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Another Fine Mesh top

► This Week in CFD
  16 Apr, 2021

This Week in CFD reached convergence long before I had exhausted the two-week backlog of news. With baseball season underway here in the US, fans will enjoy the case study describing how high-fidelity CFD can predict the trajectory of various … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

► This Week in CFD
    2 Apr, 2021

It’s a good Friday for the latest roundup of CFD flotsam and jetsam from the ocean that is the internet. [Making this the Great CFD Garbage Gyre?] We use the marine theme because, for unexplained reasons, there are a lot … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

► Resolving Boundary Layers with Unstructured Quad and Hex Meshing: On-Demand Webinar
  31 Mar, 2021

All things being equal, CFD practitioners prefer to use hexahedral mesh cells in the boundary layer for the improved robustness and accuracy they bring to the flow solver. Traditionally, a hex grid would be created using a structured grid technique … Continue reading

The post Resolving Boundary Layers with Unstructured Quad and Hex Meshing: On-Demand Webinar first appeared on Another Fine Mesh.

► This Week in CFD
  19 Mar, 2021

Completely fungible and non-tokenized, today’s CFD news begins with a hopeful glimpse at a potential in-person CFD event. You should check-out the list of tips for simulation projects and let us know what was missed. And the coolest news this … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

► Geometry Modeling and Mesh Generation – Part 3
  18 Mar, 2021

In this final installment in our series of posts about the paper Preparation of Geometry Models for Mesh Generation and CFD, we take a peek at what the paper has to say about geometry model suitability. Or more accurately, the … Continue reading

The post Geometry Modeling and Mesh Generation – Part 3 first appeared on Another Fine Mesh.

► This Week in CFD
  12 Mar, 2021

This week’s CFD news is dominated by applications but overshadowed by the image of the week from your friends at Ferrari (shown here). It’s a CFD solution, but probably not what you think at first. There are also some notable … Continue reading

The post This Week in CFD first appeared on Another Fine Mesh.

F*** Yeah Fluid Dynamics top

► “Geodaehan”
  16 Apr, 2021

In “Geodaehan” Roman De Giuli’s macro fluid art mimics massive landscapes. The film takes us over deltas, rivers, glaciers, and landslides. Some look like earthbound locations, others look like something from Mars or Titan. All are, in fact, paint, ink, and glitter on paper! It’s truly incredible how artists capture large-scale fluid physics on such a tiny canvas. (Image and video credit: R. De Giuli)

► A Macro View of Weathering
  15 Apr, 2021

Water constantly weathers sedimentary rock, both physically — through abrasion — and chemically — through dissolution and recrystallization. Now researchers have gotten their first view of this weathering at the Ångstrom level by observing porous rocks with environmental transmission electron microscopy as they interact with both water vapor and liquid water.

As expected, the experiments with liquid water showed that water dissolved the rocks and substantially changed the geometry of the rock’s pores. But the experiments also showed significant weathering from water vapor alone. The researchers found that water vapor formed a film on the surface of the rock’s pores in a process known as adsorption. This film substantially decreased the size of each pore and created strain in the rock. Once the water vapor was removed, the rock’s pores were notably altered, supporting the idea that this adsorption was, itself, a form of weathering. (Image credit: M. Kosloski; research credit: E. Barsotti et al.; via AGU EOS; submitted by Kam-Yung Soh)

► An Oasis Among Dunes
  14 Apr, 2021

The Saudi Arabian oasis of Jubbah sits in the bed of an ancient lake. It’s protected from the westerly winds that sculpt the surrounding dunes by the wind shadow of the mountain Jabel Umm Sinman. The long, skinny shape of the settlement reveals the shape of the mountain’s wake! (Image credit: NASA; via NASA Earth Observatory)

► Swapping Emulsions
  13 Apr, 2021

Chemically speaking, oil and water don’t mix. But with a little fluid mechanical effort, it’s possible to make them an emulsion — a mixture of oil droplets in water or water droplets in oil. Researchers in the Netherlands discovered that the viscosity of these emulsions depends critically on which of those mixtures you have.

To create their emulsions, the team used a tank consisting of two concentric cylinders. When the inner cylinder spins, it creates a well-understood flow field between the inner and outer cylinder. By varying the ratio of oil to water in the tank, they could explore a wide range of emulsions. They found that the emulsion’s viscosity changed dramatically when the emulsion shifted from oil droplets in water to water droplets in oil, something known as a catastrophic phase inversion. During this switch the viscosity dropped from 3 times higher than pure water to 2 times lower! (Image credit: A_Different_Perspective; research credit: D. Bakhuis et al.; via APS Physics; submitted by Kam-Yung Soh)

► Protecting From Storm Surge
  12 Apr, 2021

The most dangerous and destructive part of a tropical cyclone isn’t the wind or rain; it’s the storm surge of water moving inland. This landward shift of ocean takes place because of a cyclone’s strong winds, which drive the water via shear. The depth storm surges reach depends on the wind speed and direction, shape of the shoreline, and many other factors, making exact predictions difficult.

Fortunately, engineers can — with enough foresight and investment — build structures and networks to help protect developed land from storm surge flooding. (Image and video credit: Practical Engineering)

► Lava at Night
    9 Apr, 2021

Today’s cameras and drones capture volcanic eruptions in ways that were unthinkable in years past. This incredible footage shows the recent eruption in Iceland as it glows in the night. I love the crisp details of the flow. You can clearly see how the hotter, molten lava moves compared to the cooling crust. There’s some great footage of spurting fountains and blocks of lava getting swept along by the river. Enjoy! (Image and video credit: B. Steinbekk; submitted by jpshoer)

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

read more

CFD Online top

► OpenFOAM tips and tricks
  16 Mar, 2021
I am sharing a few tips and tricks for better OpenFOAM simulations. These are not any new tips I discovered. These are just some which I felt most useful based on my experience.

1. Mesh is the most important aspect - I have spent countless hours fixing issues in other places while the real issue was with my mesh. As a thumb rule, make sure the mesh has: no non-orthogonality issues, low aspect ratio cells, no refinement zones in the region with high gradients

2. Always start with a coarse mesh - fix your problems fast!

3. OpenFOAM tutorial cases are not fine tuned, so don't use them blindly for your case. Check the boundary conditions used by tutorial in Doxygen (or the header file in src) and ensure that the right parameters are chosen for your model physics.
Eg: totalPressure BC uses rho=none for incompressible and rho=rho for compressible subsonic.

4. For internal flow, ensure that the mass flux is stable at the inlet and outlet. For statistically stationary flows, the average mass flux should be constant/ periodic.

<more to be added as I keep learning :)>
► Boundary conditions for multiphase flow
  16 Mar, 2021
I have struggled a lot with boundary conditions for multiphase flow, particularly where the multiphase spray is impinging on the outlet patch (typically the case of an internal flow simulation of a fuel injector spray). I have finally settled on the following Boundary conditions which work!

Simulation details: I am doing a 2D axisymmetric flow simulation using a wedge BC at front/ back planes. I am using a custom solver based on homogeneous relaxation model. This is similar to cavitatingFoam solver available in OpenFOAM which uses homogeneous equilibrium model.

Mesh: For multiphase flow, it is very very important to ensure a good quality mesh, which includes the following:
1. Avoid refinement zones - At the refinement zones boundaries, I saw spurious pressure fluctuations. Therefore, avoid them at all costs.
2. Low aspect ratio cells - Ensure that the aspect ratio of cells remains low.
3. Smooth grading in cells

Boundary conditions:

I tried several combinations for pressure boundary condition but ended up getting pressure oscillations within the domain due to the two phase flow at the outlet patch. The solution I found is to use totalPressure BC for pressure at Inlet, outlet patch.

BCs combination I finally settled on:

Pressure: totalPressure at Inlet, Outlet
Velocity: pressureInletVelocity at Inlet, pressureInletOutletVelocity at Outlet
density: zeroGradient works (I use a custom BC which is similar to ZG)

Refer to the following link for useful BC combinations for internal flow:
► 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
Click image for larger version

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Attached Images
File Type: jpg mesh_Turbocharger.jpg (72.0 KB, 125 views)
File Type: jpg rhoSimpleFoam_Turbocharger.jpg (25.7 KB, 127 views)
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Attached Files
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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, 64 views)
File Type: pdf Integral Equation Methods, Lecture 1.pdf (587.5 KB, 80 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

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

► The Collaboration Effect: Developing a New Generation of Gas Turbine &#038; Rotating Detonation Engines
  22 Mar, 2021

From the Argonne National Laboratory + Convergent Science Blog Series

Imagine this: You’re flying on a plane. Maybe you’re sitting in the window seat, eating airline pretzels, happily watching an in-flight movie. But then—the flame in one of the plane’s gas turbine engines blows out. Should you panic? Well, ideally you wouldn’t even notice as the engine automatically relights and you continue cruising safely to your destination. But why did the engine blow out? Can we prevent that from happening? And if it does blow out, how can we ensure the plane stays airborne?

These are among the critical questions that Argonne National Laboratory and Convergent Science investigate together. If you’ve been following this series, you’ll know their collaboration started off focused on piston engines for automotive applications. But combustion engines across the board, including airplane engines, feature similar physical processes, and the research goals are frequently the same: increase efficiency and reduce emissions. In addition, CONVERGE’s unique combination of autonomous meshing, fully coupled detailed chemistry, and high-fidelity physical models for spray, turbulence, and combustion make it a great tool to help engine designers reach those goals. 

