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Acoustic levitation and optical tweezers both use waves — of sound and light, respectively — to trap and control particles. Water waves also have the power to move and capture objects, as shown in this award-winning poster from the 2019 Gallery of Fluid Motion. The central image shows a submerged disk, its position controlled by the arc-shaped wavemaker at work on the water’s surface. The complicated pattern of reflection and refraction of the waves we see on the surface draws the disk to a focal point and holds it there.
On the bottom right, a composite image shows the same effect in action on a submerged triangular disk driven by a straight wavemaker. As the waves pass over the object, they’re refracted, and that change in wave motion creates a flow that pulls the object along until it settles at the wave’s focus. (Image and research credit: A. Sherif and L. Ristroph)
Getting enough water in arid climates can be tough, but Western diamondback rattlesnakes have a secret weapon: their scales. During rain, sleet, and even snow, these rattlesnakes venture out of their dens to catch precipitation on their flattened backs, which they then sip off their scales.
Researchers found that impacting water droplets tend to bead up on rattlesnake scales, forming spherical drops that the snake can then drink. Compared to other desert-dwelling snakes, Western diamondbacks have a far more complicated microstructure to their scales, with labyrinthine microchannels that provide a sticky, hydrophobic surface for impacting drops. (Video and image credit: ACS; research credit: A. Phadnis et al.; via The Kid Should See This)
The splash of a drop impacting a surface depends on many factors — among them droplet speed and size, air pressure, and surface characteristics. In this award-winning video from the 2019 Gallery of Fluid Motion, we see how the geometry of a superhydrophobic surface can alter a splash.
When a drop falls on a protruding superhydrophobic surface, like the apex of a cone, it can be pierced from the inside, completely changing how the droplet rebounds and breaks up. The variations the video walks us through are all relatively simple, but resulting splashes may surprise you nevertheless. (Image and video credit: The Lutetium Project)
I love a good tongue-in-cheek physical analysis of superheroes. This estimate of the drag force experienced by Superman’s hair when outracing a plane or speeding bullet was done by Cornell students. According to their calculations, Superman’s hair (or his hair gel) must withstand nearly 80,000 Newtons of force. That’s a bit less than the typical force experienced by a restrained passenger in a car crash at highway speeds.
In grad school, my labmates and I held a spirited debate about the difference in drag Superman would experience when flying at hypersonic speeds depending on whether he had one or both arms extended in front of him. Sadly, we never found the chance to test our hypotheses in the wind tunnel. (Image and video credit: R. Geltman et al.)
Hydrogen bubbles rise off zinc submerged in hydrocholoric acid in this short film from the Beauty of Science team. In high-speed video, the rise of the bubbles is stately and mesmerizing. Notice how the smallest bubbles appear as perfect spheres; for them, surface tension is strong enough to maintain that spherical shape even against the viscous drag of their buoyant rise. Larger bubbles, formed from mergers both seen and unseen, have a harder time staying round. In them, surface tension must battle gravitational forces and drag from the surrounding fluid. (Image and video credit: Beauty of Science; via Laughing Squid)
It happens to the best of us: sometimes our beer gets shaken up during transit. One common reaction to this is to tap the side of the can repeatedly before opening, but a new scientific study shows that tapping doesn’t affect the volume of beer lost. Danish scientists tested over 1,000 cans of beer in randomized combinations of shaken, unshaken, tapped, and untapped, and observed no difference between tapped and untapped cans.
The foam-up upon opening takes place in shaken beer because carbon dioxide bubbles form in the pressurized beer, especially along defects in the wall where bubbles can nucleate. When the pressure is released, the carbon dioxide becomes supersaturated and comes out of solution, especially into the pre-formed bubbles, which rapidly grow and overflow. In theory, tapping could disturb those bubbles before opening, but in practice, it makes no difference. Your best bet? Give the beer time to settle before you open it. (Image credit: Q. Dombrowski; research credit: E. Sopina et al.; via Ars Technica)
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
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 Radiator
Simulation shows separate air and water streamline paths colored by temperature
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 Plesiosaur: Streamline VectorsIllustration only, not part of the study
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 Parvancorina: Forward directionIllustration only, not part of the study
One of nature's smallest aerodynamic specialists - insects - have provided a clue to more efficient and robust wind turbine design.
Dragonfly: Yellow-winged DarterLicense: CC BY-SA 2.5, André Karwath
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
-march=native -mtune=native
alias ofdevNative='source $HOME/OpenFOAM/OpenFOAM-dev/etc/bashrc WM_COMPILE_OPTION=OptNative'
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:
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: https://openfoam.org
\\ / A nd | Version: 6
\\/ M anipulation |
\*---------------------------------------------------------------------------*/
FoamFile
{
version 2.0;
format ascii;
class dictionary;
object blockMeshDict;
}
// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //
convertToMeters 1;
vertices
(
(-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
);
blocks
(
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)
);
edges
(
);
boundary
(
inlet
{
type patch;
faces
(
(0 8 12 4)
);
}
outlet
{
type patch;
faces
(
(3 7 15 11)
);
}
lowerWall
{
type wall;
faces
(
(0 1 9 8)
(1 2 10 9)
(2 3 11 10)
);
}
upperWall
{
type patch;
faces
(
(4 12 13 5)
(5 13 14 6)
(6 14 15 7)
);
}
frontAndBack
{
type empty;
faces
(
(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:
edges
(
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 
The following mesh is produced:
Hopefully this simple example will help some people looking to incorporate curved edges into their blockMeshing!
Cheers.
This offering is not approved or endorsed by OpenCFD Limited, producer and distributor of the OpenFOAM software via http://www.openfoam.com, and owner of theOPENFOAM® andOpenCFD® trademarks.
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 (https://curiosityfluids.com/2016/03/28/mach-1-5-flow-over-23-degree-wedge-rhocentralfoam/) as an example and produce some synthetic Schlieren and Shadowgraph images using the data.
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:
Once you’ve selected this, we then need to set the properties so that we are going to operate on the density field:
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:
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:
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:
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:
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!
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: https://curiosityfluids.com/2019/02/15/sutherlands-law/
The law given by:
It is also often simplified (as it is in OpenFOAM) to:
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 (
https://webbook.nist.gov/), 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) |
| 200 |
0.000012924 |
| 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):
![\nabla^2\left[\right] = \nabla \cdot \nabla \left[\right]](https://s0.wp.com/latex.php?latex=%5Cnabla%5E2%5Cleft%5B%5Cright%5D++%3D+%5Cnabla+%5Ccdot+%5Cnabla+%5Cleft%5B%5Cright%5D&bg=ffffff&fg=000000&s=0 "\nabla^2\left[\right] = \nabla \cdot \nabla \left[\right]")
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fitNow we define the sutherland function:
def sutherland(T, As, Ts):
return As*T**(3/2)/(Ts+T)Next we input the data:
T=[200,
400,
600,
800,
1000,
1200,
1400,
1600,
1800,
2000]
mu=[0.000012924,
0.000022217,
0.000029602,
0.000035932,
0.000041597,
0.000046812,
0.000051704,
0.000056357,
0.000060829,
0.000065162]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)
As=popt[0]
Ts=popt[1]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')
xplot=np.linspace(200,2000,100)
yplot=sutherland(xplot,As,Ts)
plt.plot(T,mu,'ok',xplot,yplot,'-r')
plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])
plt.show()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,
800,
1000,
1200,
1400,
1600,
1800,
2000]
mu=[0.000012924,
0.000022217,
0.000029602,
0.000035932,
0.000041597,
0.000046812,
0.000051704,
0.000056357,
0.000060829,
0.000065162]
popt, pcov = curve_fit(sutherland, T, mu)
As=popt[0]
Ts=popt[1]
print('As = '+str(popt[0])+'\n')
print('Ts = '+str(popt[1])+'\n')
xplot=np.linspace(200,2000,100)
yplot=sutherland(xplot,As,Ts)
plt.plot(T,mu,'ok',xplot,yplot,'-r')
plt.xlabel('Temperature (K)')
plt.ylabel('Dynamic Viscosity (Pa.s)')
plt.legend(['NIST Data', 'Sutherland'])
plt.show()
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.
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:
http://www.wolfdynamics.com/wiki/OFtipsandtricks.pdf
(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:
https://holzmann-cfd.de/publications/mathematics-numerics-derivations-and-openfoam
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:
https://wiki.openfoam.com/%223_weeks%22_series
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 (
https://www.linkedin.com/feed/update/urn:li:groupPost:1920608-6518408864084299776/?commentUrn=urn%3Ali%3Acomment%3A%28groupPost%3A1920608-6518408864084299776%2C6518932944235610112%29&replyUrn=urn%3Ali%3Acomment%3A%28groupPost%3A1920608-6518408864084299776%2C6518956058403172352%29).
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 (https://curiosityfluids.com/2019/02/14/high-level-overview-of-meshing-for-openfoam/) 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 http://www.openfoam.com, and owner of theOPENFOAM® andOpenCFD® trade marks.
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:
https://github.com/curiosityFluids/curiosityFluidsAirfoilMesher
Here you will also find a template based on the airfoil2D OpenFOAM tutorial.
(1) Copy curiosityFluidsAirfoilMesher.py to the root directory of your simulation case.
(2) Copy your airfoil coordinates in Selig .dat format into the same folder location.
(3) Modify curiosityFluidsAirfoilMesher.py 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 curiosityFluidsAirfoilMesher.py
(5) If no errors – run blockMesh
PS
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.
Inputs:
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:
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:
Here is an example of a flying with airfoil (tested since the trailing edge is tilted upwards).
Inputs:
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 http://www.openfoam.com, and owner of the OPENFOAM® and OpenCFD® trademarks.
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 www.stfsol.com. 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 www.stfsol.com 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.
 
|
| p = 1 |
 
|
| p = 2 |
 
|
| p = 3 |
CL
| CD
| |
p = 1 | 2.020 | 0.293 |
p = 2 | 2.411 | 0.282 |
p = 3 | 2.413 | 0.283 |
Experiment | 2.479 | 0.252 |
Illini EV Concept Team Photo at Shell Eco Marathon 2018
Weight is the enemy of all teams that design electric cars for the Shell Eco Marathon.
