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Another Fine Mesh top

► This Week in CFD
  15 Jan, 2021
We begin this week’s roundup of CFD news with a lot of software release announcements as though everyone was holding back until the holidays were over. And several good, long reads are included this week including one on our old … Continue reading
► Meshing, Visualization, and Computational Environments at AIAA SciTech 2021
  12 Jan, 2021
AIAA SciTech 2021, an online event, opened yesterday. Over the next two weeks there are seven technical sessions hosted by the Meshing, Visualization, and Computational Environments technical committee that you’ll probably want to attend. Here’s a brief list of the … Continue reading
► Generate Meshes All Day – And Get Paid for It!
  11 Jan, 2021
Yes, I know that sounds too good to be true. We have three open positions and are seeking folks who are excited about mesh generation and CFD and thrive in the everyday application of those technologies to innovative products. One … Continue reading
► This Week in CFD
    8 Jan, 2021
And here is the first issue of This Week in CFD for the new year. Some applications of CFD (including the Image of the Week for an overset simulation), event updates, and some interesting software work at Los Alamos to … Continue reading
► Pointwise at AIAA SciTech 2021
    7 Jan, 2021
The 2021 AIAA SciTech Forum, and expanded virtual event, begins next week and runs 11-15 January and 19-21 January. Pointwise will be well represented at the workshops and technical sessions and we invite you to join us for some mesh … Continue reading
► View and Examine Virtually Any Mesh – For Free
    6 Jan, 2021
Have you ever been given a mesh file but had no way to look at it or evaluate some of its quality metrics? The Pointwise Viewer provides precisely that functionality and is available for free. How about starting the new … Continue reading

F*** Yeah Fluid Dynamics top

► Hedgehogs Atop Waves
  21 Jan, 2021

Since Michael Faraday, scientists have watched the curious patterns that form in a vibrating liquid. By adding floating particles to such a system, researchers have discovered spiky, hedgehog-like shapes that form near the surface. At low amplitudes, the surface patterns resemble the typical smooth rounded lobes one would expect, but as the wave amplitude increases, spikes form in the tracers, driven by the motion of the waves. (Image and research credit: H. Alarcón et al.; via APS Physics)

► Stabilizing Foams
  20 Jan, 2021

Bubbles in a pure liquid don’t last long, but with added surfactants or multiple miscible liquids, bubbles can form long-lasting foams. In soapy foams, surfactants provide the surface tension gradients necessary to keep the thin liquid layers between bubbles from popping. But what stabilizes a surfactant-free foam?

New work finds that foams in mixtures of two miscible fluids only form when the surface tension depends nonlinearly on the concentration of the component liquids. When this is true, thinning the wall between bubbles creates changes in surface tension that stabilize the barrier and keep it from popping.

In mixtures without this nonlinearity, foams just won’t form. The new results are valuable for manufacturing, where companies can avoid unintentional foams simply by careful selection of their fluids. (Image credit: G. Trovato; research credit: H. Tran et al.; via APS Physics; see also Ars Technica, submitted by Kam-Yung Soh)

► The Galloping Starfish
  19 Jan, 2021

Starfish won’t win any sprints, but they’re actually quite good at moving around as they hunt for prey. Without brains, starfish are led by their feet, which pull in the direction of food they scent. Each foot is connected to what amounts to an internal hydraulic system within the starfish. With a combination of secreted adhesive and pumping, the starfish can trundle along. (Image and video credit: Deep Look)

► Oil in Water
  18 Jan, 2021

In the decade since the Deepwater Horizons oil spill, scientists have been working hard to understand the intricacies of how liquid and gaseous hydrocarbons behave underwater. The high pressures, low temperatures, and varying density of the surrounding ocean water all complicate the situation.

Released hydrocarbons form a plume made up of oil drops and gas bubbles of many sizes. Large drops and bubbles rise relatively quickly due to their buoyancy, so they remain confined to a relatively small area around the leak. Smaller drops are slower to rise and can instead get picked up by ocean currents, allowing them to spread. The smallest micro-droplets of oil hardly rise at all; instead they remained trapped in the water column, where currents can move them tens to hundreds of kilometers from their point of release. (Image and research credit: M. Boufadel et al.; via AGU Eos; submitted by Kam-Yung Soh)

► Sunset Swirls
  15 Jan, 2021

This gorgeous photograph of Kelvin-Helmholtz clouds was taken in late December in Slovenia by Gregor Riačevič. The wave-like shape of the Kelvin-Helmholtz instability comes from shear between two fluid layers moving at different relative speeds. Here on Earth, clouds like these are often short-lived, but we see similar structures in the atmospheres of gas giants like Jupiter and Saturn. (Image credit: G. Riačevič; submitted by Matevz D.)

► Adjusting for Gusts
  14 Jan, 2021

In flight, birds must adjust quickly to wind gusts or risk crashing. Research shows that the structure of birds’ wings enables them to respond faster than their brains can. The wings essentially act like a suspension system, with the shoulder joint allowing them to lift rapidly in response to vertical gusts. This motion keeps the bird’s head and torso steady, so they can focus on more complex tasks like landing, obstacle avoidance, and prey capture. (Image and research credit: J. Cheney et al.; submitted by Kam-Yung Soh)

Symscape top

► CFD Simulates Distant Past
  25 Jun, 2019

There is an interesting new trend in using Computational Fluid Dynamics (CFD). Until recently CFD simulation was focused on existing and future things, think flying cars. Now we see CFD being applied to simulate fluid flow in the distant past, think fossils.

CFD shows Ediacaran dinner party featured plenty to eat and adequate sanitation

read more

► Background on the Caedium v6.0 Release
  31 May, 2019

Let's first address the elephant in the room - it's been a while since the last Caedium release. The multi-substance infrastructure for the Conjugate Heat Transfer (CHT) capability was a much larger effort than I anticipated and consumed a lot of resources. This lead to the relative quiet you may have noticed on our website. However, with the new foundation laid and solid we can look forward to a bright future.

Conjugate Heat Transfer Through a Water-Air RadiatorConjugate Heat Transfer Through a Water-Air Radiator
Simulation shows separate air and water streamline paths colored by temperature

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► 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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CFD Online top

► Turbomachinery Solver OpenFOAM
  13 Jan, 2021
Turbomachinery Solver OpenFOAM

This forum has been a big source of information for me since I started doing CFD. Now, I want to share my work to thank your altruistic knowledge sharing. Regarding the main topic of this blog, I am going to talk about my first turbomachinery simulations with rhoSimpleFoam, the problems I found with this solver and my solution, which I called turboSimpleFoam. Feel free to ask me whatever you want or tell me if something is wrong.

Turbomachinery is a very interesting topic in my opinion, so I started the design of a turbocharger a year ago just for fun. After modelling some of the geometry, I thought that doing some CFD simulations would be of interest to understand the behaviour of the air when it passes through the device. Therefore, a computational domain was defined by the following properties:
• Compressible
• K-Omega SST
• Subsonic
• Inlet T = 300 K
• Inlet p = 1 atm
• Mass flow = 0.1 Kg/s
• Rotation Speed = 50 000 rpm

Until here the problem seemed challenging, but nothing I hadn’t done before. Taking into account the conditions of the problem to be solved, I chose rhoSimpleFoam as my solver, snappyHexMesh as my mesher and then I performed some simulations. Surprisingly for me, the temperature decreased to 280 K at the exit of the rotor so, obviously, something was terribly wrong.

Firstly, I tried several things like changing its thermodynamic properties or its boundary conditions, without significant changes. The problem was driving me crazy for a few days, but then I introduced an energy source in the rotating zone, and it worked. However, I hadn't solved it yet, because I picked an energy source of a random number of watts, but it was a good starting point.

Once I noticed that the energy along the rotating zone wasn’t being solved properly, I studied turbomachinery theory to calculate the energy source, and this was my conclusion:

𝑊𝑢 = 𝑚̇ · ( 𝑢1 · 𝑐𝑢1 − 𝑢2 · 𝑐𝑢2 )
𝑢 = 𝑟 · 𝜔
𝑑𝑊𝑢 = 𝑑𝑖𝑣𝑒𝑟𝑔𝑒𝑛𝑐𝑒( 𝑚̇ · 𝑢 · 𝑐𝑢 )

Where 𝑊𝑢 is the energy source, 𝑚̇ is the mass flow, 𝑢 is the rotation velocity, 𝑐𝑢 is the tangential velocity of the flow, 𝑟 is the radius and 𝜔 is the rotation speed.

After that, I introduced the energy source in the code and some extra variables like the rotation velocity, the tangential velocity, the radial velocity and something I called the zone term, Z. The last term is necessary due to the energy source value being zero at the static zone and one at the rotating zone. Taking all of this into account, the energy equation in the code is:

fvScalarMatrix EEqn
fvm::div(phi, he)
+ ( == "e"
? fvc::div(phi, volScalarField("Ekp", 0.5*magSqr(U) + p/rho))
: fvc::div(phi, volScalarField("K", 0.5*magSqr(U)))
+ thermophysicalTransport->divq(he)
fvOptions(rho, he)+Z*fvc::div(phi,u*Ut)

Finally, the results obtained are logical and the temperature rises to 350 K. Also, I have solved the problem in EXCEL using velocity triangles and thermodynamics, so the results of the CFD simulation can be compared with the theory of turbomachinery. As it can be seen at the end of this blog, the results obtained by both ways are quite similar.

In conclusion, the new solver turboSimpleFoam gives excellent results in comparison with the theory of turbomachinery. Also, the temperature, the pressure and the density at the outlet are in line with the reality using both ways.

Solver (Rotation axis must be in the Z direction):

PostProcess Paraview:


Attached Thumbnails
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► Energies: special issue on microscale and mesoscale modelling
  22 Dec, 2020
I will be editing this special issue of Energies

Contributions from the CFD community are welcome!
► Panel Method
  16 Dec, 2020
Panel method for 3D.
Attached Files
File Type: pptx Panel_method.pptx (62.9 KB, 2 views)
File Type: pdf Integral Equation Methods, Lecture 1.pdf (587.5 KB, 2 views)
► Title: rhoSimpleFoam for stationary compressible turbulent internals flows
    9 Dec, 2020
1/ Introduction

The scope of these notes is to discuss the simulation of stationary compressible turbulent flow in OpenFoam using the rhoSimpleFoam solver. We assume the reader to be familiar with the concept of stationary incompressible turbulent flows and the simulation of these flows in OpenFoam. This allows us to focus here on the transition from incompressible to compressible. We target internal flow with Mach number between 0.4 and 2. The application to external flows and/or higher mach Numbers is out of scope and described elsewhere. We thus consider the case of e.g. the mixing of various gasses injected into a vessel. In target scenarios the gas enters the vessel through a nozzle with small diameter at high mass flow rate. The assumption that the Mach number is bounded by 0.4 is violated in proximity of the nozzle outlet. An incompressible formulation no longer applies. We limit ourselves to case in which the various gasses mix without chemically reacting. We do have the modeling of the reacting case as final scope however.

what is rhoSimpleFoam: Stationary compressible turbulent flow in OpenFoam can be modeled using the rhoSimpleFoam solver.

why this notes: A lot of information on rhoSimpleFoam can be found elsewhere (cite wiki, cite Moukalled). Our aim here is to provide timely and updated description of the solver that includes
* a description of the thermodynamics (rhoThermo vs. psiThermo); rhoSimpleFoam is a pressure based solver. We thus need to recover density (rho) from pressure (p) using either rhoThermo or psiThermo;
* a description of transonic option (when to use it, change in energy equation, what are its advantages in terms of computational cost, what its limitations);
* numerical stability, sign of source terms, the presence of pressure waves (or the absence thereof and techniques to avoid them, see e.g. \cite{pressure-waves-sprayFoam});
* the comparison of convergence criteria (residuals vs. outlet quantities);
* a description of possible limitations of the solver (what happens e.g. at sufficiently high Mach numbers, break-down of multigrid for pressure solve due to transport term, advice on when to switch to alternative density base and/or coupled solvers), comparison between segregated and coupled solvers (HISA project);

what concept are assumed to be known to the reader (and thus not discussed here):
rhoSimpleFoam is a pressure-based segregated solver that iterates between the pressure, velocity and energy fields using the SIMPLE (or SIMPLE-C) algorithm (and possibly turbulent quantities) (cite wikipedia, Malaseekara, Moukalled). rhoSimpleFoam thus builds on components developed elsewhere in OpenFoam. In particular
* from basic solvers in OpenFoam: cell-centered finite volume discretization for scalar fields, non-linear iteration for scalar fields, linear solvers, parallel set-up and solver run;
* from incompressible solvers in OpenFoam: Reynolds averaging, cell-centered finite volume discretization for pressure-velocity coupling, Rhie-Chow interpolation, SIMPLE iteration for pressure-velocity coupling, consistent SIMPLE (SIMPLEC);
* from incompressible solvers in OpenFoam: solving for turbulent quantities;

what is outside scope of this page: transient formulation (using local time stepping), turbulence modeling beyond RANS with two-equation turbulence models (thus no Reynolds stress model, no LES), no thermodynamics beyond the ideal gas law; no adaptive mesh refinement;

* wikipedia, RANS:
* CFD-Online, pressure-waves-sprayFoam:
* CFD-Online, e-vs-h-in-energy-equation-1:
* CFD-Online, e-vs-h-in-energy-equation-2:
* CFD-Online, e-vs-h-in-energy-equation-3: (It is generally more convergent and stable to solve for internal energy if the fluid is incompressible or weakly compressible.)

2/ Representation of the Thermodynamics

The representation of the thermodynamics or the equation of state in a finite volume computation requires a separate data structure. In OpenFoam, the thermodynamics of the fluid in represented by the fluidThermo class (collection of data and operations of this data). The fluidThermo class is a parent class for rhoThermo and psiThermo. Both classes store the density (rho), compressibility (psi) and dynamic viscosity (mu). Both of the latter classes allow to update the density once a new pressure field has been computed. This update is performed through the correct() member function.

* how exactly is thermophysicalTransport->correct() in rhoSimpleFoam.C and thermo.correct in EEqn.H complementary to each other?
* what is rhoDelta as argument of correct() in rhoThermo?
* what is the body of correct() in psiThermo left body?

3/ Pressure in Compressible Flow Computations

In an incompressible flow simulation, density is constant, the flow equations (conservation of mass and momentum) can be divided by density and the kinematic pressure (p/rho [m^2/s^2]) is solved for. In a compressible flow simulation instead, the static pressure (p [Pa]) is solved for. This will have an impact on the boundary conditions being imposed, and thus the case set-up in OpenFoam. More … .

* OpenFoam v2006 Users manual: various forms of the pressure:

4/ Internal Energy vs. Enthalpy in Compressible Flow Computations

1.4/ Analytical Considerations

Heat capacity at constant volume ([units]): cv
Heat capacity at constant volume ([units]): cp
Thermal conductivity ([units]): k (heat flux = k (temperature flux) )
Thermal diffusivity when solving for enthalpy (h [units]): \alpha_{h} = \kappa / (\rho cv)
Thermal diffusivity when solving for internal energy (e [units]): \alpha_{e} = \kappa / (\rho cp)
Prandtl number is ratio of momentum diffusivity (kinematic viscosity nu) and thermal diffusivity \alpha
Prandtl number when solving for enthalpy = \nu/\alpha = (cv \mu)/ k
Prandtl number when solving for internal energy = \nu/\alpha = (cp \mu)/ k

This difference are taken care of in the implementation. Details are here:

2.4/ Numerical Considerations

It is generally more convergent and stable to solve for internal energy (instead of enthalpy) if the fluid is incompressible or weakly compressible. See and . Keep energy positive by keeping source term positive. See book Patankar.


5/ Problem formulation

1.5/ Conservation Equations (conversation of mass, momentum and energy closed by an equation of state to which turbulent quantities are added)

Density no longer constant. Pressure is dynamic pressure. Various expressions for the energy (internal energy, enthalpy and temperate exists);

1.1.5/ Conversation of mass

2.1.5/ Conservation of momentum

3.1.5/ Conservation of energy

4.1.5/ Solving for turbulent quantities

5.1.5/ Update of density through thermo-physical quantities (psi = R T)

2.5/ Boundary conditions (inlet, outlet and walls)

6/ Segregated Solution via SIMPLE Algorithm

After finite volume discretization, flow equations need to solve. Given SIMPLE for incompressible flow (and implemented in e.g. simpleFoam), add two steps. First step is update of the density (using the equation of state). Second step is the solve for the energy (enthalpy or temperature);

7/ Implementation in rhoSimpleFoam in OpenFoam

Show here UEqn.H, pEqn.H and EEqn.H and turbulent quantities;

8/ Guidelines on using the Solver

1.8/ Starting from Initial Guess Provided by simpleFoam (see notes)

2.8/ Handle on converge
limitT and limitU (first print values, then limit)

9/ Tutorials

1.9/ sBend

2.9/ elbow

3.9/ Sandia Flame D

4.9/ reverseBurner


User guide/Wiki-1/Wiki-2/Code guide/Code Wiki
OpenFOAM Governance and Technical Committees
Report bugs/Request features: OpenFOAM (ESI-OpenCFD-Trademark)
Report bugs/Request features: FOAM-Extend (Wikki-FSB)
Report bugs: OpenFOAM (Foundation)
How to create a MWE.

10/ References
* Greenshield
* Moukalled
* Malalaseekara and Versteeg
* wiki on compressible Navier-Stokes Equations:
* SIMPLE algorithms:

11/ Open End
* is similar description above available elsewhere?
* when does rhoSimpleFoam break?
* how to add source terms for radiation and chemistry?
* how do implementation in various flavor of OpenFoam differ
► Problem installing OpenFoam-v1912 from source on Mac with Clang
  15 Nov, 2020
This problem bugged me for the whole day. Ultimately the reply from below saved my night.

However, i don't like the new file system solution since you could simply run a virtual box or a docker which are basically the same but much more elegant and portable. I want to have some approach that can install openfoam locally, and be used as a library instead of working in a separate environment.