Before industry can implement 3D simulation into their design process, however, they need appropriate, well-validated models. This is where Argonne and Convergent Science come in—the core objective of their collaboration is performing fundamental research and developing models that industry can use to advance technology. In pursuit of this objective, Argonne and Convergent Science expanded their research efforts to aviation engines and beyond.

Gas Turbines

Gas turbine engines today are the most commonly used propulsion system for airplanes, and they are also widely used for power generation. Two key areas of current gas turbine research are increasing efficiency and reducing pollutant emissions. There are several approaches to achieving these goals, including the use of alternative fuels, altering the combustion environment (e.g., increasing operating pressures and temperatures), or reducing the fuel flow rate and moving toward a leaner combustion regime.

This lean burn approach, while effective at reducing emissions, poses significant design challenges. If you run the gas turbine engine too lean, the primary zone of the combustor can get too cold and the flame can blow out. This phenomenon, called lean blow-off or lean blow-out (LBO), is the reason the plane engine went out during our imaginary flight. Clearly, LBO is undesirable, and predicting the conditions at which it occurs is a primary focus for Argonne and Convergent Science, as well as for the broader gas turbine community.

LBO limits vary from fuel to fuel, and understanding these differences is critical, especially as alternative fuels become increasingly widespread. “The flame stabilization characteristics depend on the physical as well as the chemical properties of a given fuel, so our aim is to develop computational models that can predictively capture this behavior and the difference in performance between conventional and alternative fuels,” said Dr. Prithwish Kundu, Research Scientist at Argonne National Laboratory.

Using CONVERGE, Argonne and Convergent Science engineers investigated the LBO limits for two fuels: A-2 (a conventional Jet-A fuel) and C-1 (an alternative fuel)1. They conducted large eddy simulations (LES) of a realistic aviation gas turbine combustor from the U.S. National Jet Fuels Combustion Program (NJFCP). The combustor geometry preserved all flow passages and included the dome, liners, dilution jets, and effusion cooling holes. A Lagrangian approach was used to model the spray and atomization of the liquid fuels, and detailed chemistry was used to simulate combustion.

Gas turbine simulations tend to be computationally intensive because of the large computational domain, complicated geometry featuring a wide range of length scales (e.g., from millimeter-sized holes to a meter-long combustor), and complex physical processes. Argonne and Convergent Science engineers leveraged CONVERGE’s autonomous meshing to speed up the simulation setup and runtime. Automatic mesh generation saved weeks of time on the simulation setup, and Adaptive Mesh Refinement (AMR) helped shape the optimal mesh for desired spatial resolution to capture the complicated physical phenomena while keeping the overall cell count relatively low. 

With this method, Argonne and Convergent Science were able to accurately predict the difference in LBO limits for A-2 and C-1 fuels (Figure 1). Original equipment manufacturers (OEMs) have long desired a tool capable of predicting LBO, and demonstrating that CONVERGE is able to identify these limits in a reasonable amount of time is a significant achievement.

Figure 1: LBO results for A-2 and C-1 fuels in a gas turbine combustor.
CONVERGE simulation of LBO in a gas turbine combustor for A-2 (top) and C-1 (bottom) fuels.

Having validated CONVERGE’s ability to predict LBO for conventional and alternative fuels, Argonne and Convergent Science engineers are turning to high altitude relight, which is the key to keeping our planes in the air should LBO occur. High altitude relight happens under challenging conditions, i.e., very low temperature and pressure. The NJFCP is currently developing an experimental database for high altitude relight, which Argonne and Convergent Science plan to use to validate their CONVERGE simulations. Overall, these studies pave the way for creating cleaner gas turbine engines, while also ensuring the safety of air travel.

Rotating Detonation Engines

Improving traditional gas turbines is only one way to achieve high-efficiency, low-emissions engines for the aerospace and power generation industries.

“Emissions standards are regularly becoming more stringent, so gas turbines have to evolve accordingly,” said Dr. Gaurav Kumar, Principal Engineer at Convergent Science. “With stricter regulations, the technologies may need to be not just evolutionary, but revolutionary.”

Rotating detonation engines (RDEs) are one potentially revolutionary technology. RDE is an advanced engine concept that is both robust and scalable—you can run an RDE at a fairly wide range of fuel-air equivalence ratios, and you can produce both small and large engines from essentially the same design (Figure 2).

Figure 2: The basic RDE design, consisting of two cylinders, one inside the other, with a thin gap in between, called the annulus. Fuel and air are fed in at the bottom of the cylinder, and ignited with a spark to generate a combustion wave. The combustion wave becomes supersonic (i.e., detonative) and rotates around the RDE in the annulus.

Compared to deflagrative combustion (which is typical in most gas turbine engines), detonative combustion offers a number of benefits, including a substantial increase in efficiency and decrease in emissions. Detonative combustion also provides greater thrust for the same amount of fuel, which is a significant advantage for propulsion applications, such as powering aircrafts and rockets. 

However, RDEs are still in the development phase, and there are certain challenges that have kept them from becoming widely adopted.

“First, maintaining a stable detonation wave is tricky, given that the mixing is highly complex and chaotic,” said Dr. Pinaki Pal, Research Scientist at Argonne. “Thermal management is another challenge, because RDEs have a high thermal load that is unequally distributed throughout the device due to the cyclic combustion wave. This behavior can fatigue the device and shorten its lifespan.”

In addition, an RDE is a difficult environment in which to take experimental measurements. Any instrument you use must be able to capture the high frequencies and large amplitude range of the RDE, while also surviving the harsh conditions inside the device. Moreover, many experimental tools provide averaged results, such as the average temperature or pressure at the device exit. These tools fail to capture the transient nature of an RDE as the detonation wave travels around the engine. Ultimately, new methods to analyze RDEs are needed.

CFD allows you to probe any point in time and space within your computational domain, so researchers can leverage simulations to better understand the chaotic, supersonic combustion in an RDE. To that end, Argonne and Convergent Science engineers simulated both hydrogen- and ethylene-fueled RDEs in CONVERGE using detailed chemistry, LES, and autonomous meshing2,3.

CONVERGE simulation of a hydrogen-fueled RDE, with a geometry that corresponds to a design from the U.S. Air Force Research Laboratory. A spark-ignited flame travels up the pre-detonation tube and initiates a rotating detonation wave in the annulus. Hydrogen and air are injected through the inlet ports and the circumferential slot, respectively, at the bottom of the annular chamber. Before entering the annulus, the fuel and air undergo jet-in-crossflow type mixing within the mixing channel. When limit-cycle is reached, a self-sustaining rotating detonation wave continues to propagate within the RDE channel (shown in the second view).

Argonne and Convergent Science engineers quantified several key characteristics of the detonation wave, including wave height and frequency, for the hydrogen- and ethylene-fueled RDEs. The results are shown in Tables 1 and 2, respectively. For both cases, CONVERGE accurately captures the key RDE parameters compared with experimental data from the U.S. Air Force Research Laboratory.

Case Wave frequency (kHz) Wave height (mm) Fill height (mm) Oblique shock angle (mm) Air plenum pressure (kPa) Fuel plenum pressure (kPa) Channel pressure at 2.54 cm (kPa)
Expt. 3.69 34 ± 7 46 ± 4 53 ± 5 239 276 139
Sim. 3.60 35.6 47.5 51 256 292 142
Table 1: Comparison of experimental and simulation detonation wave characteristics for a hydrogen-fueled RDE2.
Case (method) Wave speed (m/s) Lift-off height (normalized) Wave height (normalized)
1 (expt.) 1035.9 ± 50 1 1
1 (sim.) 975.2 ± 40 1 1
2 (expt.) 1036 ± 50 1.1 0.78
2 (sim.) 978.8 ± 20 1.05 0.63
3 (expt.) 1014.5 ± 50 0.85 1.4
3 (sim.) 958.4 ± 30 0.83 1.39
Table 2: Comparison of experimental and simulation detonation wave characteristics for an ethylene-fueled RDE3.

“With CONVERGE, we’re able to get good quality combustion results with about 10–15 million cells, when other codes were using 90 million cells or more,” said Scott Drennan, Director of Gas Turbine and Aftertreatment Applications at Convergent Science. “And one of the key ways we’re able to do that is through Adaptive Mesh Refinement, which allows us to track the detonation wave by refining the mesh when and where it’s needed at every time step.”

Argonne and Convergent Science also employed a computational diagnostic tool called chemical explosive mode analysis (CEMA) to better understand the local combustion regime. This technique had previously been applied to diesel and scramjet engines, but this was the first time it was implemented for an RDE. Based on an eigenanalysis of the local chemical Jacobian, CEMA is able to identify local combustion modes, such as auto-ignition, deflagrative fronts, and local extinction.

“We demonstrated that CEMA is able to accurately capture the local combustion behavior within an RDE,” said Dr. Pal. “What we would like to do next is develop an on-the-fly dynamic adaptive modeling technique to prescribe regime-dependent combustion models based on the local combustion regime identified by CEMA, which would drastically reduce the computational cost and enhance the accuracy of a CFD simulation.”

In addition to further CEMA studies, there are several other areas of research that Argonne and Convergent Science plan to pursue. One project currently underway is extending the modeling approach used for the studies described above to rocket RDEs. Up to this point, Argonne and Convergent Science have simulated air-breathing RDEs. Now, they are investigating a methane-fueled rocket RDE that uses oxygen instead of air as the oxidizer. Another upcoming project is to simulate the combustor coupled with the turbine in order to evaluate the overall performance of the system. These predictive CFD models will enable engineers to gain more insight into the combustion phenomena in an RDE and to develop design strategies that can help propel the technology into the mainstream.