Reducing the weight of electric cars improves the vehicle’s acceleration and power efficiency. These performance improvements make all the difference come race day.
However, if the car’s weight is reduced too much, it could lead to safety concerns.
Illini EV Concept (Illini) is a Shell Eco Marathon team out of the University of Illinois. Team members use ANSYS academic research software to optimize the chassis of their electric car without compromising safety.
Front bump composite failure under a load of 2000N.
The first hurdle of the Shell Eco Marathon is an initial efficiency contest. Only the best teams from this efficiency assessment even make it into the race.
Therefore, Illini concentrates on reducing the most weight in the shortest amount of time to ensure it makes it to the starting line.
Illini notes that its focus is on reducing the weight of its electric car’s chassis.
“The chassis is by far the heaviest component of our car, so ANSYS was used extensively to help design our first carbon fiber monocoque chassis,” says Richard Mauge, body and chassis leader for Illini.
“Several loading conditions were tested to ensure the chassis was stiff enough and the carbon fiber did not fail using the composite failure tool,” he adds.
Competition regulations ensure the safety of all team members. These regulations state that each team must prove that their car is safe under various conditions. Simulation is a great tool to prove a design is within safety tolerances.
“One of these tests included ensuring the bulkhead could withstand a 700 N load in all directions, per competition regulations,” says Mauge. If the teams’ electric car designs can’t survive this simulation come race day, then their cars are not racing.
Front bump deformation under a load of 2000N.
Simulations can do more than prove a design is safe. They can also help to optimize designs.
Illini uses what it learns from simulation to optimize the geometry of its electric car’s chassis.
The team found that its new designs have a torsional rigidity increase around 100 percent. This is after a 15 percent decrease in weight compared to last year’s model.
“Simulations ensure that the chassis is safe enough for our driver. It also proved that the chassis is lighter and stiffer than ever before. ANSYS composite analysis gave us the confidence to move forward with our radical chassis redesign,” notes Mauge.
The story optimization story continues from Illini. It plans to explore easier and more cost-effective ways to manufacture carbon fiber parts. For instance, the team wants to replace the core of its parts with foam and increase the number of bonded pieces.
If team members just go with their gut on these hunches, they could find themselves scratching their heads when something goes wrong. However, with simulations, the team makes better informed decisions about its redesigns and manufacturing process.
To get started with simulation, try our free student download. For student teams that need to solve in-depth problems, check out our software sponsorship program.
The post How to Increase the Acceleration and Efficiency of Electric Cars for the Shell Eco Marathon appeared first on ANSYS.
This engine intake simulation was post-processed using EnSight Enterprise. This allowed for the processing of a large data set to be shared among servers.
Simulation data sets have a funny habit of ballooning as engineers move through the development cycle. At some point, post-processing these data sets on a single machine becomes impractical.
Engineers can speed up post-processing by spatially or temporally decomposing large data sets so they can be post-processed across numerous servers.
The idea is to utilize the idle compute nodes you used to run the solver in parallel to now run the post-processing in parallel.
In ANSYS 19.2 Ensight Enterprise you can spatially or temporally decompose data sets. Ensignt Enterprise is an updated version of EnSight HPC.
EnSight is a client/server architecture. The client program takes care of the graphical user interface (GUI) and rendering operations, while the server program loads the data, creates parts, extracts features and calculates results.
If your model is too large to post-process on a single machine, you can utilize the spatial decomposed parallel operation to assign each spatial partition to its own EnSight Server. A good server-to-model ratio is one server for every 50 million elements.
Each EnSight Server can be located on a separate compute node on any compute resource you’d like. This allows engineers to utilize the memory and processing power of heterogeneous high-performance computing (HPC) resources for data set post-processing.
The engineers effectively split the large data set up into pieces with each piece assigned to its own compute resource. This dramatically increases the data set sizes you can load and process.
Once you have loaded the model into EnSight Enterprise, there are no additional changes to your workflow, experience or operations.
Keep in mind that this decomposition concept can also be applied to transient data sets. In this case, the dataset is split up temporally rather than spatially. In this scenario, each server receives its own set of time steps.
A turbulence simulation created using EnSight Enterprise post-processing
EnSight Enterprise offers performance gains when the server operations outweigh the communication and rendering time of each time step. Since it’s hard to predict network communication or rendering workloads, you can’t easily create a guiding principle for the server-to-model ratio.
However, you might want to use a few servers when your model has more than 10 million elements and over a hundred time steps. This will help keep the processing load of each server to a moderate level.
Another good tip to ensure you are post-processed optimally within EnSight Enterprise. Engineers achieve the best performance gains by pre-decomposing the data and locating it locally to the compute resources they anticipate using. Ideally, this data should be in EnSight Case format.
To learn more, check out Ensight or register for the webinar Analyze, Visualize and Communicate Your Simulation Data with ANSYS EnSight.
The post Post-Processing Large Simulation Data Sets Quickly Over Multiple Servers appeared first on ANSYS.
Your design team will make informed decisions about the products they create when they bring detailed simulations up front in the development cycle.
The 19.2 release of ANSYS Discovery AIM facilitates the need of early simulations.
It does this by streamlining templates for physics-aware meshing and rapid results.
Discovery AIM user interface with a solution fidelity slide bar (top left), area of interest marking tool (left, middle), manual mesh controls (bottom, center) and a switch to turn the mesh display on and off (right, top).
Analysts have likely told your design team about the importance of a quality mesh to achieve accurate simulation results.
Creating high quality meshes takes time and specialized training. Your design team doesn’t likely have the time or patience to learn this art.
To account for this, Discovery AIM automatically incorporates physics-aware meshing behind the scenes. In fact, your design team doesn’t even need to see the mesh creation process to complete the simulation.
This workflow employs several meshing best practices analysts typically use. The tool even accounts for areas that require mesh refinements based on the physics being assessed.
For instance, areas with a sliding contact gain a finer mesh so the sliding behavior can be accurately simulated. Additionally, areas near the walls of fluid-solid interfaces are also refined to ensure this interaction is properly captured. Physics-aware meshing ensures small features and areas of interests won’t get lost in your design team’s simulation.
The simplified meshing workflow also lets your design team choose their desired solution fidelity. This input will help the software balance the time the solver takes to compute results with the accuracy of the results.
Though physics-aware meshing can create the mesh under the hood of the simulation process, it still has tools allowing user-control of the mesh. This way, if your design team chooses to dig into the meshing details — or an analyst decides to step in — they can finely tune the mesh.
Capabilities like this further empower designers as techniques and knowledge traditionally known only by analysts are automated in an easy-to-use fashion.
The 19.2 release of Discovery AIM has seen improvements with its ability to enable your design team to explore simulation results.
Many analysts will know instinctively where to focus their post-processing, but without this experience, designers may miss areas of interest.
Discovery AIM enables the designer to interactively explore and identify these critical results. These initial results are rapidly displayed as contours, streamlines or field flow lines.
Field flow and streamlines for an electromagnetics simulation
Once your design team finds locations of interest within the results, they can create higher fidelity results to examine those area of interest in further detail. Designers can then save the results and revisit them when comparing design points or after changing simulation inputs.
To learn more about other changes to Discovery AIM — like the ability to directly access fluid results — watch the Discovery AIM 19.2 release recorded webinar or take it for a test drive.
The post Discovery AIM Offers Design Teams Rapid Results and Physics-Aware Meshing appeared first on ANSYS.
Traditional chemotherapy can often be blocked by a tumor’s stroma.
There are few illnesses as crafty as pancreatic cancer. It spreads like weeds and resists chemotherapy.
Pancreatic cancer is often asymptomatic, has a low survival rate and is often misdiagnosed as diabetes. And, this violent killer is almost always inoperable.
The pancreatic tumor’s resistance to chemotherapy comes from a shield of supporting connective tissue, or stroma, which it builds around itself.
Current treatments attempt to overcome this defense by increasing the dosage of intravenously administered chemotherapy. Sadly, this rarely works, and the high dosage is exceptionally hard on patients.
Nonetheless, doctors need a way to shrink these tumors so that they can surgically remove them without risking the numerous organs and vasculature around the pancreas.
“We say if you can’t get the drugs to the tumor from the blood, why not get it through the stroma directly?” asks William Daunch, CTO at Advanced Chemotherapy Technologies (ACT), an ANSYS Startup Program member. “We are developing a medical device that implants directly onto the pancreas. It passes drugs through the organ, across the stroma to the tumor using iontophoresis.”
By treating the tumor directly, doctors can theoretically shrink the tumor to an operable size with a smaller dose of chemotherapy. This should significantly reduce the effects of the drugs on the rest of the patient’s body.
Simplified diagram of the iontophoresis used by ACT’s chemotherapy medical device.
Most of the drugs used to treat pancreatic cancer are charged. This means they are affected by electromotive forces.
ACT has created a medical device that takes advantage of the medication’s charge to beat the stroma’s defenses using electrochemistry and iontophoresis.
The device contains a reservoir with an electrode. The reservoir connects to tubes that connect to an infusion pump. This setup ensures that the reservoir is continuously filled. If the reservoir is full, the dosage doesn’t change.
The tubes and wires are all connected into a port that is surgically implanted into the patient’s abdomen.
A diagram of ACT’s chemotherapy medical device.
The circuit is completed by a metal panel on the back of the patient.
“When the infusion pump runs, and electricity is applied, the electromotive forces push the medication into the stroma’s tissue without a needle. The medication can pass up to 10 to 15 mm into the stroma’s tissue in about an hour. This is enough to get through the stroma and into the tumor,” says Daunch.
“Lab tests show that the medical device was highly effective in treating human pancreatic cancer cells within mice,” added Daunch. “With conventional infusion therapy, the tumors grew 700 percent and with the device working on natural diffusion alone the tumors grew 200 percent. However, when running the device with iontophoresis, the tumor shrank 40 percent. This could turn an inoperable tumor into an operable one.” Subsequent testing of a scaled-up device in canines demonstrated depth of penetration and the low systemic toxicity required for a human device.