Originally Posted by madgeogr View Post
Hi Alexey,
I have a problem (again) when i am following the instructions as given in
In particular, I have followed the steps without any problem until when I had to apply the patch with git:
git apply OpenFOAM-v1912.patch
When I opened the patch file, I show the flag: 404: Not Found. Where can I find the patch? When I visited your site, I show that you have patches for different versions of OpenFoam, but not for v1912.
If I download the most recent one, "OpenFOAM-7-0ebbff061.patch" and execute "git apply OpenFOAM-7-0ebbff061.patch" instead, do you think it will be OK?
► Min and Max Wavenumber
  26 Oct, 2020
Min and Max Wavenumber

Filippo Maria Denaro added an answer
December 7, 2017
the Nyquist theorem says that for a step sampling dt you can describe the smallest wavelenght 2*dt (three samples describe a sine). For a given period lenght T, the ratio T/(2*dt) gives the maximum wavenunber you can represent

curiosityFluids top

► Creating curves in blockMesh (An Example)
  29 Apr, 2019

In this post, I’ll give a simple example of how to create curves in blockMesh. For this example, we’ll look at the following basic setup:

As you can see, we’ll be simulating the flow over a bump defined by the curve:

y=H*\sin\left(\pi x \right)

First, let’s look at the basic blockMeshDict for this blocking layout WITHOUT any curves defined:

/*--------------------------------*- C++ -*----------------------------------*\
  =========                 |
  \\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox
   \\    /   O peration     | Website:
    \\  /    A nd           | Version:  6
     \\/     M anipulation  |
    version     2.0;
    format      ascii;
    class       dictionary;
    object      blockMeshDict;

// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //

convertToMeters 1;

    (-1 0 0)    // 0
    (0 0 0)     // 1
    (1 0 0)     // 2
    (2 0 0)     // 3
    (-1 2 0)    // 4
    (0 2 0)     // 5
    (1 2 0)     // 6
    (2 2 0)     // 7

    (-1 0 1)    // 8    
    (0 0 1)     // 9
    (1 0 1)     // 10
    (2 0 1)     // 11
    (-1 2 1)    // 12
    (0 2 1)     // 13
    (1 2 1)     // 14
    (2 2 1)     // 15

    hex (0 1 5 4 8 9 13 12) (20 100 1) simpleGrading (0.1 10 1)
    hex (1 2 6 5 9 10 14 13) (80 100 1) simpleGrading (1 10 1)
    hex (2 3 7 6 10 11 15 14) (20 100 1) simpleGrading (10 10 1)


        type patch;
            (0 8 12 4)
        type patch;
            (3 7 15 11)
        type wall;
            (0 1 9 8)
            (1 2 10 9)
            (2 3 11 10)
        type patch;
            (4 12 13 5)
            (5 13 14 6)
            (6 14 15 7)
        type empty;
            (8 9 13 12)
            (9 10 14 13)
            (10 11 15 14)
            (1 0 4 5)
            (2 1 5 6)
            (3 2 6 7)

// ************************************************************************* //

This blockMeshDict produces the following grid:

It is best practice in my opinion to first make your blockMesh without any edges. This lets you see if there are any major errors resulting from the block topology itself. From the results above, we can see we’re ready to move on!

So now we need to define the curve. In blockMesh, curves are added using the edges sub-dictionary. This is a simple sub dictionary that is just a list of interpolation points:

        polyLine 1 2
                (0	0       0)
                (0.1	0.0309016994    0)
                (0.2	0.0587785252    0)
                (0.3	0.0809016994    0)
                (0.4	0.0951056516    0)
                (0.5	0.1     0)
                (0.6	0.0951056516    0)
                (0.7	0.0809016994    0)
                (0.8	0.0587785252    0)
                (0.9	0.0309016994    0)
                (1	0       0)

        polyLine 9 10
                (0	0       1)
                (0.1	0.0309016994    1)
                (0.2	0.0587785252    1)
                (0.3	0.0809016994    1)
                (0.4	0.0951056516    1)
                (0.5	0.1     1)
                (0.6	0.0951056516    1)
                (0.7	0.0809016994    1)
                (0.8	0.0587785252    1)
                (0.9	0.0309016994    1)
                (1	0       1)

The sub-dictionary above is just a list of points on the curve y=H\sin(\pi x). The interpolation method is polyLine (straight lines between interpolation points). An alternative interpolation method could be spline.

The following mesh is produced:

Hopefully this simple example will help some people looking to incorporate curved edges into their blockMeshing!


This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via, and owner of theOPENFOAM®  andOpenCFD®  trademarks.

► Creating synthetic Schlieren and Shadowgraph images in Paraview
  28 Apr, 2019

Experimentally visualizing high-speed flow was a serious challenge for decades. Before the advent of modern laser diagnostics and velocimetry, the only real techniques for visualizing high speed flow fields were the optical techniques of Schlieren and Shadowgraph.

Today, Schlieren and Shadowgraph remain an extremely popular means to visualize high-speed flows. In particular, Schlieren and Shadowgraph allow us to visualize complex flow phenomena such as shockwaves, expansion waves, slip lines, and shear layers very effectively.

In CFD there are many reasons to recreate these types of images. First, they look awesome. Second, if you are doing a study comparing to experiments, occasionally the only full-field data you have could be experimental images in the form of Schlieren and Shadowgraph.

Without going into detail about Schlieren and Shadowgraph themselves, primarily you just need to understand that Schlieren and Shadowgraph represent visualizations of the first and second derivatives of the flow field refractive index (which is directly related to density).

In Schlieren, a knife-edge is used to selectively cut off light that has been refracted. As a result you get a visualization of the first derivative of the refractive index in the direction normal to the knife edge. So for example, if an experiment used a horizontal knife edge, you would see the vertical derivative of the refractive index, and hence the density.

For Shadowgraph, no knife edge is used, and the images are a visualization of the second derivative of the refractive index. Unlike the Schlieren images, shadowgraph has no direction and shows you the laplacian of the refractive index field (or density field).

In this post, I’ll use a simple case I did previously ( as an example and produce some synthetic Schlieren and Shadowgraph images using the data.

So how do we create these images in paraview?

Well as you might expect, from the introduction, we simply do this by visualizing the gradients of the density field.

In ParaView the necessary tool for this is:

Gradient of Unstructured DataSet:

Finding “Gradient of Unstructured DataSet” using the Filters-> Search

Once you’ve selected this, we then need to set the properties so that we are going to operate on the density field:

Change the “Scalar Array” Drop down to the density field (rho), and change the name to Synthetic Schlieren

To do this, simply set the “Scalar Array” to the density field (rho), and change the name of the result Array name to SyntheticSchlieren. Now you should see something like this:

This is NOT a synthetic Schlieren Image – but it sure looks nice

There are a few problems with the above image (1) Schlieren images are directional and this is a magnitude (2) Schlieren and Shadowgraph images are black and white. So if you really want your Schlieren images to look like the real thing, you should change to black and white. ALTHOUGH, Cold and Hot, Black-Body radiation, and Rainbow Desatured all look pretty amazing.

To fix these, you should only visualize one component of the Synthetic Schlieren array at a time, and you should visualize using the X-ray color preset:

The results look pretty realistic:

Horizontal Knife Edge

Vertical Knife Edge

Now how about ShadowGraph?

The process of computing the shadowgraph field is very similar. However, recall that shadowgraph visualizes the Laplacian of the density field. BUT THERE IS NO LAPLACIAN CALCULATOR IN PARAVIEW!?! Haha no big deal. Just remember the basic vector calculus identity:

\nabla^2\left[\right]  = \nabla \cdot \nabla \left[\right]

Therefore, in order for us to get the Shadowgraph image, we just need to take the Divergence of the Synthetic Schlieren vector field!

To do this, we just have to use the Gradient of Unstructured DataSet tool again:

This time, Deselect “Compute Gradient” and the select “Compute Divergence” and change the Divergence array name to Shadowgraph.

Visualized in black and white, we get a very realistic looking synthetic Shadowgraph image:

Shadowgraph Image

So what do the values mean?

Now this is an important question, but a simple one to answer. And the answer is…. not much. Physically, we know exactly what these mean, these are: Schlieren is the gradient of the density field in one direction and Shadowgraph is the laplacian of the density field. But what you need to remember is that both Schlieren and Shadowgraph are qualitative images. The position of the knife edge, brightness of the light etc. all affect how a real experimental Schlieren or Shadowgraph image will look.

This means, very often, in order to get the synthetic Schlieren to closely match an experiment, you will likely have to change the scale of your synthetic images. In the end though, you can end up with extremely realistic and accurate synthetic Schlieren images.

Hopefully this post will be helpful to some of you out there. Cheers!

► Solving for your own Sutherland Coefficients using Python
  24 Apr, 2019

Sutherland’s equation is a useful model for the temperature dependence of the viscosity of gases. I give a few details about it in this post:

The law given by:

\mu=\mu_o\frac{T_o + C}{T+C}\left(\frac{T}{T_o}\right)^{3/2}

It is also often simplified (as it is in OpenFOAM) to:

\mu=\frac{C_1 T^{3/2}}{T+C}=\frac{A_s T^{3/2}}{T+T_s}

In order to use these equations, obviously, you need to know the coefficients. Here, I’m going to show you how you can simply create your own Sutherland coefficients using least-squares fitting Python 3.

So why would you do this? Basically, there are two main reasons for this. First, if you are not using air, the Sutherland coefficients can be hard to find. If you happen to find them, they can be hard to reference, and you may not know how accurate they are. So creating your own Sutherland coefficients makes a ton of sense from an academic point of view. In your thesis or paper, you can say that you created them yourself, and not only that you can give an exact number for the error in the temperature range you are investigating.

So let’s say we are looking for a viscosity model of Nitrogen N2 – and we can’t find the coefficients anywhere – or for the second reason above, you’ve decided its best to create your own.

By far the simplest way to achieve this is using Python and the Scipy.optimize package.

Step 1: Get Data

The first step is to find some well known, and easily cited, source for viscosity data. I usually use the NIST webbook (, but occasionally the temperatures there aren’t high enough. So you could also pull the data out of a publication somewhere. Here I’ll use the following data from NIST:

Temparature (K) Viscosity (Pa.s)
400 0.000022217
600 0.000029602
800 0.000035932
1000 0.000041597
1200 0.000046812
1400 0.000051704
1600 0.000056357
1800 0.000060829
2000 0.000065162

This data is the dynamics viscosity of nitrogen N2 pulled from the NIST database for 0.101 MPa. (Note that in these ranges viscosity should be only temperature dependent).

Step 2: Use python to fit the data

If you are unfamiliar with Python, this may seem a little foreign to you, but python is extremely simple.

First, we need to load the necessary packages (here, we’ll load numpy, scipy.optimize, and matplotlib):

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

Now we define the sutherland function:

def sutherland(T, As, Ts):
    return As*T**(3/2)/(Ts+T)

Next we input the data:



Then we fit the data using the curve_fit function from scipy.optimize. This function uses a least squares minimization to solve for the unknown coefficients. The output variable popt is an array that contains our desired variables As and Ts.

popt = curve_fit(sutherland, T, mu)

Now we can just output our data to the screen and plot the results if we so wish:

print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')


plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])

Overall the entire code looks like this:

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

def sutherland(T, As, Ts):
    return As*T**(3/2)/(Ts+T)

T=[200, 400, 600,


popt, pcov = curve_fit(sutherland, T, mu)
print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')


plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])

And the results for nitrogen gas in this range are As=1.55902E-6, and Ts=168.766 K. Now we have our own coefficients that we can quantify the error on and use in our academic research! Wahoo!


In this post, we looked at how we can simply use a database of viscosity-temperature data and use the python package scipy to solve for our unknown Sutherland viscosity coefficients. This NIST database was used to grab some data, and the data was then loaded into Python and curve-fit using scipy.optimize curve_fit function.

This task could also easily be accomplished using the Matlab curve-fitting toolbox, or perhaps in excel. However, I have not had good success using the excel solver to solve for unknown coefficients.

► Tips for tackling the OpenFOAM learning curve
  23 Apr, 2019

The most common complaint I hear, and the most common problem I observe with OpenFOAM is its supposed “steep learning curve”. I would argue however, that for those who want to practice CFD effectively, the learning curve is equally as steep as any other software.

There is a distinction that should be made between “user friendliness” and the learning curve required to do good CFD.

While I concede that other commercial programs have better basic user friendliness (a nice graphical interface, drop down menus, point and click options etc), it is equally as likely (if not more likely) that you will get bad results in those programs as with OpenFOAM. In fact, to some extent, the high user friendliness of commercial software can encourage a level of ignorance that can be dangerous. Additionally, once you are comfortable operating in the OpenFOAM world, the possibilities become endless and things like code modification, and bash and python scripting can make OpenFOAM worklows EXTREMELY efficient and powerful.

Anyway, here are a few tips to more easily tackle the OpenFOAM learning curve:

(1) Understand CFD

This may seem obvious… but its not to some. Troubleshooting bad simulation results or unstable simulations that crash is impossible if you don’t have at least a basic understanding of what is happening under the hood. My favorite books on CFD are:

(a) The Finite Volume Method in Computational Fluid Dynamics: An Advanced Introduction with OpenFOAM® and Matlab by
F. Moukalled, L. Mangani, and M. Darwish

(b) An introduction to computational fluid dynamics – the finite volume method – by H K Versteeg and W Malalasekera

(c) Computational fluid dynamics – the basics with applications – By John D. Anderson

(2) Understand fluid dynamics

Again, this may seem obvious and not very insightful. But if you are going to assess the quality of your results, and understand and appreciate the limitations of the various assumptions you are making – you need to understand fluid dynamics. In particular, you should familiarize yourself with the fundamentals of turbulence, and turbulence modeling.

(3) Avoid building cases from scratch

Whenever I start a new case, I find the tutorial case that most closely matches what I am trying to accomplish. This greatly speeds things up. It will take you a super long time to set up any case from scratch – and you’ll probably make a bunch of mistakes, forget key variable entries etc. The OpenFOAM developers have done a lot of work setting up the tutorial cases for you, so use them!

As you continue to work in OpenFOAM on different projects, you should be compiling a library of your own templates based on previous work.

(4) Using Ubuntu makes things much easier

This is strictly my opinion. But I have found this to be true. Yes its true that Ubuntu has its own learning curve, but I have found that OpenFOAM works seamlessly in the Ubuntu or any Ubuntu-like linux environment. OpenFOAM now has Windows flavors using docker and the like- but I can’t really speak to how well they work – mostly because I’ve never bothered. Once you unlock the power of Linux – the only reason to use Windows is for Microsoft Office (I guess unless you’re a gamer – and even then more and more games are now on Linux). Not only that- but the VAST majority of forums and troubleshooting associated with OpenFOAM you’ll find on the internet are from Ubuntu users.

I much prefer to use Ubuntu with a virtual Windows environment inside it. My current office setup is my primary desktop running Ubuntu – plus a windows VirtualBox, plus a laptop running windows that I use for traditional windows type stuff. Dual booting is another option, but seamlessly moving between the environments is easier.

(5) If you’re struggling, simplify

Unless you know exactly what you are doing, you probably shouldn’t dive into the most complicated version of whatever you are trying to solve/study. It is best to start simple, and layer the complexity on top. This way, when something goes wrong, it is much easier to figure out where the problem is coming from.

(6) Familiarize yourself with the cfd-online forum

If you are having trouble, the cfd-online forum is super helpful. Most likely, someone else is has had the same problem you have. If not, the people there are extremely helpful and overall the forum is an extremely positive environment for working out the kinks with your simulations.

(7) The results from checkMesh matter

If you run checkMesh and your mesh fails – fix your mesh. This is important. Especially if you are not planning on familiarizing yourself with the available numerical schemes in OpenFOAM, you should at least have a beautiful mesh. In particular, if your mesh is highly non-orthogonal, you will have serious problems. If you insist on using a bad mesh, you will probably need to manipulate the numerical schemes. A great source for how schemes should be manipulated based on mesh non-orthogonality is:

(8) CFL Number Matters

If you are running a transient case, the Courant-Freidrechs-Lewis (CFL) number matters… a lot. Not just for accuracy (if you are trying to capture a transient event) but for stability. If your time-step is too large you are going to have problems. There is a solid mathematical basis for this stability criteria for advection-diffusion problems. Additionally the Navier-Stokes equations are very non-linear and the complexity of the problem and the quality of your grid etc can make the simulation even less stable. When I have a transient simulation crash, if I know my mesh is OK, I decrease the timestep by a factor of 2. More often than not, this solves the problem.

For large time stepping, you can add outer loops to solvers based on the pimple algorithm, but you may end up losing important transient information. Excellent explanation of how to do this is given in the book by T. Holzmann:

For the record, this points falls into point (1) of Understanding CFD.

(9) Work through the OpenFOAM Wiki “3 Week” Series

If you are starting OpenFOAM for the first time, it is worth it to work through an organized program of learning. One such example (and there are others) is the “3 Weeks Series” on the OpenFOAM wiki:

If you are a graduate student, and have no job to do other than learn OpenFOAM, it will not take 3 weeks. This touches on all the necessary points you need to get started.

(10) OpenFOAM is not a second-tier software – it is top tier

I know some people who have started out with the attitude from the get-go that they should be using a different software. They think somehow Open-Source means that it is not good. This is a pretty silly attitude. Many top researchers around the world are now using OpenFOAM or some other open source package. The number of OpenFOAM citations has grown every year consistently (

In my opinion, the only place where mainstream commercial CFD packages will persist is in industry labs where cost is no concern, and changing software is more trouble than its worth. OpenFOAM has been widely benchmarked, and widely validated from fundamental flows to hypersonics (see any of my 17 publications using it for this). If your results aren’t good, you are probably doing something wrong. If you have the attitude that you would rather be using something else, and are bitter that your supervisor wants you to use OpenFOAM, when something goes wrong you will immediately think there is something wrong with the program… which is silly – and you may quit.

(11) Meshing… Ugh Meshing

For the record, meshing is an art in any software. But meshing is the only area where I will concede any limitation in OpenFOAM. HOWEVER, as I have outlined in my previous post ( most things can be accomplished in OpenFOAM, and there are enough third party meshing programs out there that you should have no problem.


Basically, if you are starting out in CFD or OpenFOAM, you need to put in time. If you are expecting to be able to just sit down and produce magnificent results, you will be disappointed. You might quit. And frankly, thats a pretty stupid attitude. However, if you accept that CFD and fluid dynamics in general are massive fields under constant development, and are willing to get up to speed, there are few limits to what you can accomplish.

Please take the time! If you want to do CFD, learning OpenFOAM is worth it. Seriously worth it.