As Argonne and Convergent Science work to achieve more predictive engine simulations, one area that holds significant potential for improvement is spray modeling. One of the simplest questions we can ask is, “Where does the fuel go?” The trajectory of the spray impacts all of the downstream processes in a combustor: fuel-air mixing, ignition, combustion, emissions, and thrust. But actually determining where the fuel goes is anything but simple.

“It’s a beautifully complex problem,” said Dr. Gina Magnotti, Research Scientist at Argonne National Laboratory. “The spray is sensitive to the local operating conditions, the injector geometry, the fuel properties—and we don’t necessarily have a full grasp on all of the salient physics that control the fuel spray atomization. What happens in the first few millimeters from the injector or atomizer exit has great consequences for the fuel-air mixing and the dispersion of the spray.”

Both gas turbines and RDEs feature jet-in-crossflow type mixing, so Dr. Magnotti and her colleagues conducted a CFD study to better understand this process4. They synthetically imposed realistic surface roughness inside the injector geometry. For their CONVERGE simulations, they coupled LES with a volume of fluid (VOF) approach to understand how the initial flow development impacts the spray formation process. The results were compared to experimental measurements taken at Argonne’s Advanced Photon Source (APS). Ultimately, they found that imposing realistic surface roughness affects crosswise stretching of the jet and distribution of liquid mass, as shown in Figure 3.

Figure 3: A local increase in equivalent path length (EPL) or mass distribution relative to the nominal geometry for jets issued from injectors with imposed surface roughness level of 1.25 μm (left) and 2.50 μm (right).

This study demonstrated that there is still much to learn about the fuel injection process, and Argonne and Convergent Science plan to continue research in this area. A better understanding of the link between internal injector flow and spray formation will provide more accurate boundary conditions for gas turbine and RDE simulations, which will improve their predictive capability. 

Fearless Engineering

The overarching goal of all these projects is to develop predictive computational models that industry can use to design revolutionary technology. The collaboration between Argonne and Convergent Science enables the fundamental research necessary to develop these models and provides a path for the models to get into the hands of industry. Working with Argonne also helps Convergent Science extend CONVERGE’s capabilities to new application areas and enables cutting-edge research in new, exciting fields. As Dr. Dan Lee, Co-Owner and Vice President of Convergent Science, puts it:

It’s a privilege to work with organizations like Argonne. One of the greatest ways to learn about new applications or expand your value proposition in new applications is to partner with people who already have experience in that area. When we partner with Argonne, we’re dealing with experts in a wide variety of applications. And what’s more is that any new area we want to go into, even if Argonne doesn’t currently have expertise in that particular area, they’re used to going into new research areas—they’re fearless. And that’s a great combination: talented, experienced, fearless.

Pushing fearlessly into these new research areas—aerospace, power generation, and more—allows for a greater impact on society, helping to bring about a cleaner and safer world.

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


[1] Hasti, V.R., Kundu, P., Kumar, G., Drennan, S.A., Sibendu, S., Won, S.H., Dryer, F.L., and Gore, J.P., “Lean Blow-Out (LBO) Computations in a Gas Turbine Combustor,” 2018 AIAA/SAE/ASEE Joint Propulsion Conference, AIAA 2018-4958, Cincinnati, OH, United States, Jul 9–11, 2018. DOI: 10.2514/6.2018-4958

[2] Pal, P., Xu, C., Kumar, G., Drennan, S.A., Rankin, B.A., and Som, S., “Large-Eddy Simulation and Chemical Explosive Mode Analysis of Non-Ideal Combustion in a Non-Premixed Rotating Detonation Engine,” AIAA SciTech 2020 Forum, AIAA 2020-2161, Orlando, FL, United States, Jan 6–10, 2020. DOI: 10.2514/6.2020-2161

[3] Pal, P., Xu, C., Kumar, G., Drennan, S.A., Rankin, B.A., and Som, S., “Large-Eddy Simulations and Mode Analysis of Ethylene/Air Combustion in a Non-Premixed Rotating Detonation Engine,” AIAA Propulsion and Energy 2020 Forum, AIAA 2020-3876, Online, Aug 24–28, 2020. DOI: 10.2514/6.2020-3876

[4] Magnotti, G.M., Lin, K.-C., Carter, C.D., Kastengren, A., and Som, S., “A Computational Investigation of the Effect of Surface Roughness on the Development of a Liquid Jet in Subsonic Crossflow,” AIAA Propulsion and Energy 2020 Forum, AIAA 2020-3880, Online, Aug 24–28, 2020. DOI: 10.2514/6.2020-3880

► 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 &#038; 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 &#038; 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.

Numerical Simulations using FLOW-3D top

► Communicate Your CFD Results with Confidence
  14 Apr, 2021

The ability to visualize and present clear, meaningful analysis of simulation data is a crucial part of the CFD simulation process. FLOW-3D POST provides all the necessary tools to effectively communicate your simulation findings. In this blog, we’ll explore some powerful features of FLOW-3D POST so that you can communicate your CFD results with confidence.

Here is an example of an aluminum wheel casting, simulated using the low pressure die casting workspace in FLOW-3D CAST. The objective of this simulation is to find the steady state thermal condition within the mold. Thermal die cycling is used to visualize mold performance and ensure that there is a proper thermal gradient in the mold before beginning the filling simulation. This image shows a few easily accessible features in FLOW-3D POST.

Using multiple viewports allows the user to get a complete visualization to examine areas and features of interest. This is also useful when wanting to compare multiple designs. Notice that the top left viewport is using a 3-dimensional slice; 2D and 3D slicing options allow the user to visualize the internal features of geometries and flow systems.

The bottom viewports show a graphical output of history data based on the thermal energy transferred to the mold components.

FLOW-3D POST features
Aluminum wheel casting, simulated using the low pressure die casting workspace in FLOW-3D CAST.

The legend and annotation properties are fully customizable, allowing you to label and display your data clearly and effectively. Additional features shown here include a customized color scale and opacity setting for the mold visualization.

FLOW-3D POST CFD results

As we go deeper into the functionality of FLOW-3D POST we see some powerful ways to extrapolate information. In this tailings breach example, we can see the use of a spreadsheet data view on the right. Here, data from individual cells can be highlighted and isolated, helping the user illustrate flow features at a mesh cell-level of precision. In the panel on the left, we can see the calculator function, which can be used to manipulate history data on the fly. If quick decision-making is important to the project, built-in capabilities such as exporting data for statistical analysis can provide macro- and micro-level viewpoints of the results efficiently. 

Stay tuned! We will be diving into some of the new features including ray tracing, cell-level data extrapolation, and key framing. In the meantime, go to our FLOW-3D POST product page to see some of examples of how powerful of a tool FLOW-3D POST is.


At Flow Science we develop innovative solutions that help our customers conceptualize, create, and analyze their simulations with confidence. If you would like more information or a personal demonstration of any of our FLOW-3D products and FLOW-3D POST, please contact us at

Ajit D'Brass

Ajit D'Brass

Metal Casting Engineer at Flow Science

As a teaser for our next post, here is a simulation image of a hydraulic jump produced with the ray tracing feature.
► Good Hardware Means Improved FLOW-3D POST Experience
  18 Mar, 2021

Good Hardware Means Improved FLOW-3D POST Experience

To take full advantage of FLOW-3D POST, our advanced state-of-the-art postprocessor, it is essential that you have good hardware. In this blog, Stephen Sanchez, GUI Developer/Manager, Software Engineering, gives his two cents on how you can obtain the optimal FLOW-3D POST experience by following these hardware recommendations.

High quality graphics hardware

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

Next, you should not use Intel integrated graphics as your primary graphics hardware. Intel integrated graphics are common in most laptops, even in laptops with dedicated graphics hardware (more on this below). Most of FLOW-3D POST’s functionality does not work with this configuration, and as a result, we do not support Intel integrated graphics. FLOW-3D POST performs best when used with NVIDIA graphics cards. We highly recommend NVIDIA graphics hardware from the Maxwell architecture family and higher, as we have found it works well with FLOW-3D POST. NVIDIA Quadro cards have proven to be the most reliable. While high-end AMD cards should also work, we have found that they are not as reliable as NVIDIA hardware and drivers, so we always recommend NVIDIA over AMD.

Nvidia graphics card

Dual graphics cards on laptops – A simple but hidden solution

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

Switching graphics card to Nvidia

Update your video driver

We recommend that you check to make sure that your video driver is updated. We have had reports of artifacts and display issues in FLOW-3D POST which were easily resolved by updating the video driver. Keeping your video driver current is a good way to avoid such issues.


It is important to be aware of memory requirements, as insufficient memory can not only lead to the inability to postprocess your simulation; it can lead to as high as a 10x performance decrease! The amount of RAM needed for FLOW-3D POST depends on several factors, especially the size of your simulation. In order to provide users with the most flexibility, we have the following RAM recommendations based on the number of cells in your meshes:

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

FLOW-3D POST can be memory intensive. If you have a rough idea of the simulation sizes you will be running, then we recommend that you follow these guidelines as closely as possible.  That said, we always recommend getting the most RAM possible, regardless of the problem size, to maximize flexibility and ensure the smoothest FLOW-3D POST experience.

