrial advancement. As our new jointly offered products and cloud services now come to market, we are enthusiastically prioritizing further digital co-ventures”.

That brings us to announcement number 2:

Mr. Bentley says that Siemens now owns over 9% of Bentley’s common stock: “We have also welcomed Siemens’ recurring purchases of non-voting Bentley Systems stock on the NASDAQ Private Market, which we facilitate in order to enhance liquidity, primarily for our retiring colleagues.” A bit of context: Bentley said that Siemens owned 9% during its May Corporate Update (link on lower right of this page:, so “over 9%” isn’t a huge leap. But it is a decent stake, and the companies are now tied to one another in several different ways, more difficult to unravel than the typical joint venture. I wrote more about Bentley’s Corporate Update, here.

I’ll update as I get more info. Or see a demo. ProjectWise + Teamcenter working together. That would be something to see!

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