This offering is notapproved or endorsed by OpenCFD Limited, producer and distributorof the OpenFOAM software via, and owner of theOPENFOAM®  andOpenCFD®  trade marks.

► Automatic Airfoil C-Grid Generation for OpenFOAM – Rev 1
  22 Apr, 2019
Airfoil Mesh Generated with

Here I will present something I’ve been experimenting with regarding a simplified workflow for meshing airfoils in OpenFOAM. If you’re like me, (who knows if you are) I simulate a lot of airfoils. Partly because of my involvement in various UAV projects, partly through consulting projects, and also for testing and benchmarking OpenFOAM.

Because there is so much data out there on airfoils, they are a good way to test your setups and benchmark solver accuracy. But going from an airfoil .dat coordinate file to a mesh can be a bit of pain. Especially if you are starting from scratch.

The two main ways that I have meshed airfoils to date has been:

(a) Mesh it in a C or O grid in blockMesh (I have a few templates kicking around for this
(b) Generate a “ribbon” geometry and mesh it with cfMesh
(c) Or back in the day when I was a PhD student I could use Pointwise – oh how I miss it.

But getting the mesh to look good was always sort of tedious. So I attempted to come up with a python script that takes the airfoil data file, minimal inputs and outputs a blockMeshDict file that you just have to run.

The goals were as follows:
(a) Create a C-Grid domain
(b) be able to specify boundary layer growth rate
(c) be able to set the first layer wall thickness
(e) be mostly automatic (few user inputs)
(f) have good mesh quality – pass all checkMesh tests
(g) Quality is consistent – meaning when I make the mesh finer, the quality stays the same or gets better
(h) be able to do both closed and open trailing edges
(i) be able to handle most airfoils (up to high cambers)
(j) automatically handle hinge and flap deflections

In Rev 1 of this script, I believe I have accomplished (a) thru (g). Presently, it can only hand airfoils with closed trailing edge. Hinge and flap deflections are not possible, and highly cambered airfoils do not give very satisfactory results.

There are existing tools and scripts for automatically meshing airfoils, but I found personally that I wasn’t happy with the results. I also thought this would be a good opportunity to illustrate one of the ways python can be used to interface with OpenFOAM. So please view this as both a potentially useful script, but also something you can dissect to learn how to use python with OpenFOAM. This first version of the script leaves a lot open for improvement, so some may take it and be able to tailor it to their needs!

Hopefully, this is useful to some of you out there!


You can download the script here:

Here you will also find a template based on the airfoil2D OpenFOAM tutorial.


(1) Copy to the root directory of your simulation case.
(2) Copy your airfoil coordinates in Selig .dat format into the same folder location.
(3) Modify to your desired values. Specifically, make sure that the string variable airfoilFile is referring to the right .dat file
(4) In the terminal run: python3
(5) If no errors – run blockMesh

You need to run this with python 3, and you need to have numpy installed


The inputs for the script are very simple:

ChordLength: This is simply the airfoil chord length if not equal to 1. The airfoil dat file should have a chordlength of 1. This variable allows you to scale the domain to a different size.

airfoilfile: This is a string with the name of the airfoil dat file. It should be in the same folder as the python script, and both should be in the root folder of your simulation directory. The script writes a blockMeshDict to the system folder.

DomainHeight: This is the height of the domain in multiples of chords.

WakeLength: Length of the wake domain in multiples of chords

firstLayerHeight: This is the height of the first layer. To estimate the requirement for this size, you can use the curiosityFluids y+ calculator

growthRate: Boundary layer growth rate

MaxCellSize: This is the max cell size along the centerline from the leading edge of the airfoil. Some cells will be larger than this depending on the gradings used.

The following inputs are used to improve the quality of the mesh. I have had pretty good results messing around with these to get checkMesh compliant grids.

BLHeight: This is the height of the boundary layer block off of the surfaces of the airfoil

LeadingEdgeGrading: Grading from the 1/4 chord position to the leading edge

TrailingEdgeGrading: Grading from the 1/4 chord position to the trailing edge

inletGradingFactor: This is a grading factor that modifies the the grading along the inlet as a multiple of the leading edge grading and can help improve mesh uniformity

trailingBlockAngle: This is an angle in degrees that expresses the angles of the trailing edge blocks. This can reduce the aspect ratio of the boundary cells at the top and bottom of the domain, but can make other mesh parameters worse.


12% Joukowski Airfoil


With the above inputs, the grid looks like this:

Mesh Quality:

These are some pretty good mesh statistics. We can also view them in paraView:

Clark-y Airfoil

The clark-y has some camber, so I thought it would be a logical next test to the previous symmetric one. The inputs I used are basically the same as the previous airfoil:

With these inputs, the result looks like this:

Mesh Quality:

Visualizing the mesh quality:

MH60 – Flying Wing Airfoil

Here is an example of a flying with airfoil (tested since the trailing edge is tilted upwards).


Again, these are basically the same as the others. I have found that with these settings, I get pretty consistently good results. When you change the MaxCellSize, firstLayerHeight, and Grading some modification may be required. However, if you just half the maxCell, and half the firstLayerHeight, you “should” get a similar grid quality just much finer.

Grid Quality:

Visualizing the grid quality


Hopefully some of you find this tool useful! I plan to release a Rev 2 soon that will have the ability to handle highly cambered airfoils, and open trailing edges, as well as control surface hinges etc.

The long term goal will be an automatic mesher with an H-grid in the spanwise direction so that the readers of my blog can easily create semi-span wing models extremely quickly!

Comments and bug reporting encouraged!

DISCLAIMER: This script is intended as an educational and productivity tool and starting point. You may use and modify how you wish. But I make no guarantee of its accuracy, reliability, or suitability for any use. This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via, and owner of the OPENFOAM®  and OpenCFD®  trademarks.

► Normal Shock Calculator
  20 Feb, 2019

Here is a useful little tool for calculating the properties across a normal shock.

If you found this useful, and have the need for more, visit One of STF Solutions specialties is providing our clients with custom software developed for their needs. Ranging from custom CFD codes to simpler targeted codes, scripts, macros and GUIs for a wide range of specific engineering purposes such as pipe sizing, pressure loss calculations, heat transfer calculations, 1D flow transients, optimization and more. Visit STF Solutions at for more information!

Disclaimer: This calculator is for educational purposes and is free to use. STF Solutions and curiosityFluids makes no guarantee of the accuracy of the results, or suitability, or outcome for any given purpose.

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► Accurate Aircraft Performance Predictions using Stallion 3D
  26 Feb, 2020

Stallion 3D uses your CAD design to simulate the performance of your aircraft.  This enables you to verify your design and compute quantities such as cruise speed, power required and range at a given cruise altitude. Stallion 3D is used to optimize the design before moving forward with building and testing prototypes.

The table below shows the results of Stallion 3D around the cruise angles of attack of the Cessna 402c aircraft.  The CAD design can be obtained from the OpenVSP hangar.

The results were obtained by simulating 5 angles of attack in Stallion 3D on an ordinary laptop computer running MS Windows 10 .  Given the aircraft geometry and flight conditions, Stallion 3D computed the CL, CD, L/D and other aerodynamic quantities.  With this accurate aerodynamics results, the preliminary performance data such as cruise speed, power, range and endurance can be obtained.

Lift Coefficient versus Angle of Attack computed with Stallion 3D

Lift to Drag Ratio versus True Airspeed at 10,000 feet

Power Required versus True Airspeed at 10,000 feet

The Stallion 3D results shows good agreement with the published data for the Cessna 402.  For example, the cruse speed of the aircraft at 10,000 feet is around 140 knots. This coincides with the speed at the maximum L/D (best range) shown in the graph and table above.

 More information about Stallion 3D can be found at the following link.

About Hanley Innovations
Hanley Innovations is a pioneer in developing user friendly and accurate software that is accessible to engineers, designers and students.  For more information, please visit >

► 5 Tips For Excellent Aerodynamic Analysis and Design
    8 Feb, 2020
Stallion 3D analysis of Uber Elevate eCRM-100 model

Being the best aerodynamics engineer requires meticulous planning and execution.  Here are 5 steps you can following to start your journey to being one of the best aerodynamicist.

1.  Airfoils analysis (VisualFoil) - the wing will not be better than the airfoil. Start with the best airfoil for the design.

2.  Wing analysis (3Dfoil) - know the benefits/limits of taper, geometric & aerodynamic twist, dihedral angles, sweep, induced drag and aspect ratio.

3. Stability analysis (3Dfoil) - longitudinal & lateral static & dynamic stability analysis.  If the airplane is not stable, it might not fly (well).

4. High Lift (MultiElement Airfoils) - airfoil arrangements can do wonders for takeoff, climb, cruise and landing.

5. Analyze the whole arrangement (Stallion 3D) - this is the best information you will get until you flight test the design.

About Hanley Innovations
Hanley Innovations is a pioneer in developing user friendly and accurate software the is accessible to engineers, designs and students.  For more information, please visit >

► Accurate Aerodynamics with Stallion 3D
  17 Aug, 2019

Stallion 3D is an extremely versatile tool for 3D aerodynamics simulations.  The software solves the 3D compressible Navier-Stokes equations using novel algorithms for grid generation, flow solutions and turbulence modeling. 

The proprietary grid generation and immersed boundary methods find objects arbitrarily placed in the flow field and then automatically place an accurate grid around them without user intervention. 

Stallion 3D algorithms are fine tuned to analyze invisid flow with minimal losses. The above figure shows the surface pressure of the BD-5 aircraft (obtained OpenVSP hangar) using the compressible Euler algorithm.

Stallion 3D solves the Reynolds Averaged Navier-Stokes (RANS) equations using a proprietary implementation of the k-epsilon turbulence model in conjunction with an accurate wall function approach.

Stallion 3D can be used to solve problems in aerodynamics about complex geometries in subsonic, transonic and supersonic flows.  The software computes and displays the lift, drag and moments for complex geometries in the STL file format.  Actuator disc (up to 100) can be added to simulate prop wash for propeller and VTOL/eVTOL aircraft analysis.

Stallion 3D is a versatile and easy-to-use software package for aerodynamic analysis.  It can be used for computing performance and stability (both static and dynamic) of aerial vehicles including drones, eVTOLs aircraft, light airplane and dragons (above graphics via Thingiverse).

More information about Stallion 3D can be found at:

► 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.

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► The Darkest Hour Before Dawn
    2 Jan, 2021

Happy 2021!

The year of 2020 will be remembered in history more than the year of 1918, when the last great pandemic hit the globe. As we speak, daily new cases in the US are on the order of 200,000, while the daily death toll oscillates around 3,000. According to many infectious disease experts, the darkest days may still be to come. In the next three months, we all need to do our very best by wearing a mask, practicing social distancing and washing our hands. We are also seeing a glimmer of hope with several recently approved COVID vaccines.

2020 will be remembered more for what Trump tried and is still trying to do, to overturn the results of a fair election. His accusations of wide-spread election fraud were proven wrong in Georgia and Wisconsin through multiple hand recounts. If there was any truth to the accusations, the paper recounts would have uncovered the fraud because computer hackers or software cannot change paper votes.

Trump's dictatorial habits were there for the world to see in the last four years. Given another 4-year term, he might just turn a democracy into a Trump dictatorship. That's precisely why so many voted in the middle of a pandemic. Biden won the popular vote by over 7 million, and won the electoral college in a landslide. Many churchgoers support Trump because they dislike Democrats' stances on abortion, LGBT rights, et al. However, if a Trump dictatorship becomes reality, religious freedom may not exist any more in the US. 

Is the darkest day going to be January 6th, 2021, when Trump will make a last-ditch effort to overturn the election results in the Electoral College certification process? Everybody knows it is futile, but it will give Trump another opportunity to extort money from his supporters.   

But, the dawn will always come. Biden will be the president on January 20, 2021, and the pandemic will be over, perhaps as soon as 2021.

The future of CFD is, however, as bright as ever. On the front of large eddy simulation (LES), high-order methods and GPU computing are making LES more efficient and affordable. See a recent story from GE.

the darkest hour is just before dawn...

► Facts, Myths and Alternative Facts at an Important Juncture
  21 Jun, 2020
We live in an extraordinary time in modern human history. A global pandemic did the unthinkable to billions of people: a nearly total lock-down for months.  Like many universities in the world, KU closed its doors to students since early March of 2020, and all courses were offered online.

Millions watched in horror when George Floyd was murdered, and when a 75 year old man was shoved to the ground and started bleeding from the back of his skull...

Meanwhile, Trump and his allies routinely ignore facts, fabricate alternative facts, and advocate often-debunked conspiracy theories to push his agenda. The political system designed by the founding fathers is assaulted from all directions. The rule of law and the free press are attacked on a daily basis. One often wonders how we managed to get to this point, and if the political system can survive the constant sabotage...It appears the struggle between facts, myths and alternative facts hangs in the balance.

In any scientific discipline, conclusions are drawn, and decisions are made based on verifiable facts. Of course, we are humans, and honest mistakes can be made. There are others, who push alternative facts or misinformation with ulterior motives. Unfortunately, mistaken conclusions and wrong beliefs are sometimes followed widely and become accepted myths. Fortunately, we can always use verifiable scientific facts to debunk them.

There have been many myths in CFD, and quite a few have been rebutted. Some have continued to persist. I'd like to refute several in this blog. I understand some of the topics can be very controversial, but I welcome fact-based debate.

Myth No. 1 - My LES/DNS solution has no numerical dissipation because a central-difference scheme is used.

A central finite difference scheme is indeed free of numerical dissipation in space. However, the time integration scheme inevitably introduces both numerical dissipation and dispersion. Since DNS/LES is unsteady in nature, the solution is not free of numerical dissipation.  

Myth No. 2 - You should use non-dissipative schemes in LES/DNS because upwind schemes have too much numerical dissipation.

It sounds reasonable, but far from being true. We all agree that fully upwind schemes (the stencil shown in Figure 1) are bad. Upwind-biased schemes, on the other hand, are not necessarily bad at all. In fact, in a numerical test with the Burgers equation [1], the upwind biased scheme performed better than the central difference scheme because of its smaller dispersion error. In addition, the numerical dissipation in the upwind-biased scheme makes the simulation more robust since under-resolved high-frequency waves are naturally damped.   

Figure 1. Various discretization stencils for the red point
The Riemann solver used in the DG/FR/CPR scheme also introduces a small amount of dissipation. However, because of its small dispersion error, it out-performs the central difference and upwind-biased schemes. This study shows that both dissipation and dispersion characteristics are equally important. Higher order schemes clearly perform better than a low order non-dissipative central difference scheme.  

Myth No. 3 - Smagorisky model is a physics based sub-grid-scale (SGS) model.

There have been numerous studies based on experimental or DNS data, which show that the SGS stress produced with the Smagorisky model does not correlate with the true SGS stress. The role of the model is then to add numerical dissipation to stablize the simulations. The model coefficient is usually determined by matching a certain turbulent energy spectrum. The fact suggests that the model is purely numerical in nature, but calibrated for certain numerical schemes using a particular turbulent energy spectrum. This calibration is not universal because many simulations produced worse results with the model.

► What Happens When You Run a LES on a RANS Mesh?
  27 Dec, 2019

Surely, you will get garbage because there is no way your LES will have any chance of resolving the turbulent boundary layer. As a result, your skin friction will be way off. Therefore, your drag and lift will be a total disaster.

To actually demonstrate this point of view, we recently embarked upon a numerical experiment to run an implicit large eddy simulation (ILES) of the NASA CRM high-lift configuration from the 3rd AIAA High-Lift Prediction Workshop. The flow conditions are: Mach = 0.2, Reynolds number = 3.26 million based on the mean aerodynamic chord, and the angle of attack = 16 degrees.

A quadratic (Q2) mesh was generated by Dr. Steve Karman of Pointwise, and is shown in Figure 1.

 Figure 1. Quadratic mesh for the NASA CRM high-lift configuration (generated by Pointwise)

The mesh has roughly 2.2 million mixed elements, and is highly clustered near the wall with an average equivalent y+ value smaller than one. A p-refinement study was conducted to assess the mesh sensitivity using our high-order LES tool based on the FR/CPR method, hpMusic. Simulations were performed with solution polynomial degrees of p = 1, 2 and 3, corresponding to 2nd, 3rd and 4th orders in accuracy respectively. No wall-model was used. Needless to say, the higher order simulations captured finer turbulence scales, as shown in Figure 2, which displays the iso-surfaces of the Q-criteria colored by the Mach number.    

p = 1

p = 2

p = 3
Figure 2. Iso-surfaces of the Q-criteria colored by the Mach number

Clearly the flow is mostly laminar on the pressure side, and transitional/turbulent on the suction side of the main wing and the flap. Although the p = 1 simulation captured the least scales, it still correctly identified the laminar and turbulent regions. 

The drag and lift coefficients from the present p-refinement study are compared with experimental data from NASA in Table I. Although the 2nd order results (p = 1) are quite different than those of higher orders, the 3rd and 4th order results are very close, demonstrating very good p-convergence in both the lift and drag coefficients. The lift agrees better with experimental data than the drag, bearing in mind that the experiment has wind tunnel wall effects, and other small instruments which are not present in the computational model. 

Table I. Comparison of lift and drag coefficients with experimental data

p = 1
p = 2
p = 3

This exercise seems to contradict the common sense logic stated in the beginning of this blog. So what happened? The answer is that in this high-lift configuration, the dominant force is due to pressure, rather than friction. In fact, 98.65% of the drag and 99.98% of the lift are due to the pressure force. For such flow problems, running a LES on a RANS mesh (with sufficient accuracy) may produce reasonable predictions in drag and lift. More studies are needed to draw any definite conclusion. We would like to hear from you if you have done something similar.

This study will be presented in the forthcoming AIAA SciTech conference, to be held on January 6th to 10th, 2020 in Orlando, Florida. 

► Not All Numerical Methods are Born Equal for LES
  15 Dec, 2018
Large eddy simulations (LES) are notoriously expensive for high Reynolds number problems because of the disparate length and time scales in the turbulent flow. Recent high-order CFD workshops have demonstrated the accuracy/efficiency advantage of high-order methods for LES.

The ideal numerical method for implicit LES (with no sub-grid scale models) should have very low dissipation AND dispersion errors over the resolvable range of wave numbers, but dissipative for non-resolvable high wave numbers. In this way, the simulation will resolve a wide turbulent spectrum, while damping out the non-resolvable small eddies to prevent energy pile-up, which can drive the simulation divergent.