► Senior Applications CFD Engineer – Water
  16 Mar, 2021

Flow Science, Inc. is the developer of FLOW-3D HYDRO, a computational fluid dynamics (CFD) software specializing in transient, free-surface flows. FLOW-3D HYDRO is recognized as the premier tool for 3D free surface modeling in applications related to the civil and environmental engineering industry, including dams & spillways, conveyance infrastructure, rivers & environmental, ports & coastal, and water treatment. We have a large global user base that includes governmental agencies, private consultants, and academic research institutions.

Senior Applications Engineer – Water

Flow Science has an immediate opening for a Senior Applications CFD Engineer – Water. The candidate will work in collaboration with our sales, marketing, support, and development teams to deliver market growth leadership for the water treatment and conveyance application areas.

Principal responsibilities

The ideal candidate will have a strong background in fluid mechanics, physical, chemical and biological processes in WWTP, and applying industry standard modeling and design approaches. The candidate will also have experience and a strong passion for CFD and developing its use as a design and analysis tool to advance the state of practice within the civil and environmental engineering industry. Candidates should have exceptional oral and written communication, presentation, and interpersonal skills. The candidate should have the ability to work both independently and as part of a team.

Responsibilities include:

  • Provide technical leadership as a subject matter expert on all areas related to CFD for water treatment and conveyance infrastructure applications areas.
  • Provide market growth leadership by developing close working relationships with industry partners to help guide account growth and developments needed to meet customer needs.
  • Run sample simulations and present detailed results in areas of interest for potential users.
  • Actively participate in technical marketing efforts such as presentations, webinars, user conferences, trade shows, and customer visits.
  • Perform internal research, testing and validations to guide future developments and identify growth areas for CFD in the application area.
  • Provide advanced technical support services to existing users.
  • Develop and deliver user training workshops, technical webinars, and presentations.
  • Perform technical sales consultations with prospective users.
  • Regularly present at industry conferences and events.
  • Participate and represent Flow Science in professional associations, member of technical committees, and/or officer of local or national organization.

Required skills and experience

  • 5-10+ years of industry related worked experience that includes water and/or wastewater treatment facilities, collection systems, combined/sanitary sewer overflow, water transmission/distribution projects, and/or pump stations.
  • MS or PhD in Civil/Environmental Engineering or related engineering degree.
  • A strong background in fluid mechanics, open channel hydraulics and physical, chemical and biological processes in WWTP.
  • Experience and expertise in conventional or industry standard modeling techniques and software (Biowin, Sumo, SWMM, CAD, GIS, HEC-RAS, MIKE).
  • Experience in applying and developing 3D CFD models.
  • Strong written and verbal skills for communicating critical concepts.
  • Desire to learn and understand new and challenging concepts related to all water and environmental application areas.

Preferred skills and experience

  • Strong 3D CAD skills and GIS skills are highly desired.
  • Registration as a Professional Engineer is a plus.
  • Experience working in dams, spillways, rivers, and open channel systems is a plus.


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. Learn more about careers at Flow Science >

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► Flow Science Earns Family Friendly Business Award® at the Platinum Level
    4 Mar, 2021

Flow Science is recognized as a distinguished leader for its family friendly policies.

Santa Fe, NM, March 4, 2021 – Flow Science, Inc. has earned Platinum level distinction for its workplace policies by Family Friendly New Mexico, a statewide initiative developed to recognize employers that have adopted policies that give New Mexico businesses an advantage in recruiting and retaining the best employees. This is Flow Science’s fifth year in a row winning a Family Friendly New Mexico award.

“We are proud to receive Platinum level recognition as a family friendly employer in New Mexico. Flow Science continues to be a great place to work, offering best-in-class benefits and generous policies. In 2021, Flow Science added even more policies and benefits that support our employees in community volunteerism and charitable giving, which has taken us from Gold to Platinum level recognition,” said Aimee Abby, Flow Science’s HR Manager.

The Family Friendly New Mexico initiative offers training, support and resources to businesses on how to implement family friendly policies, provides recognition to businesses and organizations that offer their employees family friendly benefits, and acts as a resource for businesses and community leaders as they develop policies on issues such as paid family leave and childcare assistance.

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

Flow Science offers its employees medical, dental and vision insurance plans employer-paid at 90%, flexible spending accounts, life and disability insurance, retirement and financial planning assistance, a 401(k) plan with generous employer matching contributions, vacation and sick pay, maternity and paternity leave, commuter benefit, training and education, and a wellness benefit. A new program implemented in 2021 encourages employee volunteerism and offers a charitable matching program.

NM Family Friendly Platinum Award 2021

We are proud to receive Platinum level recognition as a family friendly employer in New Mexico. Flow Science continues to be a great place to work, offering best-in-class benefits and generous policies, said Aimee Abby, Flow Science’s HR Manager.

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 products in nations throughout the Americas, Europe, Australasia 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

► 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.


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. Learn more about careers at Flow Science.

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► 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. Learn more about careers at Flow Science >

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.

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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

► The Purpose is Communication, the Goal is a Decision
  12 Apr, 2021

Design, analysis, and testing are the fun parts of the job, but the best scientists and engineers go the extra mile to communicate their results clearly. When it comes time to present your work to others, it’s important to remember that the purpose of the presentation is clear communication, and the goal is (usually) to make or defend a decision. Today we’re going to highlight a few simple things that might improve the clarity and function of your plots and presentations.

This is the last blog in the series about making more effective plots and presentations. Our first three posts covered the importance of consistency, the value of tailoring your presentations for your audience, and what to plot and what not to plot.

Match Expectations & Conventions

Multiple thermal maps

Figure 1. Thermal maps for each different region of interest (volume, spray, and wall).

The first thing to remember is that for a given industry, discipline, or company, there are always going to be certain conventions and expectations. Adhering to these expected conventions will help your audience quickly understand what they are seeing. This might have to do with the orientation of the images, the layout of your plots, the definition of your axes, the colormap chosen for your contours, the units of your variables, or even the sequence of your slides. One classic example in the field of aerodynamics is to present the coefficient of pressure distributions with the Y-axis ‘flipped’. If you must deviate from your audience’s expectations make sure you have a good reason for it and take care to explain the discrepancies.

Figure 1 was created for an audience of combustion engineers who are expecting to see multiple thermal maps, one for each of the different regions of interest (volume, spray, and wall). And, of course, they want to see the temperatures in Kelvin!

Stick to The Point

Most people don’t need to be told not to include slides that are unrelated to the topic – but oftentimes that guideline is too permissive. During the design, analysis, or test you may have generated or collected a lot of data. You may have also completed validation studies, performed mesh sensitivity experiments, reviewed historical datasets, or any number of ancillary activities. Just because those activities contributed to the overall success of your project does not mean that they need to take up space in your slide deck. Try to pare down your presentation to show only the minimum amount of information that is necessary. If you’re concerned that your audience may ask questions that extend beyond the core of your presentation you can always include that information in your backup slides.

Line Plots & Scalar Data Are Your Friends

Drag polars

Figure 2. Drag polar at 3 separate Mach numbers.

Your post-processor shouldn’t function solely as a visualization tool, it’s also an analysis tool. What’s the difference?

Visualization turns raw data into pretty pictures (which certainly have their uses), but analysis refines the same raw data to make engineering decisions possible. All the isometric 3D contour maps in the world won’t give you a clear answer for how much drag your wing design produces at its cruise flight condition. And no, adding stream traces doesn’t change that. You plotted an isosurface of Mach number? That’s cool, but we still don’t know if the design meets the range requirements.

The point is simulation & test data are useful only if they help answer questions that provide guidance in engineering decision making. Line plots (for example, drag polars, resonance curves, distribution plots, etc.) can help make a complex dataset much easier to understand and act upon. Integrated quantities/scalar values are even easier to digest – and oftentimes these values play the biggest role in engineering decisions.

Figure 2 shows a drag polar at 3 separate Mach numbers. This simple line plot may not be the fanciest image ever produced – but drag polars are a staple of the aerodynamic design process and can inform a lot about the drag performance of your vehicle.

As always, these tips are intended to be general purpose. Conventions will vary widely depending on your discipline, and supporting plots will change based on the specific goals of your presentation. What should never change, no matter what, is your desire to make your presentations effective.

Stay Informed about Tecplot Products and Training

Tecplot 360 layouts, stylesheets, and scripts can help ensure consistency in your plots.

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The post The Purpose is Communication, the Goal is a Decision appeared first on Tecplot.

► Back to Basics: Tecplot 360
  24 Mar, 2021

It’s Back to Basics here at Tecplot. This first in a 3-part series is about Getting Started with Tecplot 360. In this webinar, you will learn tips, tricks, and best practices to help you work faster and more efficiently.

Here’s the webinar agenda [02:41], with timestamps!

  • Touring the Tecplot 360 user interface [04:00]
  • Loading and exploring the data [06:15]
  • Calculating new quantities [26:35]
  • Extracting data over time [34:08]
  • Exporting images and videos [38:11]
  • Q&A [42:18]

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Q&A From the Webinar

What is the difference between Tecplot 360 and Tecplot Focus?

Tecplot Focus is a scaled down version of Tecplot 360, which is primarily used for engineering plotting. The main differences are that Tecplot Focus has:

  • Fewer data loaders.
  • No CFD Analyze tools.
  • No Python automation.
  • 5 million data point limit.

Read the detailed differences in our Comparison PDF Tecplot 360 vs. Tecplot Focus.

Is there a free student plan available?

Please see the Academic Suite, or email for academic licensing options. You can also download a free trial.

Can a style file be saved for multiple frames?