Daunch notes that the Food and Drug Administration (FDA) took notice of these results. ACT’s next steps are to develop a human clinical device and move onto to human safety trials.
Before these promising tests, ACT faced a few design challenges when coming up with their chemotherapy implant.
For example, “There was some electrolysis on the electrode in the reservoir. This created bubbles that would change the electrode’s impedance,” explains Daunch. “We needed a mechanism to sweep the bubbles from the surface.”
An added challenge is that ACT never knows exactly where doctors will place the device on the pancreas. As a result, the mechanism to sweep the bubbles needs to work from any orientation.
Simulations help ACT design their medical device so bubbles do not collect on the electrode.
“We used ANSYS Fluent and ANSYS Discovery Live to iterate a series of designs,” says Daunch. “Our design team modeled and validated our work very quickly. We also noticed that the bubbles didn’t need to leave the reservoir, just the electrode.”
“If we place the electrode on a protrusion in a bowl-shaped reservoir the bubbles move aside into a trough,” explains Daunch. “The fast fluid flow in the center of the electrode and the slower flow around it would push the bubbles off the electrode and keep them off until the bubbles floated to the top.”
As a result, the natural fluid flow within the redesigned reservoir was able to ensure the bubbles didn’t affect the electrode’s impedance.
To learn how your startup can use computational fluid dynamics (CFD) software to address your design challenges, please visit the ANSYS Startup Program.
The post Simulation Optimizes a Chemotherapy Implant to Treat Pancreatic Cancer appeared first on ANSYS.
The demand for wireless communications with high data transfer rates is growing.
Consumers want wireless 4K video streams, virtual reality, cloud backups and docking. However, it’s a challenge to offer these data transfer hogs wirelessly.
Peraso aims to overcome this challenge with their W120 WiGig chipset. This device offers multigigabit data transfers, is as small as a thumb-drive and plugs into a USB 3.0 port.
The chipset uses the Wi-Fi Alliance’s new wireless networking standard, WiGig.
This standard adds a 60 GHz communication band to the 2.4 and 5 GHz bands used by traditional Wi-Fi. The result is higher data rates, lower latency and dynamic session transferring with multiband devices.
In theory, the W120 WiGig chipset could run some of the heaviest data transfer hogs on the market without a cord. Peraso’s challenge is to design a way for the chipset to dissipate all the heat it generates.
Peraso uses the multiphysics capabilities within the ANSYS Electronics portfolio to predict the Joule heating and the subsequent heat flow effects of the W120 WiGig chipset. This information helps them iterate their designs to better dissipate the heat.
Systems designers know that asking for high-power transmitters in a compact and cost-effective enclosure translates into a thermal challenge. The W120 WiGig chipset is no different.
A cross section temperature map of the W120 WiGig chipset’s PCB. The map shows hot spots where air flow is constrained by narrow gaps between the PCB and enclosure.
The chipset includes active/passive components and two main chips that are mounted on a printed circuit board (PCB). The system reaches considerably high temperatures due to the Joule heating effect.
To dissipate this heat, design engineers include a large heat sink that connects only to the chips and a smaller one that connects only to the PCB. The system is also enclosed in a casing with limited openings.
Simulation of the air flow around the W120 WiGig chipset without an enclosure. Simulation was made using ANSYS Icepak.
Traditionally, optimizing this set up takes a lot of trial and error as measuring the air flow within the enclosure would be challenging.
Instead, Peraso uses ANSYS SIwave to simulate the Joule heating effects of the system. This heat map is transferred to ANSYS Icepak, which then simulates the current heat flow, orthotropic thermal conductivity, heat sources and other thermal effects.
This multiphysics simulation enables Peraso to predict the heat distribution and the temperature at every point of the W120 WiGig chipset.
From there, Peraso engineers iterate their designs until they reached their coolest setup.
This simulation led design tactic helps Peraso optimize their system until they reached a heat transfer balance they need. To learn how Peraso performed this iteration, read Cutting the Cords.
The post Making Wireless Multigigabit Data Transfer Reliable with Simulation appeared first on ANSYS.
There is only so much communication bandwidth available. This will make it difficult to handle the boost in cellular traffic expected from the 5G network using conventional cellular technologies.
In fact, cellular networks are already running out of bandwidth. This severely limits the number of users and data rates that can be accommodated by wireless systems.
One potential solution is to leverage beamforming antennas. These devices transmit different signals to different locations on the cellular network simultaneously over the same frequency.
Pivotal Commware is using ANSYS HFSS to design beamforming antennas for cellular base stations that are much more affordable than current technology.
A 28 GHz antenna for a cellular base station.
Traditionally, cellular technologies — 3G and 4G LTE — crammed more signals on the existing bandwidth by dividing the frequencies into small segments and splitting the signal time into smaller pulses.
The problem is, there is only so much you can do to chop up the bandwidth into segments.
Alternatively, Pivotal’s holographic beamforming (HBF) antennas are highly directional. This means they can split up the physical space a signal moves through.
This way, two cells in two locations can use the same frequency at the same time without interfering with each other.
Additionally, these HBF antennas use varactor (variable capacitors) and electronic components that are simpler and more affordable than existing beamforming antennas.
A parametric study of Pivotal’s HBF designs allowed them to look at a large portion of their design space and optimize for C-SWaP and roll-off. This study looks at roll-off as a function of degrees from the centerline of the antenna.
Antenna design companies — like Pivotal — are always looking to design devices that optimize cost, size, weight and power (C-SWaP) and performance.
So, how was Pivotal able to account for C-SWaP and performance so thoroughly?
Traditionally, this was done by building prototypes, finding flaws, creating new designs and integrating manually.
Meeting a product launch with an optimized product using this manual method is grueling.
Pivotal instead uses ANSYS HFSS to simulate their 5G antennas digitally. This allows them to assess their HBF antennas and iterate their designs faster using parametric studies.
For instance, Pivotal wants to optimize their design for performance characteristics like roll-off. To do so they can plug in the parameter values, run simulations with these values and see how each parameter affects roll-off.
By setting up parametric studies, Pivotal assess which parameters affect performance and C-SWaP the most. From there they could weigh different trade-offs until they settled on an optimized design that accounted for all the factors they studied.
To see how Pivotal set up their parametric studies and optimize their antenna designs, read 5G Antenna Technology for Smart Products.
The post Designing 5G Cellular Base Station Antennas Using Parametric Studies appeared first on ANSYS.
2019 proved to be an exciting and eventful year for Convergent Science. We released the highly anticipated major rewrite of our software, CONVERGE 3.0. Our United States, European, and Indian offices all saw significant increases in employee count. We have also continued to forge ahead in new application areas, strengthening our presence in the pump, compressor, biomedical, aerospace, and aftertreatment markets, and breaking into the oil and gas industry. Of course, we remain dedicated to simulating internal combustion engines and developing new tools and resources for the automotive community. In particular, we are expanding our repertoire to encompass batteries and electric motors in addition to conventional engines. Our team at Convergent Science continues to be enthusiastic about advancing simulation capabilities and providing unmatched customer support to empower our users to tackle hard CFD problems.
As I mentioned above, this year we released a major new version of our software, CONVERGE 3.0. We have frequently discussed 3.0 in the past few months, including in my recent blog post, so I’ll keep this brief. We set out to make our code more flexible, enable massive parallel scaling, and expand CONVERGE’s capabilities. The results have been remarkable. CONVERGE 3.0 scales with near-ideal efficiencies on thousands of cores, and the addition of inlaid meshes, new physical models, and enhanced chemistry capabilities have opened the door to new applications. Our team invested a lot of effort into making 3.0 a reality, and we’re very proud of what we’ve accomplished. Of course, now that CONVERGE 3.0 has been released, we can all start eagerly anticipating our next major release, CONVERGE 3.1.
2019 was a big year for the Computational Chemistry Consortium (C3). In July, the first annual face-to-face meeting took place at the Convergent Science World Headquarters in Madison, Wisconsin. Members of industry and researchers from the National University of Ireland Galway, Lawrence Livermore National Laboratory, RWTH Aachen University, and Politecnico di Milano came together to discuss the work done during the first year of the consortium and establish future research paths. The consortium is working on the C3 mechanism, a gasoline and diesel surrogate mechanism that includes NOx and PAH chemistry to model emissions. The first version of the mechanism was released this fall for use by C3 members, and the mechanism will be refined over the coming years. Our goal is to create the most accurate and consistent reaction mechanism for automotive fuels. Stay tuned for future updates!
Barcelona played host to this year’s European CONVERGE User Conference. CONVERGE users from across Europe gathered to share their recent work in CFD on topics including turbulent jet ignition, machine learning for design optimization, urea thermolysis, ammonia combustion in SI engines, and gas turbines. The conference also featured some exciting networking events—we spent an evening at the beautiful and historic Poble Espanyol and organized a kart race that pitted attendees against each other in a friendly competition.
This year we hosted our first-ever CONVERGE User Conference–India in Bangalore and Pune. The conference consisted of two events, each covering different application areas. The event in Bangalore focused on applications such as gas turbines, fluid-structure interaction, and rotating machinery. In Pune, the emphasis was on IC engines and aftertreatment modeling. We saw presentations from both companies and universities, including General Electric, Cummins, Caterpillar, and the Indian Institutes of Technology Bombay, Kanpur, and Madras. We had a great turnout for the conference, with more than 200 attendees across the two events.
The sixth annual CONVERGE User Conference–North America took place in New Orleans, Louisiana. Attendees came from industry, academic institutions, and national laboratories in the U.S. and around the globe. The technical presentations covered a wide variety of topics, including flame spray pyrolysis, rotating detonation engines, machine learning, pre-chamber ignition, blood pumps, and aerodynamic characterization of unmanned aerial systems. This year, we hosted a panel of CFD and HPC experts to discuss scaling CFD across thousands of processors; how to take advantage of clusters, supercomputers, and the cloud to run large-scale simulations; and how to post-process large datasets. For networking events, we took a dinner cruise down the Mississippi River and encouraged our guests to explore the vibrant city of New Orleans.