We want to emphasize the equal importance of both numerical dissipation and dispersion, which can be generated from both the space and time discretizations. It is well-known that standard central finite difference (FD) schemes and energy-preserving schemes have no numerical dissipation in space. However, numerical dissipation can still be introduced by time integration, e.g., explicit Runge-Kutta schemes.     

We recently analysed and compared several 6th-order spatial schemes for LES: the standard central FD, the upwind-biased FD, the filtered compact difference (FCD), and the discontinuous Galerkin (DG) schemes, with the same time integration approach (an Runge-Kutta scheme) and the same time step.  The FCD schemes have an 8th order filter with two different filtering coefficients, 0.49 (weak) and 0.40 (strong). We first show the results for the linear wave equation with 36 degrees-of-freedom (DOFs) in Figure 1.  The initial condition is a Gaussian-profile and a periodic boundary condition was used. The profile traversed the domain 200 times to highlight the difference.

Figure 1. Comparison of the Gaussian profiles for the DG, FD, and CD schemes

Note that the DG scheme gave the best performance, followed closely by the two FCD schemes, then the upwind-biased FD scheme, and finally the central FD scheme. The large dispersion error from the central FD scheme caused it to miss the peak, and also generate large errors elsewhere.

Finally simulation results with the viscous Burgers' equation are shown in Figure 2, which compares the energy spectrum computed with various schemes against that of the direct numerical simulation (DNS). 

Figure 2. Comparison of the energy spectrum

Note again that the worst performance is delivered by the central FD scheme with a significant high-wave number energy pile-up. Although the FCD scheme with the weak filter resolved the widest spectrum, the pile-up at high-wave numbers may cause robustness issues. Therefore, the best performers are the DG scheme and the FCD scheme with the strong filter. It is obvious that the upwind-biased FD scheme out-performed the central FD scheme since it resolved the same range of wave numbers without the energy pile-up. 

► 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.

Convergent Science Blog top

► 2020: THE YEAR of CFD (Computing From a Distance)
  23 Dec, 2020

We’ve reached the end of 2020, and I think it’s fair to say this year did not go as planned. The coronavirus pandemic disrupted our lives and brought on unexpected challenges and hardships. However, this difficult time has also highlighted the resiliency of people all around the globe—we have adapted and innovated to meet these challenges head on. At Convergent Science, that meant finding new ways to communicate and collaborate to ensure we could continue to deliver the best possible software and support to our users, all while keeping our employees safe.

Despite the pandemic, we experienced exciting opportunities, advancements, and milestones at Convergent Science this past year. We hosted two virtual conferences, continued to expand into new markets and new application areas, began new collaborations, increased our employee count, and, of course, continued to improve and develop CONVERGE. 

CONVERGE 3.1: A Preview

We have spent much of 2020 developing the next major release of our CONVERGE CFD software: version 3.1. There’s a lot to look forward to in CONVERGE 3.1, which will be released next year. In CONVERGE 3.0, we added the ability to incorporate stationary inlaid meshes into a simulation. In 3.1, these inlaid meshes will be able to move within the underlying Cartesian grid. For example, you will be able to create an inlaid mesh around each of the intake valves in an IC engine simulation, and the mesh will move with the valve as it opens and closes. With this method, you can achieve high grid resolution normal to the valve surface using significantly fewer cells than with traditional fixed embedding. 

Another enhancement will allow you to use different solvers, meshes, physical models, and chemical mechanisms for different streams (i.e., portions of the domain). This means you will be able to tailor your simulation settings to each stream, which will improve solver speed and numerical performance. CONVERGE 3.1 will also feature new sealing capabilities that enable you to have any objects come into contact with one another in your simulation or have objects enter or leave your simulation. 

Furthermore, CONVERGE 3.1 will support solid- and gas-phase parcels in addition to the traditional liquid-phase parcels. This can be useful when modeling, for example, soot or injectors operating at flash-boiling conditions. CONVERGE 3.1 will also feature an improved steady-state solver that will provide significant improvements in speed, and we have enhanced our fluid-structure interaction, volume of fluid, combustion, and emissions modeling capabilities. There are many more exciting features and enhancements coming in 3.1, so stay tuned for more information!

Pursuing High-Performance Computing with Oracle

Improving the scalability of CONVERGE continues to be a strong focus of our development efforts. We work with several companies and institutions, testing CONVERGE on different high-performance computing (HPC) architectures and optimizing our software to ensure good scaling. To that end, we were thrilled to begin a new collaboration this year with Oracle, a leader in cloud computing and enterprise software. In our benchmark testing, we have seen near perfect scaling of CONVERGE on Oracle Cloud Infrastructure on thousands of cores. This collaboration presents a great opportunity for CONVERGE users to take advantage of Oracle’s advanced HPC resources to efficiently run large-scale simulations in the cloud. 

Best Use of HPC in Industry

For the second year in a row, we were honored to win an HPCwire award for research performed with our colleagues at Aramco Research Center–Detroit and Argonne National Laboratory. This year, we received the HPCwire Readers’ Choice Award for Best Use of HPC in Industry for our work using HPC and machine learning to accelerate injector design optimization for next-generation high-efficiency, low-emissions engines. Our collaborative work is forging the way to leverage HPC, novel experimental measurements, and CFD to perform rapid optimization studies and reduce our carbon footprint from transportation.

Computational Chemistry Consortium

In another collaborative effort, the Computational Chemistry Consortium (C3) made significant progress in 2020. Co-founded by Convergent Science, C3 is working to create the most accurate and comprehensive chemical reaction mechanism for automotive fuels that includes NOx and PAH chemistry to model emissions. The first version of the mechanism was completed last year and is currently available to C3’s industry sponsors. Once the mechanism is published, it will be released to the public on This past year, C3 has continued to refine the mechanism, which has now reached version 2.1. The results of these efforts have been rewarding—we’ve seen a significant decrease in error in selected validation cases. The next year of the consortium will focus on increasing the accuracy of the NOx and PAH chemistry. To that end, C3 welcomed a new member this year, Dr. Stephen Klippenstein from Argonne National Laboratory. Dr. Klippenstein will perform high-level ab initio calculations of rate constants in NOx chemistry. Ultimately, the C3 mechanism is expected to be the first publicly available mechanism that includes everything from hydrogen chemistry all the way up to PAH chemistry in a single high-fidelity mechanism.

Driving Mobility Forward

In 2020, we celebrated our 10-year anniversary of collaboration with Argonne National Laboratory. Over the past decade, this collaboration has helped us extend CONVERGE’s capabilities and broach new application areas. We have performed cutting-edge research in the transportation field, developing new methods and models that are proving to be instrumental in designing the next generation of engines. In the aerospace field, we’ve broken ground in applying CFD to gas turbines, rotating detonation engines, drones, and more. We’ve made great strides in the last ten years, and we’re looking forward to the next decade of collaboration!

Bringing CONVERGE Online

Every year, we look forward to getting together with our users, discussing the latest exciting CONVERGE research and having some fun at our user conferences. When the pandemic struck and countries began locking down earlier this year, we were determined to still hold our 2020 CONVERGE User Conference–Europe, even if it looked a bit different. Our conference was scheduled for the end of March, so we didn’t have much time to transition from an in-person to an online event, but our team was up for the challenge. In less than three weeks, we planned a whole new event and successfully held one of the first pandemic-era virtual conferences. We were so pleased with the result! More than 400 attendees from around the world tuned in for an excellent lineup of technical presentations, which spanned topics from IC engines to compressors to electric motors and battery packs. 

While we hoped to hold our North American user conference in Detroit later in the year, the continued pandemic made that impossible. Once again, we took to the internet. We incorporated some more networking opportunities, including various social groups and discussion topics, and created some fun polls to help attendees get to know one another. We were also able to offer our usual slate of conference-week CONVERGE training and virtual exhibit booths for our sponsors. The presentations at this conference showcased the breadth and diversity of applications for which CONVERGE is suited, with speakers discussing rockets, gas turbines, exhaust aftertreatment, biomedical applications, renewable energy, and electromobility in addition to a host of IC engine-related topics.

It’s hard to know what 2021 will look like, but rest assured we will be hosting more conferences, virtual or otherwise. We’re looking forward to the day we can get together in person once again!

CONVERGE Around the World

Even with the pandemic, 2020 was an exciting and productive year for Convergent Science around the globe. We gained nearly a dozen new employees, including bringing on team members in newly created roles to help expand our relationships with universities and to increase our in-house CAD design capabilities. We also continued to find new markets for CONVERGE as we entered the emobility, rocket, and burner industries. 

Our Indian office flourished in 2020. Since its creation three years ago, Convergent Science India has grown to more than 20 employees, adding nine new team members this year alone. To accommodate our growing team, we moved to a spacious new building in Pune. Our team in India expanded our global reach, bringing new academic and industry clients on board. In addition, we continued to work on growing our presence in new applications such as gas turbines, aftertreatment, motor cooling, battery failure, oil churning, and spray painting.

In Europe, despite the challenging circumstances, we increased our client base and our license sales considerably, and we were able to successfully and seamlessly support our customers to help them achieve their CFD goals. In addition to moving our European CONVERGE user conference online in record time, we attended and exhibited at many virtual tradeshows and events and are looking forward to attending in-person conferences as soon as it is safe to do so.

Our partners at IDAJ continued to do excellent work supporting our customers in Japan, China, and Korea. Due to the pandemic, they held their first-ever IDAJ Conference Online 2020, where they had both live lectures and Q&A sessions as well as on-demand streaming content. While they support many IC engine clients, they are also supporting clients working on other applications such as motor cooling, battery failure, oil churning, and spray painting.

Looking Ahead

2020 was a difficult year for many of us, but I am impressed and inspired by the way the CFD community and beyond has come together to make the most of a challenging situation. And the future looks bright! We’re looking forward to releasing CONVERGE 3.1 and helping our users take advantage of the increased functionality and new features that will be available. We’re excited to expand our presence in electromobility, renewable energy, aerospace, and other new fields. In the upcoming year, we look forward to forming new collaborations and strengthening existing partnerships to promote innovation and keep CONVERGE on the cutting-edge of CFD software.

Can we help you meet your 2021 CFD goals? Contact us today!

► Cool Your Pistons Like You Cool Your Cocktail
    8 Dec, 2020

In my first year of graduate school, a friend always filled up her water bottle, dropped some ice cubes into it, and then shook it up in order to cool the water faster. If she had added the ice cubes and let the water bottle sit, eventually all the water would equilibrate to the same temperature, but that would take a while without any movement—the water next to the ice cubes would cool down quickly, but the water farther away would cool down at a much slower rate. By shaking it up, she agitated the water and ice so that the ice came into contact with more of the warm water that needed to be cooled. This “cocktail shaker effect,” I would later find out, also applies to cooling engines. 

Combustion in an internal combustion (IC) engine occurs on top of the piston, which means that there is an extraordinary amount of heat generated on the piston crown. If left unmediated, this heat can cause the piston to break. The threat of piston damage is particularly high in diesel engines because more heat is generated in the cylinder than in a traditional gasoline engine. Unlike a bottle of warm water, though, we can’t just drop a few ice cubes into the cylinder to act as a heat sink. 

Here we see how engineers can use CONVERGE to efficiently solve the problem of cooling the piston so that it isn’t damaged by heat. The idea is simple—use engine oil as a heat sink—but the implementation is complex since the piston is constantly moving and nothing can be in contact with the piston crown inside the cylinder. 

Figure 1: Image of an oil jet-cooled piston with relevant features labeled.

Since the heat sink can’t be inside the cylinder on the piston crown, there is an oil gallery in contact with the undercrown of the piston, as shown in Figure 1. Engine oil is taken through a pump, pressurized, and constantly sprayed at the oil gallery inlet hole. In the video below, you will see how the oil enters the gallery, and, as the piston motion continues, the oil sloshes inside the oil gallery, absorbing heat from the piston before exiting the outlet hole on the other side of the gallery. 

There are several factors that are important to consider when designing this type of cooling system, all of which CONVERGE is well-equipped to handle. What size and shape should the inlet and outlet holes be to capture the stream of oil? How much oil will enter the gallery compared to how much was sprayed (i.e., capture ratio)? What is the best design of the gallery so that the oil effectively absorbs heat from the piston? What ratio of the gallery volume should be occupied (i.e., fill ratio) to ensure that the oil can move and absorb heat efficiently? CONVERGE provides answers to these questions and others through a volume of fluid (VOF) simulation

Figure 2: CONVERGE’s Adaptive Mesh Refinement refines the mesh around the oil gallery, where more heat transfer occurs.

Because a simple boundary condition is not predictive of the heat transfer throughout the entire piston, we use conjugate heat transfer (CHT) to more accurately predict the piston cooling by solving the heat distribution inside the piston. Understanding how heat transfer affects the whole piston is an essential step toward designing a geometry that will effectively cool more than just the piston surface. While CHT can be computationally expensive due to the difference in time-scales of heat transfer in the solid and fluid regions, CONVERGE provides the option to use super-cycling, which can significantly reduce the computational cost of this type of simulation.

In the video below, you will see how the above factors have been optimized to dissipate heat from the piston crown and throughout the piston as a whole. In the video on the left, you can watch the temperature contours change during the simulation as heat dissipates. The second view shows how CONVERGE’s Adaptive Mesh Refinement (AMR) is in action throughout the simulation, providing increased grid resolution near the inlet and around the oil gallery, where it is needed most. 

Ready to run your own simulations to optimize oil jet piston cooling? Contact us today!

Video of an oil jet-cooled piston. The first view shows the temperature contours. The second view contains the same piston with mesh visualized, showing that the mesh is more refined around the oil gallery where more heat transfer occurs. As the simulation proceeds, AMR provides increased grid resolution near the features of interest.

► The Collaboration Effect: Advancing Engines Through Simulation & Experimentation
    9 Nov, 2020

From the Argonne National Laboratory + Convergent Science Blog Series

Through the collaboration between Argonne National Laboratory and Convergent Science, we provide fundamental research that enables manufacturers to design cleaner and more efficient engines by optimizing combustion. 

–Doug Longman, Manager of Engine Research at Argonne National Laboratory

The internal combustion engine has come a long way since its inception—the engine in your car today is significantly quieter, cleaner, and more efficient than its 1800s-era counterpart. For many years, the primary means of achieving these advances was experimentation. Indeed, we have experiments to thank for a myriad of innovations, from fuel injection systems to turbocharging to Wankel engines.

More recently, a new tool was added to the engine designer’s toolbox: simulation. Beginning in the 1970s and ‘80s, computational fluid dynamics (CFD) opened the door to a new level of refinement and optimization.

“One of the really cool things about simulation is that you can look at physics that cannot be easily captured in an experiment—details of the flow that might be blocked from view, for example,” says Eric Pomraning, Co-Owner of Convergent Science.

Of course, experiments remain vitally important to engine research, since CFD simulations model physical processes, and experiments are necessary to validate your results and ground your simulations in reality.

Argonne National Laboratory and Convergent Science combine these two approaches—experiments and simulation—to further improve the internal combustion engine. Two of the main levers we have to control the efficiency and emissions of an engine are the fuel injection system and the ignition system, both of which have been significant areas of focus during the collaboration.

Fuel Injection

The combustion process in an internal combustion engine really begins with fuel injection. The physics of injection determine how the fuel and air in the cylinder will mix, ignite, and ultimately combust. 

Argonne National Laboratory is home to the Advanced Photon Source (APS), a DOE Office of Science User Facility. The APS provides a unique opportunity to characterize the internal passages of injector nozzles with incredibly high spatial resolution through the use of high-energy x-rays. This data is invaluable for developing accurate CFD models that manufacturers can use in their design processes.

Early on in the collaboration, Christopher Powell, Principal Engine Research Scientist at Argonne, and his team leveraged the APS to investigate needle motion in an injector.

“Injector manufacturers had long suspected that off-axis motion of the injector valve could be present. But they never had a way to measure it before, so they weren’t sure how it impacted fuel injection,” says Chris.

The x-ray studies performed at the APS were the first in the world to confirm that some injector needles do exhibit radial motion in addition to the intended axial motion, a phenomenon dubbed “needle wobble.” Argonne and Convergent Science engineers simulated this experimental data in CONVERGE, prescribing radial motion to the injector needle. They found that needle wobble can substantially impact the fuel distribution as it exits the injector. Manufacturers were able to apply the results of this research to design injectors with a more predictable spray pattern, which, in turn, leads to a more predictable combustion event.

More recently, researchers at Argonne have used the APS to investigate the shape of fuel injector flow passages and characterize surface roughness. Imperfections in the geometry can influence the spray and the subsequent downstream engine processes. 

“If we use a CAD geometry, which is smooth, we will miss out on some of the physics, like cavitation, that can be triggered by surface imperfections,” says Sameera Wijeyakulasuriya, Senior Principal Engineer at Convergent Science. “But if we use the x-ray scanned geometry, we can incorporate those surface imperfections into our numerical models, so we can see how the flow field behaves and responds.”

Argonne and Convergent Science engineers performed internal nozzle flow simulations that used the real injector geometries and that incorporated real needle motion.1 Using the one-way coupling approach in CONVERGE, they mapped the results of the internal flow simulations to the exit of each injector orifice to initialize a multi-plume Lagrangian spray simulation. As you can see in Figure 1, the surface roughness and needle motion significantly impact the spray plume—the one-way coupling approach captures features that the standard rate of injection (ROI) method could not. In addition, the real injector parameters introduce orifice-to-orifice variability, which affects the combustion behavior down the line.

Figure 1: Comparison of the spray plume (top) and the effect of orifice-to-orifice variability on combustion behavior (bottom) simulated using the standard ROI method (left) and the one-way coupling method (right), which accounts for the real injector geometry and needle motion.

The real injector geometries not only allow for more accurate computational simulations, but they also can serve as a diagnostic tool for manufacturers to assess how well their manufacturing processes are producing the desired nozzle shape and size.

Spark Ignition

Accurately characterizing fuel injection sets the stage for the next lever we can optimize in our engine: ignition. In spark-ignition engines, the ignition event initiates the formation of the flame kernel, the growth of the flame kernel, and the flame propagation mechanism.

“In the past, ignition was just modeled as a hot source—dumping an amount of energy in a small region and hoping it transitions to a flame. The amount of physics in the process was very limited,” says Sibendu Som, Manager of the Computational Multi-Physics Section at Argonne.