A style file is specific to a frame and doesn’t reference the dataset. A layout allows you to save properties for multiple frames But also includes reference to a specific dataset.

How do I copy and paste the graphics into Word and PowerPoint documents?

To make a quick copy and paste into your presentation, click in a Tecplot 360 frame to select it, and use ctrl-c and ctrl-v to copy and paste your plot into Microsoft Word or PowerPoint.

The post Back to Basics: Tecplot 360 appeared first on Tecplot.

► NASA SLS Flight Condition Analyses Time Reduced from Days to Hours
  23 Mar, 2021
The NASA Space Launch System (SLS) is a key element of the Artemis program that will provide the capabilities required for manned deep-space exploration. As currently planned, this family of heavy-lift vehicles will enable exploration of the Moon and Mars. Three classes of the SLS configuration, each of which includes both a crew and cargo version, are shown in Fig. 1. The large propulsive force needed to carry the targeted payloads is provided by two solid rocket boosters side-mounted onto a centerbody that embodies four RS-25 engines. The payload, whether the Orion crew capsule or a cargo compartment, is mounted atop the centerbody.

A new parallel processing toolbox for PyTecplot has been developed … reducing the postprocessing time by a factor of 12 …

SLS Configurations

Figure 1. SLS Configurations

Analyzing Wide Range of SLS Flight Conditions

Aerodynamic support for the SLS requires the use of both wind-tunnel tests and computational simulations to develop aerodynamic databases across the flight mission profile, seen in Fig. 2. These data are generated for a range of flight regimes including launch, liftoff, ascent, and booster separation. Flight conditions for the SLS vary from low-speed conditions on or near the launchpad to supersonic speeds during ascent. Because of this wide range of flight conditions, numerous tools are required to accurately capture the properties of the complex flowfields that evolve over time. While experimental results are useful and necessary, computational simulations yield results at flight conditions not easily tested in a wind tunnel facility, and these results include some fine-scale details that are not measurable in a wind tunnel.

SLS mission profile.

Figure 2: SLS mission profile.

In order to accurately capture the massively-separated flowfields that arise during launch of the vehicles, an unsteady CFD solver was utilized. These unsteady IDDES (improved delayed detached eddy simulation) CFD calculations yield a wealth of information that can be interrogated to provide visualizations of the evolving flowfield. As an example, a solution animation in which isosurfaces of constant Q criterion are colored by the magnitude of vorticity is shown in Fig. 3.
Figure 3: Animation showing isosurfaces of constant Q criterion flooded by the magnitude of vorticity.

Unfortunately, the data files from which the animation was extracted are extremely large, often being in excess of 10 terabytes of data, even when saving just a subset of the simulation data. Consequently, reduction of these data is time intensive from both human and computational perspectives, to the point where it is prohibitive to employ these techniques as an everyday tool for database-level analyses.

12x Reduction in Postprocessing Time with PyTecplot

In collaboration with Tecplot developers, a new parallel processing toolbox for PyTecplot has been developed and implemented to reduce the aforementioned datasets. Use of these new methods has reduced the postprocessing time by a factor of 12 relative to the baseline reduction methods. These newly-developed parallel data reduction routines reduced the time to make a movie, such as this one, from days to hours, thus enabling analyses across a much wider range of flight conditions.

More information on Artemis and SLS can be found on the NASA website.

Learn more about PyTecplot.

The post NASA SLS Flight Condition Analyses Time Reduced from Days to Hours appeared first on Tecplot.

► Plot This, Not That – Visual Communication
  17 Mar, 2021

Welcome back for our third post in a blog series about making more effective plots and presentations. Our first two posts covered the importance of consistency and the value of tailoring your presentations for your audience. This time we’re keeping it very simple by discussing a few common pitfalls with plot styling and formatting, and how avoiding them can improve your plot game.

XY-Line Plots

Let’s start off with the simplest of them all – line plots. If you have an engineering or science degree you have made dozens, perhaps hundreds, of these simple plots. When you’re making them for your own reference, you might not pay much heed to styling and formatting. But when it comes time to communicate your results to outside parties they become almost as essential as the analysis itself.

How many lines is too many? Well, that’s a tough one to answer because it depends on so many variables. Rather than make up an arbitrary rule of thumb for not overcrowding your XY-line plots – we thought it best to simply discuss how to make the lines stand out in a way that makes sense. First, let’s look at the sample dataset in Figure 1.

Bad Line Plot

Figure 1. XY-Line Plot – hard to read.

Rainfall data for three cities is plotted over time: Seattle, Dallas, and Miami. Each city has three samples. This results in a total of nine lines on the plot. As you can see in Figure 1, it is not easy to quickly differentiate between the various lines. Although each line is a different color, there is insufficient contrast between them. In addition, although we know there are subsets of data for each city, it is not easy to pick out the related lines quickly.

Line and Marker Styling

Now, look at Figure 2 and see how much easier it is to digest.

XY-Line Plots - differentiated lines

Figure 2. XY-Line Plot – with differentiated lines.

No matter how it’s styled it’s still a lot of data to digest, but the improvement between Figures 1 and 2 is obvious. In Figure 2 we have not reduced the dimensionality of our plot, but we have added line and marker styling to make distinguishing each dataset easier. We have also matched the line color per the city-type. This makes it easy to see which series are related to one another. The moral of the lesson is – be sure to use all the plot formatting tools at your disposal to provide meaningful visual distinction between your datasets. This trick can be applied to much more than just XY line plots – try it on bar charts, scatter plots, and more.

Contour Plots

Whether you think Contours are just pretty colors meant to satisfy managers or a valuable tool for aiding engineering decision making is up to you. But if you’re going to use them, at least use them correctly. We’ll look at two separate issues related to contour plots. The first up: contour levels. Look at Figure 3.

Contour Levels - bad example

Figure 3. Contour plot – pretty worthless, right?

Pretty worthless, right? Well forgive us if we are using extreme examples to prove our point, but Figure 3 obviously doesn’t tell us much. Contour plots are designed to show gradients of a field variable along a surface or plane. And to that end, you must set the contour levels at an interval that reveals the gradient. Depending on your dataset, you might wish to use a linear or an exponential distribution. To improve Figure 3 on our sample dataset, we’ll choose the latter.

Contour Plots - showing gradients

Figure 4. Contour plot showing exponential distribution.

Figure 4 shows the same dataset but uses an exponential distribution to reveal the change in turbulent frequency. Depending on your dataset, you may wish to continue with a linear distribution and ensure your max, min, and interval values are adjusted to highlight the gradient.

Colorblind-friendly Colormaps

Figure 4 is already infinitely more useful than Figure 3, but it can be improved even further by applying another plot trick. Look at Figure 5.

Contour Plot - colorblind friendly colormap

Figure 5. Contour plot with colorblind friendly colormap

In Figure 5 we have the same dataset and the same contour levels as in Figure 4, but now we’ve applied a new colormap. Not only does using a colormap like this one provide a more intuitive sense of the field variable gradient, but it is also colorblind friendly. Colorblindness is not something you hear much about – but it’s a real issue that affects millions of people. Do your audience a favor and make your plots as colorblind friendly as you are able! There are web apps available that can help you evaluate your images.

Plot Zoom Level

Last, but not least, is ensuring that you are plotting your data with a field of view that is appropriate for your results. In this example, we’ll adjust the zoom level on plots showing flow over a cylinder. If the zoom is too far out, pertinent results are washed out by uninteresting far-field values. If the zoom is too close, it’s difficult to discern how the region of interest fits into the broader dataset. Figures 6 and 7 illustrate how NOT to set your zoom level.

Zoom level too far out

Figure 6. Zoom level is too far out.

Zoom level too far in

Figure 7. Zoom level is too far in.

Clearly, Figure 6 is zoomed out too far, and therefore gives us a view of the entirety of the far field. The result leaves it unclear what the analysis was of, let alone what the results were. Figure 7 has the opposite problem. The perspective is too close, which shows a clear view of the cylinder but very little information about how the flow develops.

Correct Zoom level

Figure 8. Shows us a zoom level that is just right.

Ahh, that’s better! With the zoom set just right Figure 8 shows us a clear view of the cylinder and a detailed view of the upstream and downstream flows. Of course “just right” may look very different depending on your dataset and what you want to communicate. but just make sure to pay attention to how well your audience can see the pertinent information!

Building visuals to effectively communicate complex data is a very broad discipline, so forgive us if our examples are a tad elementary. The truth is that compelling plot styling can be accomplished in a variety of ways – and only you are in the position to determine what’s right for your data and audience. You’ll be alright if you just make sure it’s easy for your audience to understand your visuals.

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The post Plot This, Not That – Visual Communication appeared first on Tecplot.

► Webinar: Visualizing Isosurfaces of Higher Order Elements
  25 Feb, 2021

This webinar welcomes Dr. Scott Imlay, Tecplot CTO. He will discuss his research on visualization of higher-order CFD results. CFD code developers are adopting higher-order finite-element CFD methods due to their potential to reduce computation cost while maintaining accuracy. These techniques have been an area of research for many years and are becoming more widely available in popular CFD codes. The webinar content is based on Scott’s technical presentation at AIAA SciTech 2021. Note that this is preliminary research on adding higher-order element visualization to Tecplot 360.​

Agenda (01:19)​

Higher Order CFD

  • What are higher-order elements​ (1:42)
  • Why use higher-order elements (3:30)​
  • Higher-Order Visualization Challenges (6:37)
  • Basis Functions (8:32)
  • Brick (Hexahedral) Sub-Division (12:06)
  • Tetrahedral Sub-Division (12:51)
  • Prism Sub-Division (13:32)
  • Pyramid Sub-Division (14:38)
  • Implementation (15:26)
  • Results and Future work (17:33)​
  • Q&A (23:02)

We are looking for partners to try our prototype add-on or to provide data for testing. If you are interested, please contact us! The best way is through our website contact form, or email

Online Resources

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Q&A From the Webinar

Is support planned for Tecplot file formats?