In 2019, we hosted the First CONVERGE Training Workshop and User Meeting at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Attendees came from KAUST and other Saudi Arabian universities and companies for two days of keynote presentations, hands-on CONVERGE tutorials, and networking opportunities. The workshop focused on leveraging CONVERGE for a variety of engineering applications, and running CONVERGE on local workstations, clusters, and Shaheen II, a world-class supercomputer located at KAUST.
We and our colleagues at Argonne National Laboratory and Aramco Research Center – Detroit received this year’s 2019 HPCwire Editors’ Choice Award in the category of Best Use of HPC in Automotive. We were incredibly honored to receive this award for our work using HPC and AI to quickly optimize the design of a clean, highly efficient gasoline compression ignition engine. Using CONVERGE, we tested thousands of engine design variations in parallel to improve fuel efficiency and reduce emissions. We ran the simulations in days, rather than months, on an IBM Blue Gene/Q supercomputer located at Argonne National Laboratory and employed machine learning to further reduce design time. After running the simulations, the best-performing engine design was built in the real world. The engine demonstrated a reduction in CO2 of up to 5%. Our work shows that pairing HPC and AI to rapidly optimize engine design has the potential to significantly advance clean technology for heavy-duty transportation.
2019 was a great year for CONVERGE and Convergent Science around the world. In the United States, we gained nearly 20 employees. We added a new Convergent Science office in Houston, Texas, to serve the oil and gas industry. In addition, we have continued to increase our market share in other areas, including automotive, gas turbine, and pumps and compressors.
In Europe, we had a record year for new license sales, up 70% from 2018. A number of new employees joined our European team, including new engineers, sales personnel, and office administrators. We attended and exhibited at tradeshows on a breadth of topics all over Europe, and we expanded our industry and university clientele.
Our Indian office celebrated its second anniversary in 2019. The employee count nearly doubled in size from 2018, with the addition of several new software developers and marketing and support engineers. The first Indian CONVERGE User Conference was a huge success–we had to increase the maximum number of registrants to accommodate everyone who wanted to attend. We have also grown our client base in the transportation sector, bringing new customers in the automotive industry on board.
In Asia, our partners at IDAJ continue to do a fantastic job supporting CONVERGE. CONVERGE sales significantly increased in 2019 compared to 2018. And at this year’s IDAJ CAE Solution Conference, speakers from major corporations presented CONVERGE results, including Toyota, Daihatsu, Mazda, and DENSO.
While we like to recognize the successes of the past year, we’re always looking toward the future. Computing technology is constantly evolving, and we are eager to keep advancing CONVERGE to make the most of the increased availability of computational resources. With the expanded functionality that CONVERGE 3.0 offers, we’re also looking forward to delving into untapped application areas and breaking into new markets. In the upcoming year, we are excited to form new collaborations and strengthen existing partnerships to promote innovation and keep CONVERGE on the cutting-edge of CFD software.
When Eric, Keith, and I first wrote CONVERGE back in 2001, we wrote it as a serial code. That probably sounds a little crazy, since practically all CFD simulations these days are run in parallel on multiple CPUs, but that’s how it started. We ended up taking our serial code and making it parallel, which is arguably not the best way to create a parallel code. As a side effect of writing the code this way, there were inherent parts of CONVERGE that did not scale well, both in terms of speed and memory. This wasn’t a real issue for our clients who were running engine simulations on relatively small numbers of cores. But as time wore on, our users started simulating many different applications beyond IC engines, and those simulating engines wanted to run finer meshes on more cores. At the same time, computing technology was evolving from systems with relatively few cores per node and relatively high memory per core to modern HPC clusters with more cores and nodes per system and relatively less memory per core. We knew at some point we would have to rewrite CONVERGE to take advantage of the advancements in computing technology.
We first conceived of CONVERGE 3.0 around five years ago. At that point, none of the limitations in the code were significantly affecting our clients, but we would get the occasional request that was simply not feasible in the current software. When we got those requests, we would categorize them as “3.0”—requests we deemed important, but would have to wait until we rewrote the code. After a few years, some of the constraints of the code started to become real limitations for our clients, so our developers got to work in earnest on CONVERGE 3.0. Much of the core framework and infrastructure was redesigned from the ground up in version 3.0, including a new mesh API, surface and grid manipulation tools, input and output file formats, and load balancing algorithms. The resulting code enables our users to run larger, faster, and more accurate simulations for a wider range of applications.
Scalability and Shared Memory
Two of our major goals in rewriting CONVERGE were to improve the scalability of the code and to reduce the memory requirements. Scaling in CONVERGE 2.x versions was limited in large part because of the parallelization method. In the 2.x versions, the simulation domain is partitioned using blocks coarser than the solution grid. This can cause a poor distribution of workload among processors if you have high levels of embedding or Adaptive Mesh Refinement (AMR). In 3.0, the solution grid is now partitioned directly, so you can achieve a good load balance even with very high levels of embedding and AMR. In addition, load balancing is now performed automatically instead of on a fixed schedule, so the case is well balanced throughout more of the run. With these changes, we’ve seen a dramatic improvement in scaling in 3.0, even on thousands of cores.
To reduce memory requirements, our developers moved to a shared memory strategy and removed redundancies that existed in previous versions of CONVERGE. For example, many data structures, like surface triangulation, that were stored once per core in the 2.x versions are now only stored once per compute node. Similarly, CONVERGE 3.0 no longer stores the entire grid connectivity on every core as was done in previous versions. The memory footprint in 3.0 is thus greatly reduced, and memory requirements also scale well into thousands of cores.
Inlaid Mesh
Apart from the codebase rewrite, another significant change we made was to incorporate inlaid meshes into CONVERGE. For years, users have been asking for the ability to add extrusion layers to boundaries, and it made sense to add this feature now. As many of you are probably aware, autonomous meshing is one of the hallmarks of our software. CONVERGE automatically generates an optimized Cartesian mesh at runtime and dynamically refines the mesh throughout the simulation using AMR. All of this remains the same in CONVERGE 3.0, and you can still use meshes exactly as they were in all previous versions of CONVERGE! However now we’ve added the option to create an inlaid mesh made up of cells of arbitrary shape, size, and orientation. The inlaid mesh can be extruded from a triangulated surface (e.g., a boundary layer) or it can be a shaped mesh away from a surface (e.g., a spray cone). For the remainder of the domain not covered by an inlaid mesh, CONVERGE uses our traditional Cartesian mesh technology.
Inlaid meshes are always optional, but in some cases they can provide accurate results with fewer cells compared to a traditional Cartesian mesh. In the example of a boundary layer, you can now refine the mesh in only the direction normal to the surface, instead of all three directions. You can also align an inlaid mesh with the direction of the flow, which wasn’t always possible when using a Cartesian mesh. This feature makes CONVERGE better suited for certain applications, like external aerodynamics, than it was previously.
Combustion and Chemistry
In CONVERGE 3.0, our developers have also enhanced and added to our combustion models and chemistry tools. For the SAGE detailed chemistry solver, we optimized the rate calculations, improved the procedure to assemble the sparse Jacobian matrix, and we introduced a new preconditioner. The result is significant speedup in the chemistry solver, especially for large reaction mechanisms (>150 species). If you thought our chemistry solver was fast before (and it was!), you will be amazed at the speed of the new version. In addition, 3.0 features two new combustion models. In most large eddy simulations (LES) of premixed flames, the cells are not fine enough to resolve the laminar flame thickness. The thickened flame model for LES allows you to increase the flame thickness without changing the laminar flamespeed. The second new model, the SAGE three-point PDF model, can be used to account for turbulence-chemistry interaction (more specifically, the commutation error) when modeling turbulent combusting flows with RANS.
On the chemistry tools side, we’ve added a number of new 0D chemical reactors, including variable volume with heat loss, well-stirred, plug flow, and 0D engine. The 1D laminar flamespeed solver has seen significant improvements in scalability and parallelization, and we have new table generation tools in CONVERGE Studio for tabulated kinetics of ignition (TKI), tabulated laminar flamespeed (TLF), and flamelet generated manifold (FGM).
CONVERGE Studio Updates
To streamline our users’ workflow, we have implemented several updates in CONVERGE Studio, CONVERGE’s graphical user interface (GUI). We partnered with Spatial to allow users to directly import CAD files into CONVERGE Studio 3.0, and triangulate the geometry on the fly in a way that’s optimized for CONVERGE. Additionally, Tecplot for CONVERGE, CONVERGE’s post-processing and visualization software, can now read CONVERGE output files directly, for a smoother workflow from start to finish.
CONVERGE 3.0 was a long time in the making, and we’re very excited about the new capabilities and opportunities this version offers our users. 3.0 is a big step towards CONVERGE being a flexible toolbox for solving any CFD problem.
[1] The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students, and collaborators from around the globe use advanced digital resources to address research grand challenges for the benefit of science and society. The NCSA Industry Program is the largest Industrial HPC outreach in the world, and it has been advancing one third of the Fortune 50® for more than 30 years by bringing industry, researchers, and students together to solve grand computational problems at rapid speed and scale. The CONVERGE simulations were run on NCSA’s Blue Waters supercomputer, which is one of the fastest supercomputers on a university campus. Blue Waters is supported by the National Science Foundation through awards ACI-0725070 and ACI-1238993.
As the 2019 CONVERGE User Conference in New Orleans approaches, I’ve been thinking about the past five years of CONVERGE events. Let me take you back to the first CONVERGE User Conference. It was September 2014 in Madison, Wisconsin, and I was one of the first speakers. I talked about two-phase flows and the spray modeling we were doing at Argonne National Laboratory. Many of the people in the audience didn’t know you could do the kinds of calculations in CONVERGE that we were doing. Take needle wobble, for example. At the time, people didn’t know that you could not only move the needle up and down, but you could actually simulate it wobbling. After my talk, we had many interesting discussions with the other attendees. We made connections with international companies that we otherwise would not have had the chance to meet, and we formed collaborations with some of those companies that are still ongoing today.
At Argonne National Laboratory, I lead a team of more than 20 researchers, all of them focused on simulating either piston engines or gas turbines using high-performance computing. Our goal is to improve the predictive capability of piston engine and gas turbine simulations, and we do a lot of our work using CONVERGE. We develop physics-based models that we couple with CONVERGE to gain deeper insights from our simulations.