These simplified models are adequate for most stable engine conditions, but you can run into trouble when you start simulating more advanced combustion concepts. In these scenarios, the simplified ignition models fall short in replicating experimental data. Over the course of their collaboration, Argonne and Convergent Science have incorporated more physics into ignition models to make them robust for a variety of engine conditions. 

For example, high-performance spark-ignition engines often feature high levels of dilution and increased levels of turbulence. These conditions can have a significant impact on the ignition process, which consequently affects combustion stability and cycle-to-cycle variation (CCV). To capture the elongation and stretch experienced by the spark channel under highly turbulent conditions, Argonne and Convergent Science engineers developed a new ignition model, the hybrid Lagrangian-Eulerian spark-ignition (LESI) model.

In Figure 2, you can see that the LESI model more accurately captures the behavior of the spark under turbulent conditions compared to a commonly used energy deposition model.2 The LESI model will be available in future versions of CONVERGE, accessible to manufacturers to help them better understand ignition and mitigate CCV.

Figure 2: Comparison of experimental results (A) with a commonly used energy deposition model (B) and the LESI model (C) at turbulent engine-like conditions.

Cycle-to-Cycle Variation

Ideally, every cycle of an internal combustion engine would be exactly identical to ensure smooth operation. In real engines, variability in the injection, ignition, and combustion means that not every cycle will be the same. Cyclic variability is especially prevalent in high-efficiency engines that push the limits of combustion stability. Extreme cycles can cause engine knock and misfires—and they can influence emissions.

“Not every engine cycle generates significant emissions. Often they’re primarily formed only during rare cycles—maybe one or two out of a hundred,” says Keith Richards, Co-Owner of Convergent Science. “Being able to capture cyclic variability will ultimately allow us to improve our predictive capabilities for emissions.”

Modeling CCV requires simulating numerous engine cycles, which is a highly (and at times prohibitively) time-consuming process. Several years ago, Keith suggested a potential solution—starting several engine cycles concurrently, each with a small perturbation to the flow field, which allows each simulation to develop into a unique solution. 

Argonne and Convergent Science compared this approach—called the concurrent perturbation method (CPM)—to the traditional approach of simulating engine cycles consecutively. Figure 3 shows CCV results obtained using CPM compared to concurrently run cycles, which you can see match very well.3 This means that with sufficient computational resources, you can predict CCV in the amount of time it takes to run a single engine cycle.

Figure 3: CCV results from consecutively run simulations (left) versus concurrently run simulations (right) for the same gasoline direct injection engine case.

The study described above, and the vast majority of all CCV simulation studies, use large eddy simulations (LES), because LES allows you to resolve some of the turbulence scales that lead to cyclic variability. Reynolds-Averaged Navier-Stokes (RANS), on the other hand, provides an ensemble average that theoretically damps out variations between cycles. At least this was the consensus among the engine modeling community until Riccardo Scarcelli, a Research Scientist at Argonne, noticed something strange.

“I was running consecutive engine cycle simulations to move away from the initial boundary conditions, and I realized that the cycles were never converged to an average solution—the cycles were never like the cycle before or the cycle after,” Riccardo says. “And that was strange because I was using RANS, not LES.”

Argonne and Convergent Science worked together to untangle this mystery, and they discovered that RANS is able to capture the deterministic component of CCV. RANS has long been the predominant turbulence model used in engine simulations, so how had this phenomenon gone unnoticed? In the past, most engine simulations modeled conventional combustion, which shows little cyclic variability in practice in either diesel or gasoline engines. The more complex combustion regimes simulated today—along with the use of finer grids and more accurate numerics—allows RANS to pick up on some of the cycle-to-cycle variations that these engines exhibit in the real world. While RANS will not provide as accurate a picture as LES, it can be a useful tool to capture CCV trends. Additionally, RANS can be run on a much coarser mesh than LES, so you can get a faster turnaround on an inherently expensive problem, making CCV studies more practical for industry timelines.

Advancing Engine Technology

The gains in understanding and improved models developed during the Argonne and Convergent Science collaboration provide great benefit to the engine community. One of the primary missions of Argonne National Laboratory is to transfer knowledge and technology to industry. To that end, the models developed during the collaboration will continue to be implemented in CONVERGE, putting the technology in the hands of manufacturers, so they can create better engines. 

What can we look forward to in the future? There will continue to be a strong focus on developing high fidelity numerics, expanding and improving chemistry tools and mechanisms, integrating machine learning into the simulation process, and speeding up CFD simulations—establishing more efficient models and further increasing the scalability of CONVERGE to take advantage of the latest computational resources. Moreover, we can look forward to seeing the innovations of the last decade of collaboration incorporated into the engines of the next decade, bringing us closer to a clean transportation future.

In case you missed the other posts in the series, you can find them here:


[1] Torelli, R., Matusik, K.E., Nelli, K.C., Kastengren, A.L., Fezzaa, K., Powell, C.F., Som, S., Pei, Y., Tzanetakis, T., Zhang, Y., Traver, M., and Cleary, D.J., “Evaluation of Shot-to-Shot In-Nozzle Flow Variations in a Heavy-Duty Diesel Injector Using Real Nozzle Geometry,” SAE Paper 2018-01-0303, 2018. DOI: 10.4271/2018-01-0303

[2] Scarcelli, R., Zhang, A., Wallner, T., Som, S., Huang, J., Wijeyakulasuriya, S., Mao, Y., Zhu, X., and Lee, S.-Y., “Development of a Hybrid Lagrangian–Eulerian Model to Describe Spark-Ignition Processes at Engine-Like Turbulent Flow Conditions,” Journal of Engineering for Gas Turbines and Power, 141(9), 2019. DOI: 10.1115/1.4043397
[3] Probst, D., Wijeyakulasuriya, S., Pomraning, E., Kodavasal, J., Scarcelli, R., and Som, S., “Predicting Cycle-to-Cycle Variation With Concurrent Cycles In A Gasoline Direct Injected Engine With Large Eddy Simulations”, Journal of Energy Resources Technology, 142(4), 2020. DOI: 10.1115/1.4044766

► Exploring Offshore Wind Energy: Creating a Cleaner Future With CFD
  19 Oct, 2020

Renewable energy is being generated at unprecedented levels in the United States, and those levels will only continue to rise. The growth in renewable energy has been driven largely by wind power—over the last decade, wind energy generation in the U.S. has increased by 400% 1. It’s easy to see why wind power is appealing. It’s sustainable, cost-effective, and offers the opportunity for domestic energy production. But, like all energy sources, wind power doesn’t come without drawbacks. Concerns have been raised about land use, noise, consequences to wildlife habitats, and the aesthetic impact of wind turbines on the landscape 2.

However, there is a potential solution to many of these issues: what if you move wind turbines offshore? In addition to mitigating concerns over land use, noise, and visual impact, offshore wind turbines offer several other advantages. Compared to onshore, wind speeds offshore tend to be higher and steadier, leading to large gains in energy production. Also, in the U.S., a large portion of the population lives near the coasts or in the Great Lakes region, which minimizes problems associated with transporting wind-generated electricity. But despite these advantages, only 0.03% of the U.S. wind-generating capacity in 2018 came from offshore wind plants 1. So why hasn’t offshore wind energy become more prevalent? Well, one of the major challenges with offshore wind energy is a problem of engineering—wind turbine support structures must be designed to withstand the significant wind and wave loads offshore.

Today, there are computational tools that engineers can use to help design optimized support structures for offshore wind turbines. Namely, computational fluid dynamics (CFD) simulations can offer valuable insight into the interaction between waves and the wind turbine support structures. 

Two-phase CONVERGE simulation of a solitary wave breaking on a monopile. The water phase is shown, colored by horizontal velocity.

A CFD Case Study

Hannah Johlas, NSF Graduate Research Fellow

Hannah Johlas is an NSF Graduate Research Fellow in Dr. David Schmidt’s lab at the University of Massachusetts Amherst. Hannah uses CFD to study fixed-bottom offshore wind turbines at shallow-to-intermediate water depths (up to approximately 50 meters deep). Turbines located at these depths are of particular interest because of a phenomenon called breaking waves. As waves move from deeper to shallower water, the wavelength decreases and the wave height increases in a process called shoaling. If a wave becomes steep enough, the crest can overturn and topple forward, creating a breaking wave. Breaking waves can impart substantial forces onto turbine support structures, so if you’re planning to build a wind turbine in shallower water, it’s important to know if that turbine might experience breaking waves.

Hannah uses CONVERGE CFD software to predict if waves are likely to break for ocean characteristics common to potential offshore wind turbine sites along the east coast of the U.S. She also predicts the forces from breaking waves slamming into the wind turbine support structures. The results of the CONVERGE simulations are then used to evaluate the accuracy of simplified engineering models to determine which models best capture wave behavior and wave forces and, thus, which ones should be used when designing wind turbines.

CONVERGE Simulations

In this study, Hannah simulated 39 different wave trains in CONVERGE using a two-phase finite volume CFD model 3. She leveraged the volume of fluid (VOF) method with the Piecewise Linear Interface Calculation scheme to capture the air-water interface. Additionally, automated meshing and Adaptive Mesh Refinement ensured accurate results while minimizing the time to set up and run the simulations.

“CONVERGE’s adaptive meshing helps simulate fluid interfaces at reduced computational cost,” Hannah says. “This feature is particularly useful for resolving the complex air-water interface in breaking wave simulations.”

Some of the breaking waves were then simulated slamming into monopiles, the large cylinders used as support structures for offshore wind turbines in shallow water. The results of these CONVERGE simulations were validated against experimental data before being used to evaluate the simplified engineering models.

Experimental setup at Oregon State University (left) and the corresponding CONVERGE simulation (right) of a wave breaking on a monopile.


Four common models for predicting whether a wave will break (McCowan, Miche, Battjes, and Goda) were assessed. The models were evaluated by how frequently they produced false positives (i.e., the model predicts a wave should break, but the simulated wave does not break) and false negatives (i.e., the model predicts a wave should not break, but the simulated wave does break) and how well they predicted the steepness of the breaking waves. False positives are preferable to false negatives when designing a conservative support structure, since breaking wave loads are usually higher than non-breaking waves.

The study results indicate that none of the models perform well under all conditions, and instead which model you should use depends on the characteristics of the ocean at the site you’re considering.

“For sites with low seafloor slopes, the Goda model is the best at conservatively predicting whether a given wave will break,” Hannah says. “For higher seafloor slopes, the Battjes model is preferred.”

Four slam force models were also evaluated: Goda, Campbell-Weynberg, Cointe-Armand, and Wienke-Oumerachi. The slam models and the simulated CFD wave forces were compared for their peak total force, their force time history, and breaking wave shape. 

The results show that all four slam models are conservative (i.e., predict higher peak forces than the simulated waves) and assume the worst-case shape for the breaking wave during impact. The Goda slam model is the least conservative, while the Cointe-Armand and Wienke-Oumerachi slam models are the most conservative. All four models neglect the effects of runup on the monopiles, which was present in the CFD simulations. This could explain some of the discrepancies between the forces predicted by the engineering models and the CFD simulations.


Offshore wind energy is a promising technology for clean energy production, but to gain traction in the industry, there needs to be sound engineering models to use when designing the turbines. Hannah’s research provides guidelines on which engineering models should be used for a given set of ocean characteristics. Her results also highlight the areas that could be improved upon. 

“The slam force models don’t account for variety in wave shape at impact or for wave runup on the monopiles,” Hannah says. “Future studies should focus on incorporating these factors into the engineering models to improve their predictive capabilities.”

CONVERGE for Renewable Energy

CFD has a fundamental role to play in the development of renewable energy. CONVERGE’s combination of autonomous meshing, high-fidelity physical models, and ability to easily handle complex, moving geometries make it particularly well suited to the task. Whether it’s studying the interaction of waves with offshore turbines, optimizing the design of onshore wind farms, or predicting wind loads on solar panels, CONVERGE has the tools you need to help bring about the next generation of energy production.

Interested in learning more about Hannah’s research? Check out her paper here.


[1] Marcy, C., “U.S. renewable electricity generation has doubled since 2008,”, accessed on Nov 11, 2016.

[2] Center for Sustainable Systems, University of Michigan, “U.S. Renewable Energy Factsheet”,, accessed on Nov 11, 2016.

[3] Johlas, H.M., Hallowell, S., Xie, S., Lomonaco, P., Lackner, M.A., Arwade, S.A., Myers, A.T., and Schmidt, D.P., “Modeling Breaking Waves for Fixed-Bottom Support Structures for Offshore Wind Turbines,” ASME 2018 1st International Offshore Wind Technical Conference, IOWTC2018-1095, San Francisco, CA, United States, Nov 4–7, 2018. DOI: 10.1115/IOWTC2018-1095

► CONVERGE for Pumps & Compressors: The Engineering Solution for Design Optimization
  12 Oct, 2020

Across industries, manufacturers share many of the same goals: create quality products, boost productivity, and reduce expenses. In the pumps and compressors business, manufacturers must contend with the complexity of the machines themselves in order to reach these goals. Given the intricate geometries, moving components, and tight clearances between parts, designing pumps and compressors to be efficient and reliable is no trivial matter. 

First, assessing the device’s performance by building and testing a prototype can be time-consuming and costly. And when you’re performing a design study, machining and switching out various components further compounds your expenses. There are also limitations in how many instruments you can place inside the device and where you can place them, which can make fully characterizing the machine difficult. New methods for testing and manufacturing can help streamline this process, but there remains room for alternative approaches.

Centrifugal pump

Computational fluid dynamics (CFD) offers significant advantages for designing pumps and compressors. Through CFD simulations, you can obtain valuable insight into the behavior of the fluid inside your machine and the interactions between the fluid and solid components—and CONVERGE CFD software is well suited for the task.

Designed to model three-dimensional fluid flows in systems with complex geometries and moving boundaries, CONVERGE is equipped to simulate any positive displacement or dynamic pump or compressor. And with a suite of advanced models, CONVERGE allows you to computationally study the physical phenomena that affect efficiency and reliability—such as surge, pressure pulsations, cavitation, and vibration—to design an optimal machine.

The Value of CONVERGE

CFD provides a unique opportunity to visualize the inner workings of your machine during operation, generating data on pressures, temperatures, velocities, and fluid properties without the limitations of physical measurements. The entire flow field can be analyzed with CFD, including areas that are difficult or impossible to measure experimentally. This additional data allows you to comprehensively characterize your pump or compressor and pinpoint areas for improvement.

Since CONVERGE leads the way in predictive CFD technology, you can analyze pump and compressor designs that have not yet been built and still be confident in your results. Compared to building and testing prototypes, simulations are fast and inexpensive, and altering a computer-modeled geometry is trivial. Iterating through designs virtually and building only the most promising candidates reduces the expenses associated with the design process. 

While three-dimensional CFD is fast compared to experimental methods, it is typically slower than one- or two-dimensional analysis tools, which are often incorporated into the design process. However, 1D and 2D methods are inherently limited in their ability to capture the 3D nature of physical flows, and thus can miss important flow phenomena that may negatively affect performance. 

CONVERGE drastically reduces the time required to set up a 3D pump or compressor simulation with its autonomous meshing capabilities. Creating a mesh by hand—which is standard practice in many CFD programs—can be a weeks-long process, particularly for cases with complex moving geometries such as pumps and compressors. With autonomous meshing, CONVERGE automatically generates an optimized Cartesian mesh based on a few simple user-defined parameters, effectively eliminating all user meshing time. 

In addition, the increased computational resources available today can greatly reduce the time requirements to run CFD simulations. CONVERGE is specifically designed to enable highly parallel simulations to run on many processors and demonstrates excellent scaling on thousands of cores. Additionally, Convergent Science partners with cloud service providers, who offer affordable on-demand access to the latest computing resources, making it simple to speed up your simulations.

Validation Cases

Accurately capturing real-world physical phenomena is critical to obtaining useful simulation results. CONVERGE features robust fluid-structure interaction (FSI) modeling capabilities. For example, you can simulate the interaction between the bulk flow and the valves to predict impact velocity, fatigue, and failure points. CONVERGE also features a conjugate heat transfer (CHT) model to resolve spatially varying surface temperature distributions, and a multi-phase model to study cavitation, oil splashing, and other free surface flows of interest. 

CONVERGE has been validated on numerous types of compressors and pumps1-10, and we will discuss two common applications below. 

Scroll Compressor

Scroll compressors are often used in air conditioning systems, and the major design goals for these machines today are reducing noise and improving efficiency. Scroll compressors consist of a stationary scroll and an orbiting scroll, which create a complex system that can be challenging to model. Some codes use a moving mesh to simulate moving boundaries, but this can introduce diffusive error that lowers the accuracy of your results. CONVERGE automatically generates a stationary mesh at each time-step to accommodate moving boundaries, which provides high numerical accuracy. In addition, CONVERGE employs a unique Cartesian cut-cell approach to perfectly represent your compressor geometry, no matter how complex. 

In this study1, CONVERGE was used to simulate a scroll compressor with a deforming reed valve. An FSI model was used to capture the motion of the discharge reed valve. Figure 1 shows the CFD-predicted mass flow rate through the scroll compressor compared to experimental values. As you can see, there is good agreement between the simulation and experiment. 

This method is particularly useful for the optimization phase of design, as parametric changes to the geometry can be easily incorporated. In addition, Adaptive Mesh Refinement (AMR) allows you to accurately capture the physical phenomena of interest while maintaining a reasonable computational expense.

Figure 1: Top: Representative cut-plane of a scroll compressor simulation with the mesh overlaid, colored by velocity. Bottom: Experimental (black square and triangles) and CONVERGE simulation (pink circles) results1 for mass flow rate.

Screw Compressor

Next, we will look at a twin screw compressor. These compressors have two helical screws that rotate in opposite directions, and are frequently used in industrial, manufacturing, and refrigeration applications. A common challenge for designing screw compressors—and many other pumps and compressors—is the tight clearances between parts. Inevitably, there will be some leakage flow between chambers, which will affect the device’s performance.

CONVERGE offers several methods for capturing the fluid behavior in these small gaps. Using local mesh embedding and AMR, you can directly resolve the gaps. This method is highly accurate, but it can come with a high computational price tag. An alternative approach is to use one of CONVERGE’s gap models to account for the leakage flows without fully resolving the gaps. This method balances accuracy and time costs, so you can get the results you need when you need them.