This is early research and we’ve not made definite plans for HOE support in the Tecplot file formats. We’re initially targeting reading CGNS format because it has an HOE specification. We don’t have definite plans for supporting HOE in Tecplot file formats yet. But we are looking at using the CGNS standard.

We are implementing this now as a Tecplot 360 add-on. The add-on is functional for showing the isosurfaces and the surface data. We are looking for research partners to collaborate with because we do believe that the industry will need HOE. You can contact me at

Is the assumption that we have iso-parametric nodal data?

To rephrase your question, is our assumption that we allow curved elements of the same order as the basis function for this position (which would be iso-parametric)? The answer is yes, but that isn’t the solution for the long run. Our goal is to not require iso-parametric. We know that some people are doing linear geometry and higher order basis functions for the solution. Sometimes they have higher order basis functions for the geometry than they have for the solution. So it can go either way and we would like to support that. In the add-on we do use iso-parametric. If you want to create a linear element, in this case, put your edge-center nodes and location to give you a linear element.

Would you comment on how your algorithm relates to one in GMSH (algorithm for plotting high order solutions)?

I’m not super familiar with how GMSH does it. But my understanding is that GMSH also uses a subdivision technique. And it does not do it selectively for the isosurfaces. I think I should leave it there because I don’t want to state that I know more about GMSH than I do. Here is a paper about GMSH.

Did you consider using a Bézier representation rather than a heuristic buffer approach to make sure not to exclude extrema that cannot be seen via the nodal data itself?

It is a very good idea. We’ve thought about it, but we have not gone any farther than thinking about it. It is true that if you were to use Bézier representation, then they wouldn’t be nodal values anymore, but they would be the points in space that are used to adjust the shape of a B spline, for instance. And the minimum and the maximum are at those points. And so that way we could guarantee it.

The customers we have talked to are not using that as their basis functions. And so we would have to find a way to convert from more common basis functions to that before. If any of you are using those Bezier (or Bernstein polynomial) basis functions for not just the geometry, but for your solution data, I would really like to hear from you and learn more about it. Please contact me at

How does the presence of higher-level (higher-order) elements affect interpolations onto a different grid in terms of fidelity and cost?

The add-on does not currently change the interpolations within Tecplot 360. If you were to interpolate to another grid, it wouldn’t take advantage of that at this time, but in the long run we certainly intend to do that.

In the future, the underlying basis functions would be utilized exactly. And your interpolation to any new nodes would be based on the higher-order basis functions. In terms of fidelity, that would mean it has the same fidelity as the higher-order solution.

The second part of your question is about cost. The cost is going to be higher per cell because it must solve of a nonlinear system of equations. If you have nonlinear geometry, it will have to solve a system of nonlinear equations to do the interpolation. But you have far fewer elements generally in a higher-order mesh than you do in a linear mesh. And so that means it’s quite possible that it would be cheaper overall than if you’re just going from a linear mesh to your new mesh.

How does Tecplot know the basis functions used for the higher-order solution? Does Tecplot expect solutions to be converted to a predefined format?

We support only the CGNS format, the quadratic or Lagrangian elements. If you are, for instance, converted into CGNS then it will have element type for each of the zones. And when we read that CGNS file, that’s how we know. The basis functions are effectively defined by the file format and element type.

The post Webinar: Visualizing Isosurfaces of Higher Order Elements appeared first on Tecplot.

► Know Your Audience: Visual Communication
  23 Feb, 2021

Welcome back for another installment of our blog series where we talk about ways to improve your visual communication in plots & presentations. Our first post examined the importance of consistency in your plots. Today we’re going to discuss how to tailor your plots and presentations based on your audience. Creating a presentation that delves just deep enough to give your audience context and confidence in your conclusions and recommendations is the key to keeping your audience engaged but not overwhelmed.

When you spend days, weeks, or even months creating a thorough test or simulation it can be tempting to showcase every aspect of your work. But depending on who you are presenting to, that approach may not work. There are endless ways to categorize and describe different audiences – and each one is unique to an extent. For the purposes of this blog we’ll explore three very broad categories:

  • Technical – for example, subject matter experts
  • Generally technical – technical management or experts in separate disciplines
  • Non-technical – business leaders, the general public, etc.

Each audience has different goals, background knowledge, and interest in the material. Even when discussing the exact same project or research you may wish to present different plots, takeaways, and recommendations. Let’s explore some examples of what this might look like.


When your audience is predominately folks who have an equal or greater knowledge of your discipline – it is worth taking the time to make sure they believe your results. To put it simply – technical audiences are interested in understanding the “how” for a set of analyses. For a simulation or test engineer this may take the form of presenting low-level details of how the simulation or test was set-up, what assumptions were made, and any possible sources of error.

In the world of CFD one might communicate the “how” by including an explanation of your solver settings (limiters, turbulence models, etc.), plots of any computational mesh sensitivity studies that were performed, and graph of your force & moment residuals to highlight how well your solver converged. After you have proved that your simulation or experiment was performed following best practices, you can continue to dive into the relevant results. Another great way to ensure your audience has confidence in your simulation results is to show a comparison to experimental data for a particular case, like in the example below:

CFD vs. Measured Results

When presenting simulation results it can be useful to present alongside empirical data, when available. You might not have test data for every point of interest – but showing agreement to experiment at a few key control points can give your audience greater confidence in your results. The image above shows that the chordwise pressure coefficient distributions for the simulation closely match the measurements from experiment at multiple spanwise locations.

Generally Technical

“Generally technical” is a very vague definition – so what this audience looks like will vary widely depending on your role and the other disciplines that you interface with. For purposes of this blog post though – we’ll assume that the data you want to present from a simulation or test has implications for one or more technical groups that are working on the same project. If the technical folks wanted to know “how”, the generally technical folks want to know “ What were your results?”

 If we look at the development of a gas turbine engine as an example – a CFD analysis by the turbine aero team might be important to the heat transfer team, and BOTH the CFD analysis and the thermal analysis might be important to the structures team for their finite element analysis. To take things a step further – the results of the finite element analysis may be very important to the service engineering department. As you are presenting your findings to adjacent teams you will want to avoid diving too deep into the nuances of your discipline and instead focus on presenting the assumptions & the results that are relevant to downstream activities. Look below for an example.

Wing Surface Forces-Moments Plot

The plot above shows the cartesian forces along the span of a trapezoidal wing. A loads specialist might use a similar plot to communicate to downstream engineers, such as those in the structures group. It also provides the integrated quantities of interest without going into too much detail on how the values were computed or validated.


For non-technical audiences it’s not about the detailed data or your assumptions – it’s about how the project, program, or business will be affected, usually in terms of cost or schedule, by what you’ve discovered. A non-technical audience may also be interested in the results of your study as it pertains to a future state projection or desired outcome. Non-technical audiences are generally less interested in the “how” or “what”, but instead in the “why” or “so-what” (why does this matter?).

 As the engineer or scientist, it is perfectly acceptable, expected even, for you to communicate some technical data in your presentation – but keep things high level and avoid using too much jargon or trade specific symbols & abbreviations. Were you on a project to reduce the weight of a component or system? Communicate what your results say about the weight reduction efforts in terms of performance to goal. Did you contribute to a preliminary design study by performing CFD analyses on the design candidates? Consider showing a pareto diagram that highlights the design point most likely to satisfy the customer requirements. You can always keep more detailed plots in your backup slides to address any specific questions.

The image below serves as an interesting example of how to use technical plots in a way that is meaningful to a non-technical audience.

Know Your Audience - Glacier

The image above shows a contour plot of ice-thickness data for a glacier. Perhaps, if juxtaposed with a plot of past measurements, or future predictions, this technical plot would serve as a valuable illustration of the dire impact of climate change on glacial melt. In the context of a broader presentation about climate change this could be a powerful way to communicate the “so-what” to a non-technical audience (I.e., “So, if climate change is not reversed, we will lose glaciers, a vital part of the ecosystem, within X number of years”).

Put It into Practice

At the end of the day, nobody is going to be able to understand your audience better than you. If you have the opportunity, reach out to members of your audience before and after your presentation to learn about what they are expecting to see, and get feedback on how well they felt the important information was communicated. Take note of any questions you are asked at the end of your presentation; they may help you to better prepare for the next time around.

Learning great presentation skills – both in the building of the plots and slides, and in the live presentation itself, is a life-long process that can always be improved. If you take the time to understand what data and visualizations will be most interesting to your audience, you will reap the benefits by becoming a more effective engineer. Stay tuned for future blog posts in this series on effective visual communication to learn more ways to improve.

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The post Know Your Audience: Visual Communication appeared first on Tecplot.

Schnitger Corporation, CAE Market top

► Siemens adds to IC verification offering with the acquisition of OneSpin
  16 Apr, 2021

Siemens adds to IC verification offering with the acquisition of OneSpin

There was one more deal yesterday, that I just couldn’t get to: Siemens is adding to its IC verification portfolio with the acquisition of OneSpin Solutions from Azini Capital. OneSpin, according to Siemens, is “a strong, fast-growing business [that provides] a broad portfolio of formal applications for assuring IC designs operate as intended under the most adverse environmental conditions across key growth markets”.