We routinely attend and present our work at conferences like SAE World Congress and ASME, and what really sets the CONVERGE User Conference apart is the focus of the event—it’s dedicated towards the people doing simulation work with piston engines, gas turbines, and other real-world applications. The user conference is the go-to place where we can meet all of the people doing 3D CFD simulations, so it’s a fantastic networking opportunity. We get to speak to people from academia and industry and learn about their research needs—understand what their pain points are, what their bottlenecks are, where the physics is not predictive enough. Then we take that information back to Argonne, and it helps us focus our research.
Apart from the networking, the CONVERGE User Conference is also a great venue for presenting. My team has presented at the CONVERGE conferences on a wide variety of topics, including lean blow-out in gas turbine combustors, advanced ignition systems, co-optimization of engines and fuels, predicting cycle-to-cycle variation, machine learning for design optimizations, and modeling turbulent combustion in compression ignition and spark ignition engines. The attendees are engaged and highly technical, so you get direct, focused feedback on your work that can help you find solutions to challenges you may be encountering or give ideas for future studies.
The presenters themselves take the conference seriously. The quality of the presentations and the work presented is excellent. If you’ve never attended a CONVERGE User Conference before, my advice to you is to try to be a sponge. Bring your notebooks, bring your laptops, and take as many notes as you can. The amount of useful information you will gain from this conference is enormous and more relevant than other conferences you may attend, since this event is tailored for a specific audience. The CONVERGE User Conference also draws speakers from all over the world, which provides a unique opportunity to hear about the challenges that automotive original equipment manufacturers (OEMs), for example, face in other countries, which are different challenges than those in the United States. Listening to their presentations and getting access to those speakers has been very helpful for us. And since there are plenty of opportunities for networking, you can interact with the speakers at the conference and connect with them later on if you have further questions.
Overall, the CONVERGE User Conference is a great opportunity for presenting, learning, and networking. This is a conference where you will gain a lot of useful knowledge, meet many interesting people, and have some fun at the evening networking events. If you haven’t yet come to a CONVERGE User Conference—I highly recommend making this year your first.
Interested in learning more about the CONVERGE User Conference? Check out our website for details and registration!
On July 16th, I will look up at the night sky and celebrate the 50-year anniversary of the launch of Apollo 11. As I admire the full moon, the CFDer in me will think about the classic metaphor of the three-legged stool. Modern engineering efforts depend on theory, simulation, and experiment: Theory gives us basic understanding, simulation tells us how to apply this theoretical understanding to a practical problem, and experiment confirms that our applied understanding is in agreement with the physical world. One element does not seek to replace another; instead, each element reinforces the others. By modern standards, simulation did not exist in the 1960s—NASA’s primary “computers” were the women we saw in Hidden Figures, and humans are limited to relatively simple calculations. When NASA sent people to the moon, it had to build a modern cathedral balanced atop a two-legged stool.
I like the cathedral metaphor for the Saturn V rocket because it expresses some unexpected similarities between the efforts. A medieval cathedral was a huge, societal construction effort. It required workers from all walks of life to contribute above and beyond, not just in scale but in care and diligence. Designers had to go past what they fully understood, overcoming unknown engineering physics through sheer persistence. The end product was a unique and breathtaking expression of craftsmanship on a colossal scale.
In aerospace, we are habituated to assembly lines, but each Saturn V was a one-off. The Apollo program as a whole employed some 400,000 people, and the Saturn family of launch vehicles was a major slice of the pie. Though their tools were certainly more advanced than a medieval artisan’s, these workers essentially built this 363-foot-tall rocket by hand. They had to, because the rocket had to be perfect. The rocket had to be perfect because there was so little margin for error, because engineers were reaching so far beyond the existing limits of understanding. Huge rockets are not routine today, but I want to highlight a few design challenges of the Saturn V as places where modern simulation tools would have had a program-altering effect.
The mighty F-1 remains the largest single-chambered liquid-fueled rocket engine ever fired. All aspects of the design process were challenging, but devising a practical combustion chamber was particularly torturous. Large rocket engines are prone to a complex interaction between combustion dynamics and aeroacoustics. Pressure waves within the chamber can locally enhance the combustion rate, which in turn alters the flow within the engine. If these physical processes occur at the wrong rates, the entire system can become self-exciting and unstable. From a design standpoint, engineers must control engine stability through chamber shaping, fuel and oxidizer injector design, and internal baffling.
Without any way to simulate the fuel injection, mixing, combustion, and outflow, engineers were left with few approaches other than scaling, experimentation, and doggedness. They started with engines they knew and understood, then tried to vary them and enlarge them. They built a special 2D transparent thrust chamber, then applied high-speed photography to measure the unsteadiness of the combustion region. They literally set off tiny bombs within an operating engine, at a variety of locations, monitoring the internal pressure to see whether the blast waves decayed or were amplified. Eventually they produced a workable design for the F-1, but, in the words of program manager Werner von Braun:
…lack of suitable design criteria has forced the industry to adopt almost a completely empirical approach to injector and combustor development… [which] does not add to our understanding because a solution suitable for one engine system is usually not applicable to another…
It was being performed by engineers, but in some senses, it wasn’t quite engineering. Persistence paid off in the end, but F-1 combustion instability almost derailed the whole Apollo program.
Imagine if Rocketdyne engineers had had access to modern simulation tools! A tool like CONVERGE can simulate liquid fuel spray impingement directly, allowing an engineer to parametrically vary the geometry and spray parameters. A tool like CONVERGE can calculate the local combustion enhancement of impinging pressure fluctuations, allowing an engineer to introduce different baffle shapes and structures to measure their moderating effect. And the engineer can, in von Braun’s words, add to his or her understanding of how to combat combustion instability.
Fuel slosh in the colossal lower-stage tanks presented another design challenge. The first-stage liquid oxygen tank was 33 feet in diameter and about 60 feet long. How do you study slosh in such an immense tank while subjecting it to what you think will be flight-representative vibration and acceleration? What about the behavior of leftover propellant in zero gravity? In the 1960s, the answer was you built the rocket and flew it! In fact, the early Saturn launches (uncrewed, of course) featured video cameras to monitor fuel flow within the tanks. Cameras of that era recorded to film, and these cameras were housed in ejectable capsules. After collecting their several minutes of footage, the capsules would deploy from the spent stage and parachute to safety. I bet those engineers would have been over the moon if you had presented them with modern volume of fluid simulation tools.
Readers who have watched Apollo 13 may recall that the center engine of the Saturn V second stage failed during the launch. This was due to pogo, another combustion instability problem. In a rocket experiencing pogo, a momentary increase in thrust causes the rocket structure to flex, which (at the wrong frequency) can cause the fuel flow to surge, causing another self-exciting momentary increase in thrust. In severe cases, this vibration can destroy the vehicle. Designers added various standpipes and accumulators to de-tune the system, but this was only performed iteratively, flying a rocket to measure the effects. Today, we can study the fluid-structure interaction before we build the structure! Modern simulation tools are dramatic aids to the design process.
Today’s aerospace engineering community is doing some amazing things. SpaceX and Blue Origin are landing rockets on their tails. The United Launch Alliance has compiled a perfect operational record with the Delta IV and Atlas V. Companies like Rocket Lab and Firefly Aerospace are demonstrating that you don’t need to have the resources of a multinational conglomerate to put payloads into orbit. But for me, nothing may ever surpass the incredible feat of engineers battling physical processes they didn’t fully understand, flying people to the moon on a two-legged stool.
Interested in reading more about the Saturn V launch vehicle? I recommend starting with Dr. Roger Bilstein’s Stages to Saturn.
As I’ve started to cook more, I’ve learned the true value of multipurpose kitchen utensils and appliances. Especially living in an apartment with limited kitchen space, the fewer tools I need to make delicious meals, the better. A rice cooker that doubles as a slow cooker? Great. A blender that’s also a food processor? Sign me up. Not only do these tools prove to be more useful, but they’re also more economical.
The same principle applies beyond kitchen appliances. CONVERGE CFD software is well known for its flow solver, autonomous meshing, and fully coupled chemistry solver, but did you know that it also features an extensive suite of chemistry tools, with even more coming in version 3.0? Whether you need to speed up your abnormal combustion simulations, create and validate new chemical mechanisms, expedite your design process with 0D or 1D modeling, or compare your chemical kinetics experiments with simulated results, CONVERGE chemistry tools have you covered. The many capabilities of CONVERGE translate to a broadly applicable piece of software for CFD and beyond.
Zero-Dimensional Simulations
CONVERGE 3.0 expands on the previous versions’ 0D simulation capabilities with a host of new tools and reactors that are useful across a wide range of applications. If you’re running diesel engine simulations, you can take advantage of CONVERGE’s autoignition utility to quickly generate ignition delay data for different combinations of temperature, pressure, and equivalence ratio. Furthermore, you can couple the autoignition utility with 0D sensitivity analysis to determine which reactions and species are important for ignition or to determine the importance of various reactions in forming a given species.
The variable volume tool in CONVERGE 3.0 is a closed homogeneous reactor that can simulate a rapid compression machine (RCM). RCMs are ideal for chemical kinetics studies, especially for understanding autoignition chemistry as a function of temperature, pressure, and fuel/oxygen ratio.
Another new reactor model is the 0D engine tool, which can provide information on autoignition and engine knock. HCCI engines operate by compressing well-mixed fuel and oxidizer to the point of autoignition, and so you can use the 0D engine tool to gain valuable insight into your HCCI engine.
For other applications, look toward the well-stirred reactor (WSR) model coming in 3.0. The WSR assumes a high rate of mixing so that the output composition is identical to the composition inside the reactor. WSRs are thus useful for studying highly mixed IC engines, highly turbulent portions of non-premixed combustors, and ignition and extinction limits on residence time such as lean blow-out in gas turbines.