Another factor that must be taken into account when designing screw compressors is thermal expansion. Heat transfer between the fluid and the solid walls means the clearances will vary down the length of the rotors. CONVERGE’s CHT model can capture the heat transfer between the solid and the fluid to account for this phenomenon.

This study2 of a dry twin screw compressor employs a gap model to account for leakage flows, CHT modeling to capture heat transfer, and AMR to resolve large-scale flow structures. Mass flow rate, power, and discharge temperature were predicted with CONVERGE and compared to experimentally measured values. This study also investigated the effects of the base grid size on the accuracy of the results. In Figure 2, you can see there is good agreement between the experimental and simulated data, particularly for the most refined grid. The method used in this study provides accurate results in a turn-around time that is practical for engineering applications.

Figure 2: Top: Representative cut-plane of a dry twin screw compressor simulation with the mesh overlaid (colored by velocity). Bottom: Mass flow rate, power, and discharge temperature results2 of the experiment (black squares) and the CONVERGE simulations (colored circles).


The benefits CONVERGE offers for designing pumps and compressors directly translate to a tangible competitive advantage. CFD benefits your business by reducing costs and enabling you to bring your product to market faster, and CONVERGE features tools to help you optimize your designs and produce high-quality products for your customers. To find out how CONVERGE can benefit you, contact us today!


[1] Rowinski, D., Pham, H.-D., and Brandt, T., “Modeling a Scroll Compressor Using a Cartesian Cut-Cell Based CFD Methodology with Automatic Adaptive Meshing,” 24th International Compressor Engineering Conference at Purdue, 1252, West Lafayette, IN, United States, Jul 9–12, 2018.

[2] Rowinski, D., Li, Y., and Bansal, K., “Investigations of Automatic Meshing in Modeling a Dry Twin Screw Compressor,” 24th International Compressor Engineering Conference at Purdue, 1528, West Lafayette, IN, United States, Jul 9–12, 2018.

[3] Rowinski, D., Sadique, J., Oliveira, S., and Real, M., “Modeling a Reciprocating Compressor Using a Two-Way Coupled Fluid and Solid Solver with Automatic Grid Generation and Adaptive Mesh Refinement,” 24th International Compressor Engineering Conference at Purdue, 1587, West Lafayette, IN, United States, Jul 9–12, 2018.

[4] Rowinski, D.H., Nikolov, A., and Brümmer, A., “Modeling a Dry Running Twin-Screw Expander using a Coupled Thermal-Fluid Solver with Automatic Mesh Generation,” 10th International Conference on Screw Machines, Dortmund, Germany, Sep 18–19, 2018.

[5] da Silva, L.R., Dutra, T., Deschamps, C.J., and Rodrigues, T.T., “A New Modeling Strategy to Simulation the Compression Cycle of Reciprocating Compressors,” IIR Conference on Compressors, 0226, Bratislava, Slovakia, Sep 6–8, 2017. DOI: 10.18462/iir.compr.2017.0226

[6] Willie, J., “Analytical and Numerical Prediction of the Flow and Performance in a Claw Vacuum Pump,” 10th International Conference on Screw Machines, Dortmund, Germany, Sep 18–19, 2018. DOI: 10.1088/1757-899X/425/1/012026

[7] Jhun, C., Siedlecki, C., Xu, L., Lukic, B., Newswanger, R., Yeager, E., Reibson, J., Cysyk, J., Weiss, W., and Rosenberg, G., “Stress and Exposure Time on Von Willebrand Factor Degradation,” Artificial Organs, 2018. DOI: 10.1111/aor.13323

[8] Rowinski, D.H., “New Applications in Multi-Phase Flow Modeling With CONVERGE: Gerotor Pumps, Fuel Tank Sloshing, and Gear Churning,” 2018 CONVERGE User Conference–Europe, Bologna, Italy, Mar 19–23, 2018.

[9] Willie, J., “Simulation and Optimization of Flow Inside Claw Vacuum Pumps,” 2018 CONVERGE User Conference–Europe, Bologna, Italy, Mar 19–23, 2018.

[10] Scheib, C.M., Newswanger, R.K., Cysyk, J.P., Reibson, J.D., Lukic, B., Doxtater, B., Yeager, E., Leibich, P., Bletcher, K., Siedlecki, C.A., Weiss, W.J., Rosenberg, G., and Jhun, C., “LVAD Redesign: Pump Variation for Minimizing Thrombus Susceptibility Potential,” ASAIO 65th Annual Conference, San Francisco, CA, United States, Jun 26–29, 2019.

► Leveling Up Scaling with CONVERGE 3.0
  14 Aug, 2020

In a competitive market, predictive computational fluid dynamics (CFD) can give you an edge when it comes to product design and development. Not only can you predict problem areas in your product before manufacturing, but you can also optimize your design computationally and devote fewer resources to testing physical models. To get accurate predictions in CFD, you need to have high-resolution grid-convergent meshes, detailed physical models, high-order numerics, and robust chemistry—all of which are computationally expensive. Using simulation to expedite product design works only if you can run your simulations in a reasonable amount of time.

The introduction of high-performance computing (HPC) drastically furthered our ability to obtain accurate results in shorter periods of time. By running simulations in parallel on multiple cores, we can now solve cases with millions of cells and complicated physics that otherwise would have taken a prohibitively long time to complete. 

However, simply running cases on more cores doesn’t necessarily lead to a significant speedup. The speedup from HPC is only as good as your code’s parallelization algorithm. Hence, to get a faster turnaround on product development, we need to improve our parallelization algorithm.

Let’s Start With the Basics

Breaking a problem into parts and solving these parts simultaneously on multiple interlinked processors is known as parallelization. An ideally parallelized problem will scale inversely with the number of cores—twice the number of cores, half the runtime.

A common task in HPC is measuring the scalability, also referred to as scaling efficiency, of an application. Scalability is the study of how the simulation runtime is affected by changing the number of cores or processors. The scaling trend can be visualized by plotting the speedup against the number of cores.

How Does CONVERGE Parallelize?

Parallelization in CONVERGE 2.4 and Earlier

In CONVERGE versions 2.4 and earlier, parallelization is performed by partitioning the solution domain into parallel blocks, which are coarser than the base grid. CONVERGE distributes the blocks to the interlinked processors and then performs a load balance. Load balancing redistributes these parallel blocks such that each processor is assigned roughly the same number of cells.

This parallel-block technique works well unless a simulation contains high levels of embedding (regions in which the base grid is refined to a finer mesh) in the calculation domain. These cases lead to poor parallelization because the cells of a single parallel block cannot be split between multiple processors.

Figure 1 shows an example of parallel block load balancing for a test case in CONVERGE 2.4. The colors of the contour represent the cells owned by each processor. As you can see, the highly embedded region at the center is covered by only a few blocks, leading to a disproportionately high number of cells in those blocks. As a result, the cell distribution across processors is skewed. This phenomenon imposes a practical limit on the number of levels of embedding you can have in earlier versions of CONVERGE while still maintaining a reasonable load balance.

Figure 1: Parallel-block load balancing in CONVERGE 2.4.

Parallelization in CONVERGE 3.0

In CONVERGE 3.0, instead of generating parallel blocks, parallelization is accomplished via cell-based load balancing, i.e., on a cell-by-cell basis. Because each cell can belong to any processor, there is much more flexibility in how the cells are distributed, and we no longer need to worry about our embedding levels.

Figure 2 shows the cell distribution among processors using cell-based load balancing in CONVERGE 3.0 for the same test case shown in Figure 1. You can see that without the restrictions of the parallel blocks, the cells in the highly embedded region are divided between many processors, ensuring an (approximately) equal distribution of cells.

Figure 2: Cell-based load balancing in CONVERGE 3.0.

The cell-based load balancing technique demonstrates significant improvements in scaling, even for large numbers of cores. And unlike previous versions, the load balancing itself in CONVERGE 3.0 is performed in parallel, accelerating the simulation start-up.

Case Studies

In order to see how well the cell-based parallelization works, we have performed strong scaling studies for a number of cases. The term strong scaling means that we ran the exact same simulation (i.e., we kept the number of cells, setup parameters, etc. constant) on different core counts.

SI8 PFI Engine Case

Figure 3 shows scaling results for a typical SI8 port fuel injection (PFI) engine case in CONVERGE 3.0. The case was run for one full engine cycle, and the core count varied from 56 to 448. The plot compares the speedup obtained running the case in CONVERGE 3.0 with the ideal speedup. With enough CPU resources, in this case 448 cores, you can simulate one engine cycle with detailed chemistry in under two hours—which is three times faster than CONVERGE 2.4!

Cores Time (h) Speedup Efficiency Cells per core Engine cycles per day
56 11.51 1 100% 12,500 2.1
112 5.75 2 100% 6,200 4.2
224 3.08 3.74 93% 3,100 7.8
448 1.91 6.67 75% 1,600 12.5
Figure 3: CONVERGE 3.0 scaling results for an SI8 PFI engine simulation run on an in-house cluster. On 448 cores, CONVERGE 3.0 scales with 75% efficiency, and you can simulate more than 12 engine cycles in a single day. Please note that the parallelization profiles will differ from one case to another.

Sandia Flame D Case

If the speedup of the SI8 PFI engine simulation impressed you, then just wait until you see the scaling study for the Sandia Flame D case! Figure 4 shows the results of a strong scaling study performed for the Sandia Flame D case, in which we simulated a methane flame jet using 170 million cells. The case was run on the Blue Waters supercomputer at the National Center for Supercomputing Applications (NCSA), and the core counts vary from 500 to 8,000. CONVERGE 3.0 demonstrates impressive near-linear scaling even on thousands of cores.

Figure 4: CONVERGE 3.0 scaling results for a combusting turbulent partially premixed flame (Sandia Flame D) case run on the Blue Waters supercomputer at the National Center for Supercomputing Applications[1]. On 8,000 cores, CONVERGE 3.0 scales with 95% efficiency.


Although earlier versions of CONVERGE show good runtime improvements with increasing core counts, speedup is limited for cases with significant local embeddings. CONVERGE 3.0 has been specifically developed to run efficiently on modern hardware configurations that have a high number of cores per node.

With CONVERGE 3.0, we have observed an increase in speedup in simulations with as few as approximately 1,500 cells per core. With its improved scaling efficiency, this new version empowers you to obtain simulation results quickly, even for massive cases, so you can reduce the time it takes to bring your product to market. 

Contact us to learn how you can accelerate your simulations with CONVERGE 3.0.

[1] The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students, and collaborators from around the globe use advanced digital resources to address research grand challenges for the benefit of science and society. The NCSA Industry Program is the largest Industrial HPC outreach in the world, and it has been advancing one third of the Fortune 50® for more than 30 years by bringing industry, researchers, and students together to solve grand computational problems at rapid speed and scale. The CONVERGE simulations were run on NCSA’s Blue Waters supercomputer, which is one of the fastest supercomputers on a university campus. Blue Waters is supported by the National Science Foundation through awards ACI-0725070 and ACI-1238993.

Numerical Simulations using FLOW-3D top

► Marketing Assistant
    4 Jan, 2021

We are looking for a detailed-oriented, highly-organized, self-starter with excellent writing and communication skills to join the marketing department at Flow Science as a Marketing Assistant. In this role, you will assist with a variety of marketing functions, including tradeshows, social media, websites, and digital marketing. If you are driven to succeed, desire to learn new skills, and want to be part of a small, dynamic, highly technical team, then we encourage you to apply for this position. 

This position will start remotely, but the candidate should plan to work onsite later in the year.

Education and experience

A minimum of an Associate’s degree in marketing, communications, liberal arts or related is required. 1+ years of work experience in marketing or business preferred.

Desirable skills

The successful candidate will be proficient with MS Office Suite and, preferably, have some exposure to Adobe suite, web languages, and social media marketing.

Submit your cover letter and resume, plus a writing sample to

About Flow Science, Inc.

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


Flow Science offers an exceptional benefits package to full-time employees including medical, dental, vision insurances, life and disability insurances, 401(k) and profit-sharing plans with generous employer matching, and an incentive compensation plan that offers a year-end bonus opportunity up to 30% of base salary.

► CFD Engineer
  15 Dec, 2020

Flow Science is not offering H1B sponsorship for this position.

Come work in one of the best small cities in the US1 for one of the best companies in New Mexico2! Flow Science is growing tech company with deep roots looking for outstanding engineers with an interest or expertise in the aerospace, automotive, additive manufacturing, and consumer products industries.

Principal responsibilities and key requirements

CFD engineers work at the intersection of classical physics, numerical methods, and computer science. We apply our expertise in these fields to help clients solve complex real-world engineering problems, teach applied CFD, and guide our development teams to create new models that grow our capabilities and application areas. This challenging and dynamic role requires the following skills to be successful:

  • An engineering degree from an ABET or equivalently accredited university and some work experience
    • MS degree (mechanical, aerospace, or chemical engineering preferred) and engineering internship experience OR
    • BS degree (mechanical, aerospace, or chemical engineering preferred) and 2+ years of engineering work experience
  • Strong understanding of engineering fundamentals, particularly fluid mechanics, heat transfer, and solid mechanics
  • Excellent oral communication, technical writing, and interpersonal skills
  • Ability to comfortably navigate a diverse, multicultural environment
  • Excellent organizational skills
  • Common sense and an unending desire to learn

Preferred skills and experience

Exceptional CFD engineers usually draw heavily on the following skills and experience:

  • 2+ years of relevant industry or academic experience (e.g., additive manufacturing, propellant management design, slosh analysis, consumer product processing, coating, analysis of complex fluids, etc.)
  • Experience with CFD, FEA, or other numerical analysis
  • Experience with experimental setups and data analysis
  • Experience with 3D CAD
  • Programming experience (FORTRAN and Python)
  • Demonstrated initiative in work projects
  • EIT certification


Flow Science offers an exceptional benefits package to full-time employees including medical, dental, vision insurances, life and disability insurances, 401(k) and profit-sharing plans with generous employer matching, and an incentive compensation plan that offers a year-end bonus opportunity up to 30% of base salary.


Still interested? Submit your resume and a cover letter to Paper copies may be submitted via mail (Attention: Human Resources, 683 Harkle Road, Santa Fe, NM 87505) or fax (505-982-5551). 

1 HuffPost listed Santa Fe, NM as one of the top 5 small cities in the US

2 Flow Science has been named one of the Best Places to Work in New Mexico by Albuquerque Business First.

► Siphon Spillways
    1 Dec, 2020
FLOW-3D HYDRO Case Studies

Siphon Spillways

This article was contributed by Ali Habibzadeh (Project Engineer) and Jose (Pepe) Vasquez (Principal Engineer) at Northwest Hydraulic Consultants.

CFD modeling is a powerful tool for evaluating the hydraulic design of spillway structures. The capacity of the spillway at design flow is of paramount significance in terms of dam safety (USBR 1987). Northwest Hydraulic Consultants has applied CFD modeling in numerous case studies of existing or new spillway designs. The following article demonstrates a sample case study conducted on an existing siphon spillway.

Air-regulated siphon spillways operate under different hydraulic conditions depending on the upstream water level (McBirney 1957). For relatively small heads over the crest of the spillway, the siphon operates like a free weir with atmospheric pressure inside the siphon barrel (i.e. discharge will be proportional to hw3/2). As the head increases, flow within the siphon barrel transitions to pressurized flow; the siphon barrel will be primed with sub-atmospheric pressure. At that stage, discharge through the siphon barrel is like that of an orifice (i.e. discharge will be proportional to ho1/2). The driving head through a primed siphon is equal to the differential head between the upstream water level and the water level just downstream of the siphon exit. Because the effective head on a primed siphon (ho) is, in general, significantly larger than the head over a free siphon (hw), the primed siphon can convey a significantly larger amount of flow when compared to a free siphon with a slight increase in the upstream water level (Ervine 1976). However, this is only true if the siphon actually primes (Tadayon and Ramamurthy, 2013). During floods and emergency events, it is extremely important that a siphon self-primes without human intervention, but this is not always what occurs.

To enhance priming, deflectors are often installed along the floor of siphons to generate a jet directed towards the opposite wall to enclose a confined volume of air. The increased turbulence generated by the jet gradually removes the confined air, dropping the pressure within the barrel.

As the upstream water level drops, the prime within the siphon breaks and flow switches back to atmospheric pressure. As this transition occurs, the discharge decreases significantly, with the head-discharge relation switching from an orifice to a weir.

Siphon spillways schematic
Schematic section view of siphon spillway; weir flow (left) and orifice flow (right).

FLOW-3D Modeling of a Siphon Spillway

Northwest Hydraulic Consultants used FLOW-3D to evaluate the discharge capacity of an existing 3-ft high rectangular siphon spillway. Since the existing siphon experienced issues with self-priming during floods, a hooded air vent at the entrance and a floor deflector within the barrel were added. The first animation below, shows a section model of the siphon with increasing upstream water level.

The first animation was conducted with a fixed upstream water level that was determined from field observation at the existing spillway. The deflection of the flow by the floor deflector results in a confined volume of air within the barrel. Over time, this air is entrained by the flow resulting in the absolute pressure within the barrel to drop from atmospheric (~2,115 lb/ft2) to sub-atmospheric (around 1,500 lb/ft2). As the pressure drops within the barrel, air is removed and replaced by water. The discharge through the siphon increases from less than 1 cfs to more the 16 cfs when the siphon is primed, and the barrel runs full.

The second animation illustrates the siphon prime break action as the upstream water surface elevation decreases. The process of siphon prime break occurs when the difference between the atmospheric ambient pressure and pressure at the crown of the siphon barrel exceeds the differential head required to entrain air into the siphon. Hence, the siphon prime breaks and air replaces fluid within the barrel. As shown in this animation, after the siphon prime break action is complete, inside pressure and discharge through the barrel return to their original weir flow values.

The results of FLOW-3D were confirmed by a physical model study conducted at Northwest Hydraulic Consultants’ hydraulics lab.  


Ervine, D. A., (1976). “The Design and Modelling of Air-Regulated Siphon Spillways.”, Proceedings of the Institution of Civil Engineers, Vol. 61, pp. 383-400.

McBirney, W. B. (1957). “Some Experiments with Emergency Siphon Spillways.”, US Bureau of Reclamation, PAP-97.