Siemens Digital Industries Software SVP, IC Verification, Ravi Subramanian said that OneSpin enables Siemens to offer “a broad portfolio of automated formal apps for key use-cases, including trust and security, safety, RISC-V and FPGA applications. The unique combination of Siemens’ existing formal products, together with OneSpin Solutions’ domain expertise, outstanding app portfolio and ‘apps-first’ mindset, can enable Siemens to provide customers with increased efficiency and confidence across the complete verification platform (simulation, formal, emulation and prototyping), leading to faster verification, automation and debug.”

OneSpin Solutions technology will be added to Siemens’ Xcelerator portfolio, as part of the EDA solutions.

Details of the acquisition were not announced, but it is expected to close before the end of June, 2021.

► Pointwise acquired by Cadence
  15 Apr, 2021

Pointwise acquired by Cadence

It’s getting hard to keep up! Just in, an email from Pointwise, the CAE meshing experts:

We are happy to report today that Pointwise, Inc. is being acquired by Cadence Design Systems, Inc. (Nasdaq: CDNS), a pivotal leader in Intelligent System Design, building upon more than 30 years of computational software expertise. .. By becoming a part of Cadence, we believe we can accelerate growth and technology innovation for our customers.

This is really interesting. Remember that Cadence just acquired Numeca, most known for its CFD, signaling that it has intentions in CAE beyond its core electromagnetics base. It now clearly plans to tackle CFD in many more markets, a point not lost on Pointwise:

Cadence is an ideal innovation partner to address the growing complexity that the CFD market demands. Combining Pointwise’s technology-leading portfolio with Cadence’s Intelligent System Design strategy, our customers will benefit from a more robust design and analysis ecosystem. Pointwise will enhance Cadence’s system analysis portfolio with computational fluid dynamics (CFD) mesh generation capabilities where accuracy, reliability, and predictability are paramount concerns resolved by high-fidelity characterization of fluids.

The terms of the deal weren’t disclosed. But congrats to John Chawner and the team at Pointwise!

► Autodesk to acquire Upchain PLM
  15 Apr, 2021

Autodesk to acquire Upchain PLM

Super quick: Autodesk just announced that it will acquire Upchain, maker of cloud-based PLM and PDM solutions. The acquisition price wasn’t disclosed but it appears that the companies expect the transaction to close by July 31, 2021.

In an email to industry analysts, Autodesk said this was a “significant investment on Autodesk’s path toward eliminating the inefficiencies, re-work and lost details that result from the numerous applications and platforms in use by today’s manufacturers”.

According to Crunchbase, Upchain launched in 2015, and has raised $7.4 million in funding since then.

Back to Autodesk’s email: “Why Upchain: Traditional design and manufacturing data management processes are inefficient, fraught with data loss and disconnected workflows. With this acquisition, Autodesk is positioned as the provider that will bridge the gap between data and process in manufacturing, creating better integration and collaboration from requirements gathering, through design and engineering, into production, including management of all the design, supply chain and BOM changes along the way.”

There’s a conference call at 11am ET and I’ll update if warranted.

Added at 10am ET: here’s a link to Autodesk’s FAQ about the deal.

► Bentley adds to its digital cities offering with INRO deal
  14 Apr, 2021

Bentley adds to its digital cities offering with INRO deal

Bentley Systems just announced that it has acquired INRO Software, a maker of transportation planning, traffic simulation, and mobility visualization solutions. You may recall that Bentley has already acquired and built a few pedestrian and traffic modeling solutions — this extends the reach of those acquisitions into building a comprehensive system-level model.

IINRO’s Emme planning system supports urban, regional, and national transportation engineers in forecasting demand. Dynameq is a vehicle-based traffic simulation platform that’s used in urban traffic planning. Last, CityPhi, provides data visualization and analytics for large mobility and geospatial datasets.

A long time ago, when I was at MIT, one of my dorm mates was a civil engineer-to-be, specializing in traffic planning. She went on to work for the New York City’s subway system. She spent hours, taking notes on the bus system, routes, passenger numbers, density on specific bus routes at specific times – software like INRO’s makes this more high tech, sure, but also leads to significant insights that can help tune the transportation system overall. A’s work would have been much more valuable if we had had a way of predicting commuter traffic, for example, and tied that into how long a particular bus route would take. Then tie that into how long a connecting bus should wait, and so on. Bentley plans to add just these capabilities for urban planning.

 Bentley plans to combine INRO’s traffic simulations with current assets such as CUBE (which models how demand is spread across a transportation system), Streetlytics (analytics on observed trips), LEGION (pedestrian modeling), and OpenRoads (civil engineering solutions).

Bentley says this combined solution can be used to create what it calls “mobility digital twins” — and it does, even if we think of it more simply as a model of the transportation infrastructure of a place. Not just trains, but trains+buses+private cars+Uber+pedestrians+cyclists+whatever comes next. The timing is perfect; as we’re all figuring out how to come back to city centers, planning the transportation options requires flexibility, access to current data, and the ability to make reasoned trade-offs. Too, even without a pandemic, how citizens navigate a city is increasingly part of the urban planning function; one Scandinavian city has a goal of making it possible to get from anywhere to anywhere within the city in 20 minutes or less. I live outside Boston — that’s a stretch goal for sure around here. But it is a consideration for people who could essentially live anywhere and work remotely.

Bentley SVP, digital cities, Bob Mankowski says that the INRO team “led the research of advanced multimodal network modeling methods which helped establish state-of-the-art mobility simulation and … is leading its software future in our mobility digital twin advancement. With the addition of INRO and its world-class team, Bentley Systems can even better accelerate cities and regions in going digital to ‘build back better’!” [Build Back Better is the US slogan for much of what’s being debated in our legislature, including the infrastructure bill.]

Financial details were not released, but we may learn more when Bentley announces Q1 earnings sometime soon.

► Siemens weighs in on 2020 and 2021 — and is optimistic
    8 Apr, 2021

Siemens weighs in on 2020 and 2021 — and is optimistic

Last month, I was fortunate enough to speak with Tony Hemmelgarn, CEO Siemens’ Digital Industries Software division. Mr. Hemmelgarn oversees much of what we think of as Siemens’ PLM business, with brands such as Teamcenter, NX, Simcenter, Tecnomatix, and Mentor (now rebranded as Siemens EDA), but also other offerings such as MindSphere and Mendix.

We hear so much from Siemens’ PLMish competitors during earnings season that I wanted to get Mr. Hemmelgarn’s take on the same topics, starting with what happened in 2020. Mr. Hemmelgarn told me that Siemens Digital Industries Software had a good 2020, that the business grew, even in a very tough year. He said that “Electronic Design Automation (EDA) grew substantially. Integrated circuits were a big part of that growth, but electrical systems, printed circuit boards (PCBs) all contributed. We did fairly well in automotive and aerospace, even in the slowdown, in part because of our abilities in electrical systems and wire harnesses”. (See here for my recap of what the parent Siemens AG recently said about how important the EDA business is in the overall picture.)

Mr. Hemmelgarn said several of the largest auto companies are developing their own integrated circuits and circuit boards (ICs & PCBs) as part of their push into autonomous operations — this lets them protect their intellectual property and certify safety. He explained that Siemens EDA’s “Pave 360 is a shift left in IC verification for autonomous. OEMs are using TASS with IC design to prove out the whole system before going to silicon.” TASS makes both advanced simulation software and has testing and certification facilities that let companies working in autonomous vehicles test out components, concepts, and complete designs.

Not surprisingly, said Mr. Hemmelgarn, this contributed to a solid year for its CAE offerings, jointly under the Simcenter umbrella. CAE, especially CFD, continues to grow. I asked about where and how; Mr. Hemmelgard said that 2020 saw both expansions in existing accounts and the addition of new users: “COVID led to more people using CFD in new ways. We saw people simulate air purification to eliminate 99% of viruses, and Airbus using STAR CCM+ to understand particle droplets. The US Air Force is researching contamination hotspots, and AEC firms use CFD to reduce airborne transmission” — as just a few examples. Some, like Airbus, are long-term customers who are using CFD in new ways, moving from simulating the flow of air over a place to the air inside. But many are new.

Success in CAE wasn’t limited to CFD. Mr. Hemmelgarn said that noise vibration and harshness (NVH) simulation is seeing new interest as battery-operated vehicles expose sounds that weren’t audible over combustion engines. Other Simcenter applications, like Amesim (the 0D/1D engine), continued to grow their user bases “because people were working remotely, having to do their jobs digitally” without access to labs and test benches.

But it’s not all CAE, either. Mr. Hemmelgarn said that Teamcenter, the PLM brand, continues to do very well and is now the US Air Force standard. The partnership with SAP around Teamcenter also continues to evolve as the companies co-develop solutions. As of our conversation, SAP has Teamcenter in its price book; Teamcenter X [the cloud offering] will be there soon. Next will be integrating more SAP and Teamcenter capabilities. As Mr. Hemmelgarn said, “so far, so good. We’re meeting with customers now and are excited about the prospects.”

What about 2021? Mr. Hemmelgarn said that many trends would continue: EDA won’t slow down; it will continue to expand as more and more objects are electrified and as products become increasingly complex. He believes PLM will see a pickup towards the end of the year, as the manufacturing industry overall ramps up. In general, Siemens Digital Industries Software is “planning on good growth,” partly driven by more interest in cloud solutions over the next 18 to 24 months.