In addition to the new 0D reactor models, CONVERGE 3.0 will also feature new 0D tools. The chemical equilibrium (CEQ) solver calculates the concentration of species at equilibrium. The CEQ solver in CONVERGE, unlike many equilibrium solvers, is guaranteed to converge for any combination of gas species. The RON/MON estimator finds the research octane number (RON) and motor octane number (MON) for a fuel by finding the critical compression ratio (CCR) at which autoignition occurs and correlates this with the CCR of PRF fuel composition using the LLNL Gasoline Mechanism.
One-Dimensional Simulations
For 1D simulations, CONVERGE contains the 1D laminar premixed flame tool, which calculates the flamespeed of a combustion reaction using a freely propagating flame. You can use this tool to ensure your mechanisms yield reasonable flamespeeds for specific conditions and to generate laminar flamespeed tables that are needed for some combustion models, such as G-Equation, ECFM, and TFM. In CONVERGE 3.0, this solver has seen significant improvement in parallelization and scalability, as shown in Fig. 1. You can additionally perform 1D sensitivity analysis to determine how sensitive the flamespeed is to the various reactions and species in your mechanism.
CONVERGE 3.0 also includes a new 1D reactor model: the plug flow reactor (PFR). PFRs can be used to predict chemical kinetics behavior in continuous, flowing systems with cylindrical geometry. PFRs have commonly been applied to study both homogeneous and heterogeneous reactions, continuous production, and fast or high-temperature reactions.
Chemistry Tools
Zero- and one-dimensional simulation tools aren’t all CONVERGE has to offer. CONVERGE also features a number of tools for optimizing reaction mechanisms and interpreting your chemical kinetics simulation results.
Detailed chemistry calculations can be computationally expensive, but you can decrease computational time by reducing your chemical mechanism. CONVERGE’s mechanism reduction utility eliminates species and reactions that have the least effect on the simulation results, so you can reduce computational expense while maintaining your desired level of accuracy. In previous versions of CONVERGE, mechanism reduction was only available to target ignition delay. In CONVERGE 3.0, you can also target flamespeed, so you can ensure that your reduced mechanism maintains a similar flamespeed as the parent mechanism.
CONVERGE additionally offers a mechanism tuning utility to optimize reaction mechanisms. This tool prepares input files for running a genetic algorithm optimization using CONVERGE’s CONGO utility, so you can tune your mechanism to meet specified performance targets.
If you’re developing multi-component surrogate mechanisms, or you need to add additional pathways or NOx chemistry to a fuel mechanism, the mechanism merge tool is the one for you. This tool combines two reaction mechanisms into one and resolves any duplicate species or reactions along the way.
CONVERGE 3.0 will feature new table generation and visualization tools. With the tabulated kinetics of ignition (TKI) and tabulated laminar flamespeed (TLF) tools, you can generate ignition or flamespeed tables that are needed for certain combustion models. To visualize your results, you can run a CONVERGE utility to prepare your tables for visualization in Tecplot for CONVERGE or other visualization software.
CONVERGE’s suite of chemistry tools is just one of the components that make CONVERGE a robust, multipurpose solver. And just as multipurpose kitchen appliances have more uses during meal prep, CONVERGE’s chemistry capabilities mean our software has a broad scope of applications for not just CFD—but for all of your chemical kinetics simulation needs. Interested in learning more about CONVERGE or CONVERGE’s chemistry tools? Contact us today!
I recently attended an internal Convergent Science advanced training course on turbulence modeling. One of the audience members asked one of my favorite modeling questions, and I’m happy to share it here. It’s the sort of question I sometimes find myself asking tentatively, worried I might have missed something obvious. The question is this:
Reynolds-Averaged Navier Stokes (RANS) turbulence models and Large-Eddy Simulation (LES) turbulence models have very different behavior. LES will become a direct numerical simulation (DNS) in the limit of infinitesimally fine grid, and it shows a wide range of turbulent length scales. RANS does not become a DNS, no matter how fine we make the grid. Rather, it shows grid-convergent behavior (i.e., the simulation results stop changing with finer and finer grids), and it removes small-scale turbulent content.
If I look at a RANS model or an LES turbulence model, the transport equations look very similar mathematically. How does the flow ‘know’ which is which?
There’s a clever, physically intuitive answer to this question, which motivates the development of additional hybrid models. But first we have to do a little bit of math.
Both RANS and LES take the approach of decomposing a turbulent flow into a component to be resolved and a component to be modeled. Let’s define the Reynolds decomposition of a flow variable ϕ as
$$\phi = \bar \phi \; + \;\phi’,$$
where the overbar term represents a time/ensemble average and the prime term is the fluctuating term. This decomposition has the following properties:
$$\overline{\overline{\phi}} = \bar \phi \;\;{\rm{and}}\;\;\overline{\phi’} = 0.$$
LES uses a different approach, which is a spatial filter. The filtering decomposition of ϕ is defined as
$$\phi = \left\langle \phi \right\rangle + \;\phi ”,$$
where the term in the angled brackets is the filtered term and the double-prime term is the sub-grid term. In practice, this is often calculated using a box filter, a spatial average of everything inside, say, a single CFD cell. The spatial filter has different properties than the Reynolds decomposition,
$$\left\langle {\left\langle \phi \right\rangle } \right\rangle \ne \left\langle \phi \right\rangle \;\;{\rm{and}}\;\;\left\langle {\phi ”} \right\rangle \ne 0.$$
To derive RANS and LES turbulence models, we apply these decompositions to the Navier-Stokes equations. For simplicity, let’s consider only the incompressible momentum equation. The Reynolds-averaged momentum equation is written as
$$\frac{{\partial \overline {{u_i}} }}{{\partial t}} + \frac{{\partial \overline {{u_i}}\; \overline {{u_j}} }}{{\partial {x_j}}} = – \frac{1}{\rho }\frac{{\partial \overline P }}{{\partial {x_i}}} + \frac{1}{\rho }\frac{\partial }{{\partial {x_j}}}\left[ {\mu \left( {\frac{{\partial \overline {{u_i}} }}{{\partial {x_j}}} + \frac{{\partial \overline {{u_j}} }}{{\partial {x_i}}}} \right) – \frac{2}{3}\mu \frac{{\partial \overline {{u_k}} }}{{\partial {x_k}}}{\delta _{ij}}} \right] – \frac{1}{\rho }\frac{\partial }{{\partial {x_j}}}\left( {\rho \color{Red}{\overline {{{u’}_i}{{u’}_j}}} } \right).$$
This equation looks the same as the basic momentum transport equation, replacing each variable with the barred equivalent, with the exception of the term* in red. That’s where the RANS model will make a contribution.
The LES momentum equation, again neglecting Favre filtering, is written
$$\frac{{\partial \left\langle {{u_i}} \right\rangle }}{{\partial t}} + \frac{{\partial \left\langle {{u_i}} \right\rangle \left\langle {{u_j}} \right\rangle }}{{\partial {x_j}}} = – \frac{1}{\rho }\frac{{\partial \left\langle P \right\rangle }}{{\partial {x_i}}} + \frac{1}{\rho }\frac{{\partial \left\langle {{\sigma _{ij}}} \right\rangle }}{{\partial {x_j}}} – \frac{1}{\rho }\frac{\partial }{{\partial {x_j}}}\left( {\rho \color{Red}{\left\langle {{u_i}{u_j}} \right\rangle}} – \rho \left\langle {{u_i}} \right\rangle \left\langle {{u_j}} \right\rangle \right).$$
Once again, we have introduced a single unclosed term*, shown in red. As with RANS, this is where the LES model will exert its influence.
These terms are physically stress terms. In the RANS case, we call it the Reynolds stress.
$${\tau _{ij,RANS}} = – \rho \overline {{{u’}_i}{{u’}_j}}.$$
In the LES case, we define a sub-grid stress as follows:
$${\tau _{ij,LES}} = \rho \left( {\left\langle {{{u}_i}{{u}_j}} \right\rangle – \left\langle {{u_i}} \right\rangle \left\langle {{u_j}} \right\rangle } \right).$$
By convention, the same letter is used to denote these two subtly different terms. It’s common to apply one more assumption to both. Kolmogorov postulated that at sufficiently small scales, turbulence was statistically isotropic, with no preferential direction. He also postulated that turbulent motions were self-similar. The eddy viscosity approach invokes both concepts, treating
$${\tau _{ij,RANS}} = f\left( {{\mu _t},\overline V } \right)$$
and
$${\tau _{ij,LES}} = g\left( {{\mu _t},\overline V } \right),$$
where \(\overline V \) represents the vector of transported variables: mass, momentum, energy, and model-specific variables like turbulent kinetic energy. We have also introduced \({\mu _t}\), which we call the turbulent viscosity. Its effect is to dissipate kinetic energy in a similar fashion to molecular viscosity, hence the name.
If you skipped the math, here’s the takeaway. We have one unclosed term* each in the RANS and LES momentum equations, and in the eddy viscosity approach, we close it with what we call the turbulent viscosity \({\mu _t}\). Yet we know that RANS and LES have very different behavior. How does a CFD package like CONVERGE “know” whether that \({\mu _t}\) is supposed to behave like RANS or like LES? Of course the equations don’t “know”, and the solver doesn’t “know”. The behavior is constructed by the functional form of \({\mu _t}\).
How can the turbulent viscosity’s functional form construct its behavior? Dimensional analysis informs us what this term should look like. A dynamic viscosity has dimensions of density multiplied by length squared per time. If we’re looking to model the turbulent viscosity based on the flow physics, we should introduce dimensions of length and time. The key to the difference between RANS and LES behavior is in the way these dimensions are introduced.
Consider the standard k-ε model. It is a two-equation model, meaning it solves two additional transport equations. In this case, it transports turbulent kinetic energy (k) and the turbulent kinetic energy dissipation rate (ε). This model calculates the turbulent viscosity according to the local values of these two flow variables, along with density and a dimensionless model constant as
$${\mu _t} = {C_\mu }\rho \frac{{{k^2}}}{\varepsilon }.$$
Dimensionally, this makes sense. Turbulent kinetic energy is a specific energy with dimensions of length squared per time squared, and its dissipation rate has dimensions of length squared per time cubed. In a sufficiently well-resolved solution, all of these terms should limit to finite values, rather than limiting to zero or infinity. If so, the turbulent viscosity should limit to some finite value, and it does.