Tadayon, R. and Ramamurthy, A. S., (2013). “Discharge Coefficient for Siphon Spillways.”, ASCE Journal of Irrigation and Drainage Engineering, Vol. 139, No. 3, pp. 267-270.

USBR. (1987). “Design of Small Dams.”, 3rd Ed., U.S. Government Printing Office, Washington, DC.

► The Solid Proof: The Latest in Solidification Modeling
  16 Nov, 2020

One of the most exciting new developments offered with the release of FLOW-3D CAST v5.1 is the new chemistry-based aluminum-silicon and aluminum-copper alloy solidification model. This new model allows users to predict the microstructure and mechanical properties for as-cast and heat-treated castings. Experimental data was used to verify and validate our model predictions, which is detailed in Modeling and Simulation of Microstructures and Mechanical Properties of AlSi- and AlCi-based Alloys, a publication that recently won the Best Paper Award from the 2020 American Foundry Society Aluminum & Light Metals Division.

Test bars - FLOW-3D CAST Solidification Model

The paper highlights a casting study done in collaboration with The University of Alabama at Birmingham, in which A356 and A206 commercial ingots were used to create a wedge-shaped pattern using the lost foam method. For more detail about the study, check out our recent webinar on the new solidification model.

What does the new model do exactly?

FLOW-3D CAST’s new solidification model accurately predicts grain size and secondary dendritic arm spacing (SDAS) by tracking the evolution of the alloy’s chemical elements and reactions. Then empirical relationships are used to calculate then resulting microstructure to mechanical properties. This calculation also allows us to output a non-dimensional Niyama criterion for improved porosity prediction.

Here we highlight some of our results from the aluminum silicon A356 samples. The data is very compelling!

First and foremost, accurate cooling curves are foundational to the study of microstructure. The first step was to establish that our model correctly matched thermocouple data extracted from experiments.

With this solid foundation, and with the detailed knowledge of the alloy composition, an accurate prediction of the secondary dentric arm spacing then leads to an accurate prediction of mechanical properties.

Accurate input data and a solid handle on pouring and cooling parameters are always the necessary foundation that can help us obtain accurate predictions of microstructure, porosity and mechanical properties.

Alloy composition
Cooling curve FLOW-3D CAST

Verification and Validation

We see an excellent correlation between the experimental  data and the outputs of the solidification model, as shown in the following plots for SDAS, ultimate tensile strength, and elongation.                        

Here at Flow Science we deliver innovative developments that help our customers conceptualize, create, and analyze their casting designs with confidence. If you would like more information on the new solidification model or a personal demonstration of FLOW-3D CAST v5.1, please reach out to

Thank you and stay tuned for our next post!

Ajit D'Brass

Ajit D'Brass

Metal Casting Engineer at Flow Science

► Simulation of Joule Heating-based Core Drying
    4 Nov, 2020
FLOW-3D CAST case studies

Simulation of Joule heating-based Core Drying

This article was contributed by Eric Riedel 1,2

1Otto-von-Guericke-University Magdeburg, Institute of Manufacturing Technology and Quality Management, Germany

2Soplain GmbH, Germany

Modern casting production requires the use of sand cores. Growing environmental awareness as well as tougher regulations have supported the development of inorganic, emission-free binder systems, in which the cores are dried and cured by heat. In what is known as the hot box process, heat is generated in the core boxes and transferred to the sand binder mixture. However, the hot box process exhibits two major technological disadvantages.

The first disadvantage is the very low thermal conductivity of quartz sand of about 1 W/(m·K). Due to outside-in heat transfer, the process is time-consuming, can lead to shell formation and thus quality issues. For this reason, very high core-box temperatures of up to 523.15 K or more are applied to accelerate the heat transfer. The second disadvantage of the hot box process is that the core drying itself cannot be directly measured and digitized in real time. Instead, it can only be measured passively by recording peripheral parameters, such as from the core box.

The ACS Process

The new, patented Advanced Core Solution (ACS) process aims for time- and energy-efficient core drying and curing. The ACS process uses a property common to all inorganic binder systems: because they are water-based, they are electrically conductive. The key factor is the development of electrically conductive core box materials, whose conductivity can be adjusted to that of the sand-binder mixture. When a voltage is applied, the electrical current flows uniformly through the core box and sand-binder mixture, as demonstrated in Figure 1. Put more precisely, current flows through the electrically conductive binder bridges between the sand grains. Due to its inherent electrical resistance, the sand core heats uniformly without shell formation. The scientific principle behind it, called Joule heating, is based on Joule’s first law. In the series process, the electrically conductive core box heats up through Joule heating as well, additionally accelerating the drying process. This is a further important advantage, since for the ACS process, no complicated heating devices within the core boxes are required anymore, thus simplifying core box construction.

With this new process, and for the first time, heat is generated directly where it is needed: within the core. Since the necessary heat is generated through the homogeneously-distributed binder and transferred to the adjoining sand, the low thermal conductivity of the quartz sand is no longer a limiting process factor. Additionally, for the first time, the recording of drying-specific electrical parameters allows for comprehensive real-time monitoring of the drying process itself. Using FLOW-3D, the ACS process can be simulated, fulfilling an important criterion for industrial application, including the quantification of process benefits.

Sand core joule heating setup
Figure 1: Basic comparison of the current flows: a) without, b) with adjustment of the electrical conductivity of the core box to that of the sand-binder mixture.

Model Description

The modeling is based on the work of Starobin et al. [1], but extends it with the Electro-mechanics model in FLOW-3D. Activating the electric potential (iepot = 1), takes electro-thermal effects, i.e., Joule heating (iethermo = 1), into account. Model details can be taken from [2]. Via the electrical properties of the components, the core box is assigned a dynamic potential (ioepotm = 1) with an electrical conductivity (oecond) and, if necessary, a dielectric potential (odiel); the same applies to the sand core in order to account for electrical conductivity of the entire sand-binder mixture. The electrodes are assigned a fixed potential (ioepotm = 0), an electrical conductivity, and a negative electric potential (oepot) for one electrode and a positive electric potential for the other. Since a temperature-dependent definition of the electrical conductivity is not yet possible, we worked with restart simulations and active simulation control. This way, the average electrical conductivities of the respective temperature ranges could be considered, i.e., 293.15 to 303.15 K, 303.15 to 313.15 K, and so on. The following investigations focus on one-fluid simulation, i.e., purging was not considered.


In the first step, a commercially available inorganic sand-binder mixture was used for the experimental investigation and validation of the simulation model to investigate heating and temperature-dependent electrical conductivity. The time required to reach 373.15 K as well as the power and energy input into the sand core were measured. Based on the experimental analysis and results, a basic simulation model was created. For reasons of discretion, some of the underlying results are presented only qualitatively. The results are demonstrated in Figure 2, showing high accordance between the measured values and the simulation.

Experimental vs. simulation results core drying
Figure 2: Comparison of experimental and simulation results. The measuring points mark the reaching of the specified target temperatures in steps of ten, starting at 293.15 K: a) temperature-averaged power input- average deviation from measured values: 0,95 %, b) energy input - average deviation from measured values: 4.8 %.

Based on the validated results, the ACS process and simulation are shown using a simple but high-volume geometry, which illustrates the fundamentals and high potential of the advanced ACS development compared to the classic hot box process. The geometric alignment can be taken from Figure 3. Three cases were simulated: (1) a classic hot box process; (2) an ACS cold start process with cold tool (293.15 K); and (3) an ACS series process accounting for the tool heating due to the Joule effect. All three-dimensional models were discretized with a cell size of 1 mm. Table 1 sums up the most important details of the calculated scenarios.

Favorizing core heating drying
Figure 3: Geometric alignment of simulation setup for conductive core heating and drying.
Core box properties table
Table 1: Overview of calculated core drying cases. Values are derived from real experiments.

Results and Discusssion

Figure 4 shows the temperature and moisture development for the classic hot box process, clearly showing the outside-in heat transfer and corresponding moisture reduction. The simulation was carried out for 120 s with moisture still present in the sand core center at the end of the simulation; in practice, cycle time targets force an early termination of the drying process with shell formation and residual moisture in the core center. However, the ACS cold start simulation (corresponding to the first shot when the core shooting machine is started up), which is shown in Figure 5, shows the basic principle of the new process: the uniform heating of the core leads to an inside-out moisture transport. Furthermore, the sand core heats up faster than the core box. In the series process, the core box also reaches temperatures greater than 373.15 K through Joule heating, resulting in a mixture of hot box and ACS processes which further accelerates the drying process. The results of the ACS series simulation are summarized in Figure 6. While the sand core is not fully cured even after 120 seconds in the hot box process, the ACS process allows the core to dry completely after 72 or 45 seconds. Despite the significantly lower core box temperature, the new process shows a significant acceleration in core drying and the great potential of the new approach. One major advantage is a massive reduction in cycle times, including the associated energy requirements and the corresponding CO2 emissions. The energy introduced into the sand core can be measured during the real process as well as predicted in advance using simulation, which is another great advantage in terms of process design and transparency. Additionally, the simulation clearly illustrates the geometry-independent homogeneous heating of the test specimen, which means that moisture is not trapped in the core center and shell formation is avoided. All in all, the new process enables a significant increase in efficiency of the process and the quality of the inorganically bound sand cores as well. The process diagrams of all three cases are summarized in Figure 7.

Summary and Outlook

The demonstrated modelling shows the capability of FLOW-3D to simulate the new core drying process accurately as well as the potential of the new process for more efficient core drying and curing compared to the conventional hot box process. Even if the new simulation setup is still in the development stage and needs more real-case experiments, it still allows for great insights in the drying behavior, with very good agreement with experimental measurements so far.

Presently, within the simulation, the electrical conductivity of the sand-binder mixture is generated via the quartz sand, which in reality is not electrically conductive but corresponds to the electrical conductivity of the real-measured sand-binder mixtures. This way, the electrical conductivity of the entire sand-binder mixture is accounted for in the simulation and seems to fit the experimental results. For more precise simulations, the possibility of saving a temperature-dependent electrical conductivity of the solid core (i.e. the sand-binder mixture) would be helpful in order to take the actual conductivity curve into account. Further steps will concentrate on two-fluid simulation models. Initial trials show the basic feasibility with good results.

Despite the steps still to be taken, it can be said that the ability to simulate the ACS process with FLOW-3D marks an important milestone in the holistic establishment of a Joule heating-based core drying process and shows the benefits of this process for inorganic sand core manufacturing.


  • Starobin, C.W. Hirt, H. Lang, M. Todte, Core Drying Simulation and Validation, AFS Proceedings, Schaumburg, IL USA, 2011
  • FLOW-3D from Flow Science, Inc., Santa Fe, NM, USA
► Flow Science Receives the 2020 Flying 40
    4 Nov, 2020

Flow Science named one of the fastest growing technology companies in New Mexico for the fifth year running.

Santa Fe, NM, November 4, 2020 – Flow Science has been named one of New Mexico Technology’s Flying 40 recipients for the last five consecutive years. The New Mexico Technology Flying 40 awards recognize the 40 fastest growing technology companies in New Mexico each year.

It is an honor to be recognized for the fifth year in a row by the Flying 40. As Flow Science continues to grow and expand its operation in New Mexico, we strive to appear on this list for years to come, said Flow Science President & CEO, Dr. Amir Isfahani.

These awards are given out by the Flying 40 program based on three revenue categories: the top revenue growth companies with revenues between $1 million and $10 million, the top revenue growth companies with revenues of more than $10 million, and the top revenue-producing technology companies irrespective of revenue growth.

Sherman McCorkle, President and CEO of the Sandia Science & Technology Park Development Corporation (SSTPDC), who hosted the program in 2020 stated, As the New Mexico economy enters an impressive growth phase, I think it is important to recognize not only the companies that laid the foundation, but also those who are leading recovery. With aggregate revenues close to $1 billion, these companies deserve our recognition. SSTPDC is excited to carry on the legacy of the program.

More information about the Flying 40 can be found online at

About Flow Science

Flow Science, Inc. is a privately-held software company specializing in transient, free-surface CFD flow modeling software for industrial and scientific applications worldwide. Flow Science has distributors for FLOW-3D sales and support in nations throughout the Americas, Europe, and Asia. Flow Science is located in Santa Fe, New Mexico.

Media Contact

Flow Science, Inc.
683 Harkle Rd.
Santa Fe, NM 87505
Attn: Amanda Ruggles
+1 505-982-0088

Mentor Blog top

► News Article: Graphcore leverages multiple Mentor technologies for its massive, second-generation AI platform
  10 Nov, 2020

Graphcore has used a range of technologies from Mentor, a Siemens business, to successfully design and verify its latest M2000 platform based on the Graphcore Colossus™ GC200 Intelligence Processing Unit (IPU) processor.

► Technology Overview: Simcenter FLOEFD 2020.1 Package Creator Overview
  20 Jul, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, Simcenter FLOEFD helps users create thermal models of electronics packages easily and quickly. Watch this short video to learn how.

► Technology Overview: Simcenter FLOEFD 2020.1 Electrical Element Overview
  20 Jul, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, Simcenter FLOEFD allows users to add a component into a direct current (DC) electro-thermal calculation by the given component’s electrical resistance. The corresponding Joule heat is calculated and applied to the body as a heat source. Watch this short video to learn how.

► Technology Overview: Simcenter FLOEFD 2020.1 Battery Model Extraction Overview
  17 Jun, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, the software features a new battery model extraction capability that can be used to extract the Equivalent Circuit Model (ECM) input parameters from experimental data. This enables you to get to the required input parameters faster and easier. Watch this short video to learn how.

► Technology Overview: Simcenter FLOEFD 2020.1 BCI-ROM and Thermal Netlist Overview
  17 Jun, 2020

Simcenter™ FLOEFD™ software, a CAD-embedded computational fluid dynamics (CFD) tool is part of the Simcenter portfolio of simulation and test solutions that enables companies optimize designs and deliver innovations faster and with greater confidence. Simcenter FLOEFD helps engineers simulate fluid flow and thermal problems quickly and accurately within their preferred CAD environment including NX, Solid Edge, Creo or CATIA V5. With this release, Simcenter FLOEFD allows users to create a compact Reduced Order Model (ROM) that solves at a faster rate, while still maintaining a high level of accuracy. Watch this short video to learn how.

► On-demand Web Seminar: Avoiding Aerospace Electronics Failures, thermal testing and simulation of high-power semiconductor components
  27 May, 2020

High semiconductor temperatures may lead to component degradation and ultimately failure. Proper semiconductor thermal management is key for design safety, reliability and mission critical applications.

Tecplot Blog top

► Multi-threaded Variable Calculations
  19 Jan, 2021

Faster Variable Calculations Through Multi-Threading

Tecplot 360 2020 R2 has additional multi-threading for variable calculations. Under the Analyze>Calculate Variables dialog, all functions listed will be fully multi-threaded. In previous versions, multi-threading was used only if there were multiple zones. Multi-threading is now used within a zone. In the video example, the improvement is 8 times faster than with earlier versions of Tecplot 360. This test was done on an 8-core windows machine computing Q Criterion. The dataset was 8.6 million polyhedrals.

I’ve loaded the dataset, calculated Q criterion, and generated an isosurface. In Tecplot 360 2020 R2 you can see that all 8 cores are used for the computation. In Tecplot 360 2020 R1 only one CPU core is effectively being used. The full computation takes 284 seconds in 2020 R1, and by the time I finish this sentence the computation in 2020 R2 will already be done. And it took only 36 seconds.

The next calculation was tested on a 32-core Windows machine. It was over 11 times faster in Tecplot 360 2020 R2 compared to the previous release. The improvement was not as large with other datasets with multiple zones, because Tecplot 360 2020 R1 already uses multi-threading across zones. But multi-threading within a zone still results in a faster computation: 1.3 times faster for the OpenFOAM dataset, and over two times faster for the Plot3D data.

All three of these multi-threading examples followed the same steps:

  • Load the dataset
  • Calculate Q criterion
  • Create an iso-surface
  • Export the image

The post Multi-threaded Variable Calculations appeared first on Tecplot.

► Consistency is Key: Visual Communication
  12 Jan, 2021

How to plot your data and present it to tell a clear, concise, and convincing story.

It may come as a shock to some of you, but here at Tecplot we have a thing for well-made plots and the presentations that they occupy. There is something eminently satisfying about looking at a set of data that has been thoughtfully reduced into the relevant facts necessary to enable sound engineering judgment. It is in service of that ideal that we have decided to put together a short blog series on how to make plots & presentations that tell a clear, concise, and convincing story.

Our goal isn’t to present anything profound – only to share practical reminders of the importance of effective communication in engineering. An expert engineer doesn’t simply need to understand the science of their discipline – they need to also know how to convey the relevant facts to their colleagues and stimulate productive discussion.

In this first post we’ll tackle something quite tangible – consistency. Consistency is important for comparisons between datasets (or between regions of the same dataset) because it enables the audience to identify significant differences more easily. If your audience is presented with plots that convey similar datasets but use varying format, scale, color, orientation, etc., it distracts them and takes the focus away from what really matters – the story behind the data.

Make Reading Easy


The image at right is an example of two pressure coefficient distribution plots at discrete spanwise locations; we’ll dive into a few of the ways this plot uses consistency to enhance its readability. 

  • The X-axis has the same range in both plots, same with the Y-axis.
  • All axis labels are spaced consistently in both plots. Similarly, labels for the slice location, Mach number, and Pressure_Coefficient are consistently displayed for both plots.
  • The line color is the same shade of blue to reinforce that the same variable is plotted for each slice.

How do you decide what to keep consistent and what to vary? That will depend on the data you are presenting, the type of plot you’re using, and the differences you wish to highlight.

In this example, the two Cp plots at different spanwise locations demonstrate the relative position of the pressure change due the lambda shock structure that is characteristic of the Onera M6 wing.

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Highlight Critical Insights


Line plots are critical for deriving actionable insights from most engineering analyses, but they aren’t the only types of plots that can be useful. The 2D contour plot at right is a great example of how consistency can be used to compare changes in a dataset over time.

This 2D contour plot compares the pressure of fluid flow around a rotating cylinder at two different time steps. Also included is a single streamline to demonstrate the change in the vortex shedding over time.

We have kept our axis labels, markers, and annotations consistent for easy readability. In addition, we have:

  • Fixed the contour levels to make an easier visual comparison of the flowfield pressure changes.
  • Seeded the streamlines upstream of the cylinder at the same location for both plots.