That, of course, led to further exploration of customers’ interest in the cloud. Mr. Hemmelgarn said that it’s been slow in the “PLM space, but [we’re] getting a lot of interest in Teamcenter in the cloud for collaborations — authoring still slow.” Companies tell us that having Siemens manage Teamcenter is interesting and could be another route to greater adoption. Mr. Hemmelgarn said that the next 12 to 18 months would be critical as Siemens tweaks its business model to reach more SMB companies with Teamcenter X. “If we take it over for them, they get the full benefit … the cloud/SaaS model makes it easier for us to reach these customers. And the way we support SaaS customers is very different, and we’ve invested a lot in making that happen.”

And 2021 will also see even more expansion in CAE. Mr. Hemmelgarn sees companies engaging in “new thought processes for CAE on edge devices, such as executable digital twins, where a simulation model may be made of a robot that’s linked to the physical world via an edge device. The edge device then controls and operates the robot based on digital twins .” That’s exciting — we’ve had the pieces to do this for a while, but stripping down a CAE model to solve quickly enough to affect real-time operations could change how production lines are operated and maintained.

In fact, Mr. Hemmelgarn sees 2021 as a year in which Siemens will “bring operating technology (OT) and information technology (IT) together. We have lots of IT with all of the PLM applications and OT with all of Siemens’ automation capabilities. We can connect them with Mendix (Siemens’ rapid application development and integration platform) to bring it all together. Mendix’s low-code has been big with traditional customers for a while but is opening up in new companies as well. What’s interesting is when a CIO talks to us from the industrial side as well as looking at the business system — we can do both in Mendix”. But the convergence of IT and OT, for Siemens, is not limited to edge devices. Siemens is planning more integration across its automation business with PLM — and they’re already doing this with virtual commissioning.

Mr. Hemmelgarn believes that 2020 accelerated the move to greater digitalization. “Working remotely and keeping safe is hard to do if there aren’t a lot of digital processes. A comprehensive digital twin is essential if working from home. Larger companies are pretty much there already; SMBs are trying to ramp this up — they’re cautious with respect to what the next 12 months could bring, but the debate about digital couldn’t be more relevant.”

My take? Siemens’ huge software/hardware portfolio, combined with business model tweaks geared to simplifying how buyers transact with Siemens, will lead to growth among new types of customers and in new markets. Add in the big and important partnerships with SAP and IBM — 2021 is going to be fascinating. As Mr. Hemmelgarn said, the goal is exploring “how [we] leverage the things we’ve done well into more areas.”

► Shapr3D: Cool CAD on an iPad (and now, macOS too)
    6 Apr, 2021

Shapr3D: Cool CAD on an iPad (and now, macOS too)

You probably don’t know this about me, but I used to be really good at CAD. A long time ago, after the advent of the PC but before Bitcoin. I used to demo CAD in some of my product management roles. Still, stone age. Today I love talking to CAD people about what they do, along with the why and the how. So when the good folks at Shapr3D combined my love of CAD with my Apple fangirl-ness and sent me a spanking new iPad Pro, Apple Pencil, and an M-1 Apple MacBook Air on which to try Shapr3D, I couldn’t resist.

First, some backstory.

Late last year, Shapr3D’s Ron Close arranged for me to speak with Radek Fáborský at ŠKODA AUTO, the European car company. Mr. Fáborský works in the tooling department, with 1300 other people, preparing all of the tooling for welding, casting, and stamping production lines. Mr. Fáborský’s particular expertise is the design and commissioning of robots on welding lines.

These robots are guided by cameras and other optical instruments, another of Mr. Fáborský’s specialties. One area of expertise Mr. Fáborský doesn’t have is traditional CAD, yet he needs to sketch out how the robot welder moves, the bins it picks from, and the enclosure it works in, to design the optimal setup. Mr. Fáborský tells me he spends hours on the production floor, sitting next to the robot and watching how it acts in response to camera guidance. He’s really passionate about this: “Yes, we have webcams pointing at the pallet—so we could monitor remotely—but then we don’t have the feel or hear the sounds of what is going on.”

Combine the desire to be on the production floor with the need to create sketches of the pallet, grippers, and other parts of the system—or the whole system—and what technology do you use?

Mr. Fáborský (remember, not a CAD guy) found out about Shapr3D from the Apple App store and decided to give it a try. “Traditional CAD didn’t work for how I work — I must be surrounded by living things, pallets of parts, the robot, its camera, the computer that shows the point cloud generated by the camera … I need a living canvas in my hand, with a pencil, so that I can draw what I see and imagine. Digital paper isn’t enough; I need CAD but also to be looking at what’s going on, maybe even using Shapr features, like importing a picture, to start a design idea.”

Well, if Mr. Fáborský can do it, so can I.

The boxes arrived—there might have been a little dancing with excitement at unboxing—and I dove right into Shapr3D’s tutorials. Honestly, Shapr3D is so much easier to learn than any CAD I’ve ever used (but remember, it’s not my primary job anymore, so YMMV) and the touch/Apple Pencil interface on the iPad is incredibly intuitive.

Shapr3D uses direct modeling (as did my stone-age CAD, so I was very familiar with how it all works) to create geometry, and its user interface is fast and easy to navigate. One creates an outline shape, sweeps it, Booleans with others — a very natural way to create even complex shapes. And for Mr. Fáborský’s applications, which would be primarily rectilinear shapes, this seems perfectly suited. The user interface guides users to the next potential logical steps, using an adaptive and predictive scheme that the company says is perhaps 30% faster than traditional UIs for direct modeling.

Shapr3D also allows entry from the iPad’s keyboard and mouse, more like a traditional CAD setup. I didn’t try this since my interest was in trying to duplicate Mr. Fáborský’s environment. I understand why some users would want this—and the iPad is certainly as powerful as many laptops—but I find the idea of using the iPad / Apple Pencil combo fascinating.

My Shapr3D experience was terrific, but I was just playing around. Mr. Fáborský has a real job to do, so let’s let him finish the story.

His goal is to design the robot’s physical interfaces within its surrounding world—gripper, slides, special tools that are autonomous like an artificial human hand that stirs parts in a pallet—so he starts with a sketch of that entire setup. This enables him to imagine what type of robot makes the most sense, what to put around it, and how to organize its space.

He sketches in Shapr3D, exports a STEP file, and forwards that to engineers who use a more traditional desktop CAD tool to finalize his concepts into manufacturable models and drawings. (Since our conversation, Shapr3D has come out with a drawings capability, but it’s unlikely Mr. Fáborský would be doing that — it’s not part of his job.) The project is complete when Mr. Fáborský receives the gripper, pallet, or other gear and determines that it’s fit for purpose. The concept is then rolled out across the production lines.

Will Shapr3D replace traditional CAD at ŠKODA AUTO? No, or at least not yet. Mr. Fáborský and his managers see Shapr3D as a “superb CAD designing tool that does everything we need and can easily exchange ideas with the desktop solutions.” He is introducing Shapr3D around ŠKODA AUTO (and parent, Volkswagen) as a “brilliant, agile tool to communicate ideas to the 3D environment … Shapr on an iPad won’t replace everyday CAD — they need different hardware, with a keyboard, mouse, a huge screen. The iPad suits me because I’m a prototype developer, sketcher, on the production floor.”

Since my chat with Mr. Fáborský, Shapr3D came out on Apple’s new iMac M-1 chip hardware, so perhaps that perception will change. Mr. Fáborský is an iPad person in part because he’s rarely at his desk. To him, Shapr3D on the iPad is “fantastic, like drawing on canvas but creative, intuitive — I don’t want to go back to desktop solution.”

But many people are desktop users, and for them, Shapr3D running natively on the macOS platform extends the mouse+keyboard idea from the iPad to the full-on laptop/desktop experience. I didn’t play with this as much, but it works well and feels more like a traditional, direct modeling CAD desktop experience. Somewhat reimagined with a modern UI, but much more along the lines of what you might expect.

Shapr3D continues to evolve. Shapr3D CEO Istvan Csanady told me that he has an aggressive roadmap that will, in time, make Shapr3D a serious contender in traditional CAD, but with what the company says is a better design experience. It started as a concept design tool, but the ambition now is to add functionality that appeals to more traditional workflows — and the far larger potential user base that brings.

Mr. Csanady also said that his users are atypical in the staid CAD world. He told me that as millennials get into management positions and make strategic IT decisions, they value user experience and IT usability: “this generation is super-bullish on great user experiences, and demands easy-to-use business tools. And that includes CAD. You can purchase Shapr3D with a single click, are onboarded in one click, don’t need formal training to become productive. This changes the mindset for buying and using, and our quality experience keeps users with us.”

2020 saw 24 Shapr3D releases. 2021 will see early releases on the Windows platform, more drawing capability, continued rollout to large industrial clients, and a change in packaging and pricing to appeal to three diverse audiences: corporate/business customers, hobbyists, and free/trial licenses.

I found Shapr3D to be useful, intuitive, and much more enjoyable than I was expecting. The new hardware, to be sure, played a part. But the adaptive UI, the Apple Pencil interaction, and the ability to move around in the real world while working on a design, as in Mr. Fáborský’s experience, also played a big part.

FTC: Shapr3D provided loaner hardware and software licenses but did not compensate Schnitger Corp. for this blog post.

The image was provided by Shapr3D. My models were good but nowhere near this good.


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