LES, in contrast, directly introduces units of length via the spatial filtering process. Consider the Smagorinsky model. This is a zero-equation model that calculates turbulent viscosity in a very different way. For the standard Smagorinsky model,
$${\mu _t} = \rho C_s^2{\Delta ^2}\sqrt {{S_{ij}}{S_{ij}}},$$
where \({C_s}\) is a dimensionless model constant, \({S_{ij}}\) is the filtered rate of strain tensor, and Δ is the grid spacing. Once again, the dimensions work out: density multiplied by length squared multiplied by inverse time. But what do the limits look like? The rate of strain is some physical quantity that will not limit to infinity. In the limit of infinitesimal grid size, the turbulent viscosity must limit to zero! The model becomes completely inactive, and the equations solved are the unfiltered Navier-Stokes equations. We are left with a direct numerical simulation.
When I was a first-year engineering student, discussion of dimensional analysis and limiting behaviors seemed pro forma and almost archaic. Real engineers in the real world just use computers to solve everything, don’t they? Yes and no. Even those of us in the computational analysis world can derive real understanding, and real predictive power, from considering the functional form of the terms in the equations we’re solving. It can even help us design models with behavior we can prescribe a priori.
Detached Eddy Simulation (DES) is a hybrid model, taking advantage of the similarity of functional forms of the turbulent viscosities in RANS and LES. DES adopts RANS-like behavior near the wall, where we know an LES can be very computationally expensive. DES adopts LES behavior far from the wall, where LES is more computationally tractable and unsteady turbulent motions are more often important.
The math behind this switching behavior is beyond the scope of a blog post. In effect, DES solves the Navier-Stokes equations with some effective \({\mu _{t,DES}}\) such that \({\mu _{t,DES}} \approx {\mu _{t,RANS}}\) near the wall and \({\mu _{t,DES}} \approx {\mu _{t,LES}}\) far from the wall, with \({\mu _{t,RANS}}\) and \({\mu _{t,LES}}\) selected and tuned so that they are compatible in the transition region. Our understanding of the derivation and characteristics of the RANS and LES turbulence models allows us to hybridize them into something new.
*This term is a symmetric second-order tensor, so it has six scalar components. In some approaches (e.g., Reynolds Stress models), we might transport these terms separately, but the eddy viscosity approach treats this unknown tensor as a scalar times a known tensor.
Santa Fe, NM, December 12, 2019 — In conjunction with its 40th anniversary, Flow Science, Inc. will hold the FLOW-3D World Users Conference 2020 on June 8-10, 2020 at the Maritim Hotel in Munich, Germany. Customers from around the world have been invited to the FLOW-3D World Users Conference 2020 to celebrate Flow Science’s milestone anniversary. Co-hosted by Flow Science Deutschland, this year’s conference features metal casting and water & environmental application tracks, advanced training sessions, in-depth technical customer presentations, and the latest product developments presented by Flow Science’s senior technical staff. Attendees will also enjoy a tour of the BMW Museum as part of the conference’s social events.
Flow Science has confirmed that Hubert Lang of BMW will be this year’s keynote speaker. Hubert Lang has worked in BMW’s Light Metal Foundry in Landshut, Germany since 1998. Introduced to FLOW-3D’s metal casting capabilities in 2005, Lang has led the expansion of BMW’s use of FLOW-3D. Today BMW uses FLOW-3D for a wide range of metal casting processes and special projects.
This year’s conference is particularly special. Not only are we celebrating our 40th anniversary with our customers around the world, but we are very pleased to welcome our keynote speaker Hubert Lang to honor our 15 years of partnership with BMW. Hubert will showcase some of BMW’s innovative designs for which FLOW-3D has played an indispensable role over the years. Since starting out as pioneers in computational fluid dynamics (CFD) 40 years ago, we continue to develop cutting edge software to enable customers like BMW to solve the toughest CFD problems around the world, said Dr. Amir Isfahani, CEO of Flow Science.
The call for abstracts is now open. Customers are encouraged to share their experiences, present their success stories, case studies and validations, and obtain valuable feedback from their peers and Flow Science staff. Topics include but are not limited to: metal casting, additive manufacturing, civil & municipal hydraulics, micro/nano/bio fluidics, aerospace and automotive applications. The deadline to submit an abstract is Friday, April 17.
Advanced training sessions for FLOW-3D’s family of products will be offered as part of the conference. Taught by senior technical staff and experts in their fields, advanced training topics include version up seminars for FLOW-3D CAST and FLOW-3D AM users, as well as sessions focused on troubleshooting techniques and municipal applications using FLOW-3D. Detailed information about these training sessions is available on the training page.
Online registration for the conference is now available.
In conjunction with the FLOW-3D World Users Conference 2020, advanced training sessions will be held the afternoon of June 8 at the conference hotel. Taught by senior technical staff and experts in their fields, advanced training topics include version up seminars for FLOW-3D CAST and FLOW-3D AM users, as well as sessions focused on troubleshooting techniques and municipal applications using FLOW-3D. The courses are designed so that everyone, regardless of their application, can participate in the troubleshooting session. You can sign up for multiple training sessions when you register online.
Instructor: Dr.-Ing. Dipl.-Phys. Matthias Todte, Flow Science Germany
This one-hour FLOW-3D CAST training course will begin with an introductory overview and a review of the new features and GUI design changes in FLOW-3D CAST v5.1. Through examples, new workspaces will be covered in detail, including Investment Casting, Continuous, Sand Core Making, Centrifugal as well as the new Exothermic Sleeve capabilities and database. We will also discuss the new solidification model available in FLOW-3D CAST v5.1.
Date: Monday, June 8
Time: 13:00 – 14:00
Cost: 100 €
Instructor: Raed Marwan, President, Flow Science Japan
This one-hour course is open to FLOW-3D AM users as well as those interested in exploring the powerful capabilities of FLOW-3D AM for simulating additive manufacturing and laser welding processes. An overview of the additive manufacturing processes that can be simulated using FLOW-3D AM will be briefly introduced at the beginning of the training. The training will then focus on how to set up simulations for Selective Laser Melting (SLM) processes. The training will cover the powder laying process for single and multi-layer beds, powder spreading, and powder melting.
Date: Monday, June 8
Time: 14:00 – 15:00
Cost: 100 €
Instructor: Brian Fox, MSc, Senior Water & Environmental Applications Engineer, Flow Science
CFD is rapidly gaining use as an advanced tool for the design and analysis of municipal systems for stormwater conveyance and water/wastewater treatment. FLOW-3D’s well-known strengths in free surface simulation provide excellent capabilities for simulating the complex flows encountered in stormwater conveyance structures. Our multiphysics capabilities offer a powerful tool for linking the physical, chemical and biological processes that are critical for the design and analysis of water/wastewater treatment systems.
In this two-hour training session we will explore FLOW-3D’s current capabilities along with recent and proposed developments for municipal applications. This class will be divided into four segments:
Attendees will leave the training with in-depth knowledge of FLOW-3D’s modeling capabilities for municipal applications. For users interested in expanding their service offerings, this is an excellent opportunity to learn about capabilities for this exciting and fast growing market.
Date: Monday, June 8
Time: 13:00 – 15:00
Cost: 200 €
This two hour training is intended for all users of FLOW-3D products, regardless of application.
Instructor: Brian Fox, Senior Water & Environmental Applications Engineer, Flow Science
Understanding how to identify and resolve simulation and setup issues is a critical skill for every serious CFD modeler. In this workshop we will discuss how to efficiently diagnose and address issues with FLOW-3D simulations to help keep projects moving forward on schedule. Beginning from troubleshooting techniques and the overall process, we will review the methods and practical tools available in FLOW-3D for identifying, investigating and diagnosing problems simulation errors. We will then proceed to discuss model setup options that can be used to address these issues. Throughout the class, we will apply the approach to interactively troubleshoot several real simulations to demonstrate the troubleshooting process and techniques that will help you work more efficiently on your own simulations. Finally, we will describe how to use the ideas from this training in a preventive manner in your workflow.
Date: Monday, June 8
Time: 15:00 – 17:00
Cost: 200 €
Registration and training fees are waived for conference speakers (one per presentation).
The HPC-enabled FLOW-3D v12.0 takes full advantage of the most advanced hardware available with pricing options available for entry-level through enterprise scale clients.
Santa Fe, NM, November 13, 2019 — Flow Science, Inc. has announced that it has released the HPC-enabled FLOW-3D v12.0. The HPC version of FLOW-3D v12.0 can be run on in-house clusters or on the FLOW-3D CLOUD software-a-as-a-service platform, which provides high performance computing as well as the lowest cost entry point to FLOW-3D.
Existing HPC customers and IT admins will benefit from one-time cluster hardware configuration setup, improved support for multiple job schedulers, and a simplified interface for setting up simulation on any compatible HPC platform.
Our HPC-enabled products in tandem with our cloud platform, which boasts the latest and the greatest hardware allow us to provide our customers the tools they need, when they need them, in order to accelerate their R&D and stay ahead of their competitors. Whether you are running FLOW-3D on a single core or 1000s of CPU cores, FLOW-3D is engineered to take full advantage of the ongoing advancements in hardware, said Flow Science CEO, Amir Isfahani.
FLOW-3D v12.0 marks an important milestone in the design and functionality of the graphical user interface, which simplifies model setup and improves user workflows. A state-of-the-art Immersed Boundary Method brings greater accuracy to FLOW-3D v12.0’s solutions. Featured developments include the Sludge Settling Model, the 2-Fluid 2-Temperature Model, and the Steady State Accelerator, which allows users to model their free surface flows even faster. The HPC-enabled version of FLOW-3D v12.0 allows customers to access these advanced simulation options at an accelerated pace. Performance benchmarks of the HPC-enabled version of FLOW-3D v12.0 are available.
From running design variations simultaneously to solving fine-resolution, large, and highly-complex design scenarios that take weeks to run on a high-end workstation, our HPC-enabled products get you the answer you need as quickly as possible on your in-house cluster or on our cloud platform, added Amir Isfahani.