If the contour levels or the streamline location varied between the two plots, you could easily make some inaccurate assumptions about the fluid flow.

Fix the Perspective

Our last example is a 3D contour plot using the Onera M6 wing. The image below is similar to the 2D contour plot in that we’ve kept our contour levels, colormap, and labels consistent between frames. However, when comparing 3D plots it’s important to consider how the 3D perspective can affect your ability to make an unbiased assessment.

Consistency-3D Perspective

The image shows a fixed perspective, which includes the pan, zoom, and rotation settings. You’ll notice that because the volume slices are at different locations the wing appears to move between frames. But the wing is fixed in place. With this approach, it is immediately clear that the plot is not comparing flow states at the same slice location for two different solutions.

Keep the Consistency

Ultimately there are few hard and fast rules when it comes to formatting plots. And it is certainly worth taking the time to ensure that the critical insights from your analyses are not obscured by inconsistency across your plots. Consistency has the most impact when comparing two plots side by side. But don’t underestimate the value of maintaining consistency throughout your presentations – and even between different presentations. Consistency will establish a presentation style that your audience will have an easier time digesting.

Consistency is one tactic to making your plots and your presentations easier to understand. We will explore more plotting tips and tricks in future posts.

Stay tuned!

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The post Consistency is Key: Visual Communication appeared first on Tecplot.

► Alan Klug Named Tecplot President
    5 Jan, 2021

New President and Director of Customer Development set the stage for Tecplot growth in the coming years.

Bellevue, Washington (January 5, 2021) – Tecplot, Inc. today announced that Alan Klug is named Tecplot President. Alan will run day-to-day operations at Tecplot USA. In addition, he will oversee the FieldView CFD and Tecplot Europe business units. Tom Chan will continue as Tecplot CEO in an advisory role but will focus on managing the portfolio of acquisitions within the Vela Software Group.

Tecplot also announced that Charles Schnake, previously with Rolls Royce and Aerion Supersonic, is named Tecplot Director of Customer Development.

“As Tecplot’s Vice President of Customer Development, Alan has been instrumental in our transition and growth under Vela/CSI ownership. I am excited for Tecplot’s future under his direction as President. Please join me in congratulating Alan on his promotion,” says Tom Chan, Tecplot CEO.

“From aerospace to life sciences, our customers continue to make outstanding achievements, even during the difficult past year. I look forward to working closely with them to provide best-in-class solutions and find better ways to serve them,” says Alan Klug, Tecplot President. “And I welcome the experience and expertise Charles brings to Tecplot as he steps into the customer development leadership role.”

“It is my honor and pleasure to assume leadership of Tecplot’s Customer Development team,” says Charles Schnake, Tecplot Director of Customer Development. “Alan has left behind some big shoes to fill, but fortunately we will continue to benefit from his experience and guidance. I am excited to add my energy and perspective to an already successful team, and to forge relationships with our customers and partners.”

Read more about Tecplot Leadership.

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

The post Alan Klug Named Tecplot President appeared first on Tecplot.

► Referencing Variables by Name in Tecplot 360
    9 Dec, 2020

A new capability to reference variables by name has been added to Tecplot 360 2020 R2.

This is important because sometimes you may have two simulation runs where one simulation exports more variables than the other. In this case macros and stylesheets may produce a plot that references incorrect variables.

Referencing Variables by Name

To demonstrate this new capability, we’ll use two CONVERGE simulations. One where NOX is variable number 25 and the other where NOX is variable number 12. When we did our first simulation, we captured more variables than we needed. And for a later simulation we trimmed the output to only the required variables for our study.

If we save this frame style and apply it to our dataset where NOX is variable number 25, the resulting style is incorrect. This is because we saved by variable number, and variable number 12 is NOX for the first dataset but is Equivalence Ratio in the second.

Saving variable names, instead of variable by numbers, in the stylesheet will correct this issue. This capability is not on by default. It can be turned on by uncommenting a line in the Tecplot configuration file. Layouts, stylesheets and macros will now be saved using variable names instead of variable numbers.

When we save the frame style and apply it to our other dataset, that style is applied as expected. Note that if you have duplicate variable names, for example multiple sets of UVW vector variables, the variable name that is first numerically will be picked.

This concludes the tutorial for Referencing Variables by Name. Thank you for watching.

The post Referencing Variables by Name in Tecplot 360 appeared first on Tecplot.

► Tecplot 360 2020 R2 Now Available
    9 Dec, 2020

Tecplot 360 2020 R2 Now Available

BELLEVUE, WA (December 9, 2020) – Tecplot, Inc. has announced the general availability of Tecplot 360 2020 Release 2.

Tecplot 360 2020 R2Tecplot 360 2020 R2 represents our commitment to helping engineers and scientists analyze their results quickly and easily. The reintroduction of Tecplot Chorus ensures that you’re able to understand your simulated results on the whole rather than just as individual cases. We’ve also improved the speed of variable calculations, in some cases up to 6x faster, as well as making Tecplot 360 compatible with additional file formats.

The ability to reference variables by name makes layouts, stylesheets, and macros more flexible, easier to read, and more applicable to other, similar, datasets.

Finally, the ability to split zones that have distinct connected regions makes it easier to isolate data for which you need quantifiable results. For example, an exhaust manifold may have four distinct outlets, this new feature allows you to easily split those outlets into individual zones to compute quantities like averages and mass flow rates to ensure balanced flow.

What’s new in Tecplot 360 2020 R2

  • Tecplot Chorus – Chorus returns with support for 4k monitors and newer operating systems.
  • Faster variable calculations – Variables calculated under the Analyze>Calculate Variables menu are now multi-threaded, providing up to 6x faster variable calculations.
  • CGNS 4 file support – The CGNS loader is now built against CGNS 4.1.2. CGNS 4 added capabilities which allow for faster parallel write of polyhedral data. 
  • Exodus II file support – Tecplot 360 now offers a new data loader for the Exodus II file format.
  • Ability to split a zone into distinct connected regions – A new function has been added to PyTecplot and the Tecplot 360 macro language to create new zones from isolated regions in one or more finite element zones.
  • Reference variables by name – Tecplot 360 can now save variables by name in macros, layouts, and stylesheets and use names when loading data and retaining the existing style.

See all updates in this release.

Download Tecplot 360

Tecplot 360 2020 Release 2 is available for download as Free Trial Software, or for customers through the MyTecplot Customer Portal.

About Tecplot 360

Tecplot 360 is a suite of CFD visualization and analysis tools that can handle large data sets, automate workflows, and visualize parametric results. Three powerful modules in this one tool include:

  • Tecplot Chorus – an analytics tool for exploring large datasets composed of multiple solutions or experiments.
  • SZL Server – a client-server module for accessing data remotely.
  • PyTecplot – a Python API for automating workflows.

Integrated XY, 2D and 3D plotting speeds data analysis and increases productivity. Easy-to-use, fast and memory efficient, Tecplot 360 produces visually powerful output to help engineers and scientists communicate CFD, other simulation and test data results to clients and stakeholders.

The Tecplot 360 suite of tools are available for customers on TecPLUS maintenance service and anyone who downloads a free trial of the software.

Special pricing is available for Academic users upon request, see Tecplot for Academics.

About Tecplot, Inc.

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

Tecplot visualization and analysis software allows customers using desktop computers and laptops to quickly analyze and understand (local or remote) 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
(425) 653-1200

The post Tecplot 360 2020 R2 Now Available appeared first on Tecplot.

► Key Frame Animation in Tecplot RS
    4 Dec, 2020
Key Frame Animations allow you to easily animate a smooth progression through two or more specified views (key frames) and export them as an AVI or MP4. You can zoom, rotate, and translate in these animations. This tool can be used to create reservoir “fly over” movies or first person animations which scan the inside of the reservoir.

To access this dialog, select View > Key Frame Animation. From there, you can start creating animations.

Key Frame Animation

Easily animate a smooth progression of two or more specified views. This tutorial shows you how this feature works.

I will start by appending my initial view, which is where I want my animation to start. Next, I could rotate, zoom, or translate my grid solution and append additional views. I could also move the key frames closer together if I wish to shorten up a movie and name them for easy reference. And now I could animate my key frame animation within the Tecplot RS interface to see what my movie looks like. And finally, I could export the movie into my preferred output format. The resulting output movie can then be shared with colleagues or embedded into a presentation.

The post Key Frame Animation in Tecplot RS appeared first on Tecplot.

Schnitger Corporation, CAE Market top

► Cadence to acquire NUMECA
  20 Jan, 2021

Cadence to acquire NUMECA

And the consolidation continues. Cadence announced this morning that it will acquire NUMECA, maker of the OMNIS CAE framework (though you probably know it for its FINE/Design3D optimization solution and its FINE/Marine virtual towing tank/CFD naval architecture tools — that’s what I first learned about, years ago). I don’t follow Cadence in enough detail to know, so here’s what they said about the logic behind the deal:

The addition of NUMECA’s technologies and talent supports the Cadence Intelligent System Design strategy and broadens its system analysis portfolio with CFD solutions, servicing a fast-moving market segment [by which they mean CFD, I think] where accuracy, reliability and predictability are paramount concerns for high-fidelity modeling … The acquisition builds on the momentum of recent Cadence system innovation developments with the Clarity 3D Solver for electromagnetic (EM) simulation, the Clarity 3D Transient Solver for finite difference time domain (FDTD) system-level EM simulation and the Celsius Thermal Solver for electrical-thermal co-simulation product introductions. NUMECA’s technology will also contribute to Cadence best-in-class system analysis solutions for integrated circuits (ICs), electronic subsystems and full system designs.

Cadence President Anirudh Devgan, said, “The acquisition of NUMECA’s proven CFD technology and talented team complements Cadence’s finite element analysis and other system innovation technologies and is another successful step that will advance our customers’ ability to design the exciting products of tomorrow.”

Tom Beckley, SVP of the Custom IC & PCB Group at Cadence, added, “With the addition of NUMECA’s technology to the Cadence portfolio, we are broadening our system analysis capabilities and integrated design solutions, addressing critical customer challenges in areas such as internal and external flows, acoustics, heat transfer, fluid-structure interaction and optimization.”

Terms of the transaction were not disclosed. The acquisition is expected to close in the first quarter of 2021, subject to customary closing conditions.

It’s amazing that I just this week wrote a piece for NAFEMS on consolidation in CAE — and commented that these acquisitions will only foster more innovation since there’s an obvious path to a payout. I’ll need to add NUMECA+Cadence to that list.

As I said above, I haven’t followed Cadence to any level of detail. That’s because they’ve historically been EDA (electronic design automation)-focused, and that’s not my wheelhouse. But as they stretch into new areas of CAE add more 3-D solvers to their electromagnetic and thermal offerings (and the NUMEACA OMNIS platform becomes more widely promoted), Cadence can move further into marine, aerospace, automotive, and industrial markets. Definitely, something to watch.

► Totally OT: Honoring MLK
  18 Jan, 2021

I wrote the piece below in 2014 and can’t believe how much has happened since then — and, but, how little progress we’ve made in making the world a more equal place. Dr. King’s message was simple: all people are created equal and it’s our actions that make them unequal. In health care, in housing, in the job market, in educational opportunities, in all of the areas of life that many of us take for granted. His passion and eloquence were awesome and his words are more important today than ever.


Today the US stops (mostly) to remember and honor Martin Luther King, Jr. –known as MLK– for his work and legacy, redefining what it means to fight for the right to be equal. I was too little to remember Dr. King’s speeches from when he was alive but I do remember the aftermath of his death. My family moved to New York City in 1967; on April 4, 1968, Dr. King was shot while preparing to lead a protest march in Memphis, Tennessee. Either that same evening or the next day (news traveled a lot more slowly back then), the mayor of New York asked the city to stay calm and said that the city’s leaders would continue Dr. King’s work to end poverty. We saw, in school and on the nightly news, other cities in the US erupt in flames as grief and anger overcame Dr. King’s principles of nonviolence and working with the political system to create change. My parents wondered if I’d be safe, walking the few blocks to school — and I was. New York was far from perfect in the 1960s, but this was something to be proud of.

Unfortunately, it took his assassination to cause adults to talk to us about freedom, equality, poverty and nonviolence. But once those conversations start, they cannot be stopped and I’m sure they profoundly changed my views of the world, and those of the other little kids in the classrooms and living rooms around the US.

Dr. King’s legacy only grew after his death and, at least in the schools I attended school in New York, his work and speeches came front and center in the curriculum. If you have a few minutes, it’s worth your time to read his Letter from a Birmingham Jail and watch his most famous speech, I Have A Dream.

His words about how we are all connected, how every human being matters, are as true today as they were when he first wrote them.

Image credit: Nobel Prize, Nobel Media AB

► Maybe the end to fiscal 2020 wasn’t so bad — SAP and AVEVA update
  15 Jan, 2021

Maybe the end to fiscal 2020 wasn’t so bad — SAP and AVEVA update

First things first: the lack of financial news in the PLMish world is encouraging. If the December quarter (meaning October+November+December) had been far below expectations, we would have heard from companies issuing earnings pre-announcements. There’s only been one of those, from SAP, so, so far so good.

And even SAP’s wasn’t dire. SAP said last night that business improved sequentially –meaning that the fourth quarter was better than the third– “even as the COVID-19 crisis persisted and lockdowns were reintroduced in many regions”. We’ll get details when they release their full details on January 29, but here are two possible interpretations: SAP customers are learning how to work within these restrictions and are using more SAP kit to do it and/or SAP has learned to sell from afar. Of course, even that can’t help all parts of SAP’s business; the company said its business travel-related cloud service continues to struggle.

Overall, though, SAP says preliminary data for Q4 has software licenses revenue down 15% year over year (down 11% in constant currencies, cc). Cloud revenue was up 8% (up 13% cc), and total revenue was down 6% year over year to €7.54 billion (down 2% cc). That doesn’t sound great but is better that SAP (and its investors) had expected.

SAP CFO Luka Mucic said, “In a uniquely challenging environment, 2020 was a record year for cash flow in every single quarter and the full year. Our better-than-anticipated top line performance combined with our quick response on the cost side drove strong operating profit. SAP’s expedited shift to the cloud will drive long-term, sustainable growth while significantly increasing the resiliency and predictability of our business.”

We heard more upbeat news from AVEVA, which released a trading update this morning. The company said the December quarter was “strong [with] Organic constant currency revenue growth over 26%. This was driven by a significant number of scheduled subscription renewals, including a large three-year contract renewal in the Food sector. It also benefited from the early renewal of a large three-year EPC contract that had been scheduled for AVEVA’s Q4 [meaning, the March 2021 quarter], and the conversion of two large contracts in the Marine sector from annual fees to multi-year subscription, giving the customers more flexibility in a challenging Marine market environment.”

So much to unpack there. First, 26% cc growth is astonishing. Remember that the December 2019 quarter was still normal, pre-COVD, business-as-usual, so not obviously an easy comparable. Next, early renewals are awesome but rare and show how important AVEVA is to that customer’s business. Last, good news from the Marine sector! I can’t remember the last time that happened.

AVEVA said the strong December quarter boosted revenue growth to “approximately 1.5% in the nine months to 31 December 2020 on an organic constant currency basis.”

The company also gave a quick update on its acquisition of OSIsoft: The deal is a go, with only the US’ Committee on Foreign Investments (CFIUS) still to approve. AVEVA says it expects to receive that by early February, and that the transaction should close shortly afterwards.

What does it all mean? I think we’re in for a bumpy ride this earnings season. Good news when buyers and sellers find each other, even in a remote selling situation. Bad news when sales require a lot of in-person contact or on-site services. More good news where vendors have and buyers want cloud technology. And bad news when the inverse are true.

Based on two data points, though, business in the December quarter was better than in September quarter. Let’s hold on to that.

► 3D Systems completes sale of Cimatron and GibbsCAM – and totally OT thoughts on what happened at the US Capitol
    7 Jan, 2021

3D Systems completes sale of Cimatron and GibbsCAM – and totally OT thoughts on what happened at the US Capitol

3D Systems announced today that it completed the sale of Cimatron and GibbsCAM to “a subsidiary of ST Acquisition Co., an affiliate of Battery Ventures” on January 1, 2021. 3D Systems said it netted around $64 million in the sale, part of which it used to pay down debt in order to improve its financial position and move along in its reorganization plan.

Jeffrey Graves, CEO of 3D Systems, said, “The divestiture of Cimatron and GibbsCAM, which were businesses focused on subtractive technologies, was an important step in our plans to refocus our company on our core mission – ‘to be the leader in enabling additive manufacturing solutions for applications in growing markets that demand high-reliability products.’ These divestitures strengthened our balance sheet, enabling us to both pay off our debt and terminate the ATM Program [a program under which it sold shares of its common stock to raise cash] much earlier than originally planned.”

This update is important to many people –employees, customers, partners, and friends of Cimatron and GibbsCAM, 3D Systems, and Battery Ventures, among others– but it isn’t the piece I intended to write today. I drafted and (digitally) tore up my thoughts on yesterday’s storming of the US Capitol numerous times. You don’t come here for politics, and I get that. But silence isn’t really an option for me.

Let’s just say that Winston Churchill was right: “Democracy is the worst form of government except for all the rest.” Those who enabled and riled up these rioters must be held accountable — whether by law or at the ballot box, next time they come up for election. But right now, we have so much work to do that we can’t let this temper tantrum sideshow distract us. On Wednesday. according to the Boston Globe, more than 3,000 people in the US lost their lives to the coronavirus. That means 3,000 more families got truly devastating news. And that’s just one of the many things that need our attention. Let’s cut the !@#$ and get to work.

Thank you to the police and National Guard who protected our elected officials and government workers.

► CGTech, Vericut now part of Sandvik
    4 Jan, 2021

CGTech, Vericut now part of Sandvik

I hope you had the opportunity to unwind and relax as 2020 came to a close. Let’s hope 2021 is a much kinder year!

To start off the new year, a quick update: While we were pondering New Year’s Eve cocktails, Sandvik announced that it completed its acquisition of CGTech, make of numerical control (NC/CNC) simulation, verification, and optimization solutions. You likely know them for their Vericut offering.

Happy New Year!

► From our bubble to yours …
  22 Dec, 2020

There’s no doubt: 2020 was hard. We made it this far, though, and look forward to meeting up with you in person in a 2021 that’s better in every way!


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