A live webinar will provide an overview of high performance computing and FLOW-3D CLOUD with an emphasis on deploying hardware and software resources on demand, such as performance benchmarks for understanding scaling and speed-up on the cloud. The webinar will take place on December 11, 2019 at 1:00 pm EST. Online registration is available.
Flow Science has made this new release available to customers who are currently under maintenance contracts.
We invite our customers from around the world to join us at the FLOW-3D World Users Conference 2020 to celebrate 40 years of FLOW-3D.
The conference will be held on June 8-10, 2020 at the Maritim Hotel in Munich, Germany. Join engineers, researchers and scientists from some of the world’s most renowned companies and institutions to hone your simulation skills, explore new modeling approaches and learn about the latest software developments. This year’s conference features metal casting and water & environmental application tracks, advanced training sessions, in-depth technical presentations by our customers, and the latest product developments presented by Flow Science’s senior technical staff. The conference will be co-hosted by Flow Science Deutschland.
We are extremely pleased to announce that Hubert Lang of BMW will be this year’s keynote speaker.
Hubert Lang studied Mechanical Engineering with a focus on automotive engineering at Landshut University of Applied Sciences. In 1998, he started in BMW’s Light Metal Foundry in Landshut, working in their tool design department, where he oversaw the development of casting tools for six-cylinder engines. In 2005, Hubert moved to the foundry’s simulation department, where he was introduced to FLOW-3D’s metal casting capabilities. Since then, he has led considerable expansion in the use of FLOW-3D, both in the volume of simulations as well as the number of application areas.
Today, BMW uses FLOW-3D for sand casting, permanent mold gravity casting, low pressure die casting, high pressure die casting, and lost foam casting. FLOW-3D has also been applied to several special projects at BMW, such as supporting the development of an inorganic binder system for sand cores through the development of a core drying model; calculation of the heat input during coating of cylinder liners; the development of the casting geometry for the injector casting procedure; and the layout and dimensioning of cooling systems for casting tools.
We are pleased to offer a tour of the BMW Museum as part of the conference offerings. The tour will take place at 17:30 after the technical proceedings on Tuesday, June 9. You can sign up for the tour when you register for the conference.
Taught by senior technical staff and experts in their fields, advanced training topics include Version Up seminars for FLOW-3D CAST and FLOW-3D AM users, as well as sessions focused on Troubleshooting techniques and Municipal applications. The courses are designed so that everyone, regardless of their application, can participate in the Troubleshooting Session. You can sign up for these training sessions when you register online.
Abstracts should include a title, author(s) and a 200 word description. Please email your abstract to info@flow3d.com by Friday, April 17.
Registration and training fees will be waived for presenters.
Each presenter will have a 30 minute speaking slot, including Q & A. All presentations will be distributed to the conference attendees and on our website after the conference. A full paper is not required for this conference. Please contact us if you have any questions about presenting at the conference. Flow Science Deutschland will sponsor this year’s Best Presentation Award.
This year’s conference dinner will be held in the ever-popular Augustiner-Keller. All conference attendees and their guests are invited to join us on Tuesday, June 9 for a traditional German feast in a beautiful and famous beer garden. The conference dinner will take place following the BMW Tour.
Maritim Hotel Munich
+49 (0) 89 55235-0
info.mun@maritim.de
Our software, FLOW-3D, is used by engineers, designers, and scientists at top manufacturers and institutions throughout the world to simulate and optimize product designs. Many of the products used in our daily lives, from many of the components in an automobile to the paper towels we use to dry our hands, have actually been designed or improved through the use of FLOW-3D.
Flow Science has a job opportunity for a motivated, creative and collaborative Software Engineer. As a Software Engineer, you will use your object-oriented programming skills to create and maintain the user interface between our simulation software and the end user. You’ll have the opportunity to combine your creative skills with your analytical skills to contribute to a powerful tool used by customers around the world.
Flow Science offers an exceptional compensation and benefits package superior to those offered by most large companies. Flow Science employees receive a competitive base salary, employer paid medical, dental, vision coverage, life and disability insurances, relocation assistance, 401(k) retirement plan with extremely generous employer matching, and an outstanding incentive compensation plan that offers year-end bonus opportunity.
A resume and cover letter should be e-mailed to careers@flow3d.com.
Wally Rhines Chapter Twelve – The Future Mentor’s Questa verification tools now run on 64-bit ARM based servers Why EV Battery Design Is So Difficult SMTAI 2019: Nir Benson Discusses Mentor’s Challenges and Solutions Evolving to Meet the Challenges for Electronics Manufacturers Wally Rhines Chapter Twelve – The Future SemiWiki One of the great opportunities for the semiconductor industry is
Betting on Hydrogen-Powered Cars On-demand DRC within P&R cuts closure time in half for MaxLinear Functional Safety Verification For AV SoC Designs Accelerated With Advanced Tools Automating the pain out of clock domain crossing verification Mentor unpacks LVS and LVL issues around advanced packaging Betting on Hydrogen-Powered Cars SemiEngineering Hydrogen fuel cell vehicles are a compelling
Early pressure drop investigations and optimizations are crucial for today’s efficient designs. This short 3-minute video shows you how to conduct pressure drop characterizations such as determination of kv or cv coefficients in Simcenter FLOEFD, the leading frontloading CFD simulation solution for design engineers.
Frontloading CFD refers to the practice of moving CFD simulation early into the design process where proposed models can be quickly analyzed and improved. Simcenter FLOEFD is embedded in CAD thus making CFD “plug and play”. Due to key technologies at its core, Simcenter FLOEFD is easy to use, fast and accurate. In fact, design engineers using Simcenter FLOEFD have experienced x2 to x40 productivity improvement. While this video clearly features the FloEFD for Solid Edge interface, you can expect the same level of integration with other popular design programs including Siemens NX, PTC Creo, and CATIA V5.
Advanced integrated circuits (ICs) and system-on-chip (SoC) devices coupled with sophisticated software will be crucial enablers of advanced mobility technologies such as advanced driver assistance systems (ADAS), active safety, vehicle connectivity, and automated driving. Computing chips running sophisticated software already enable an immense amount of functionality in today’s vehicles. As vehicle
New enhancements to Simcenter Flotherm 2019.2 and Simcenter Flotherm XT 2019.3 electronics cooling software incl: new results analysis mode and post processing automation; Simcenter Flotherm Package Creator, PCB copper thermal modeling options, and more.
Guy Wagner, of Electronic Cooling Solutions Inc., details how Simcenter Flotherm XT CFD simulation software provides the capabilities to optimize the thermal management design of a liquid-cooled deep learning machine that uses neural networks.
Engineers are able to accurately model fluid flow in all micro channels and passages within a module to meet requirement for high heat dissipation and ensure that multiple ICs operate in appropriate temperature ranges. As a result, ECS are able to model product performance in detail using the exact complex CAD geometry that their clients prefer.
This blog on Tecplot Macro Tutorials was written from a webinar entitled Ask the Expert About Tecplot 360 hosted by Scott Fowler, Tecplot 360 Product Manager. We received numerous questions about Tecplot macros and this blog pulls our macro resources together in one place.
What is a Tecplot macro? A Tecplot macro is a set of instructions, called macro commands, that perform actions in Tecplot 360. Macro commands can be used to accomplish virtually any task that can be done via the Tecplot 360 interface, offering an easy way to automate Tecplot 360 processes.
We recommend that you download the macros, datasets and layout files used in the descriptions below. If you do not already have Tecplot 360 running, you can download a Free Trial.
Macro, Data and Layout Files (17MB ZIP)
This macro reads and edits different time steps and produces transient plots. This macro first finds out how many time steps are in your dataset, then it loops over those time steps. At each time step the current time step is set and an image is exported. That’s It!
export_over_time.mcr
$!EXTENDEDCOMMAND COMMANDPROCESSORID='extend time mcr' COMMAND='QUERY.NUMTIMESTEPS NUMTIMESTEPS'
$!LOOP |NUMTIMESTEPS|
$!EXTENDEDCOMMAND COMMANDPROCESSORID='extend time mcr' COMMAND='SET.CURTIMESTEP |LOOP|'
$!EXPORTSETUP EXPORTFORMAT = PNG
$!EXPORTSETUP EXPORTFNAME = "FILE_|LOOP|.png"
$!EXPORT
$!ENDLOOP
The Extend Time Macro add-on simplifies the macro interface by allowing you to use a simple loop to query the number of solution times in the dataset and advance the time step. This differs from the native Tecplot macro language as it does not require that you know the solution time of your data.
This add-on uses a different algorithm than Tecplot 360 EX for sorting the solution times. Because Tecplot 360 combines time steps that are sufficiently close together, the number of time steps reported by this add-on may differ from the number of time steps reported by Tecplot 360.
You can load this addon by adding the following line to your tecplot.add File. See chapters 31 – 3.5 and 31 – 1.2 in the Tecplot 360 User’s Manual.
$!LoadAddOn "tecutilscript_extendtime.mcr"
A question asked during the webinar was “How do I convert TEC files to binary?” The .tec file extension has been adopted by the greater Tecplot community. These files are usually ASCII, but in this case, the TEC file was a binary file. So, the question becomes “How do I convert from binary to ASCII?” Well, that’s easy!
Let’s backtrack for a moment and review the canonical Tecplot file extensions:
» Read the “Comparison of Tecplot Data File Formats” blog on Tecplot file types.
We don’t have a standalone utility to convert binary files to ASCII, but you can do it using the macro binary_to_ascii.mcr (also included in the ZIP file mentioned above). In the macro, you simply use the ReadDataSet command and then a WriteDataSet command.
From the command line you would call tec360.exe and pass this macro. Note that the file names in the macro need to be hard coded.
>tec360 -b -p binary_to_ascii.mcr
Another user wanted to convert ASCII files to binary.
You can use a utility called Preplot. Preplot is included with Tecplot 360 and it’s located in the bin directory in the Tecplot 360 installation. You just pass it an ASCII file (.dat) and tell it the output file name. Preplot will do the conversion for you.
> preplot myasciifile.dat mybinaryfile.plt
» Watch the video tutorial, Preplot and SZL Convert Tools.
» Read the Tecplot 360 User’s Manual.
» Reference the Data Format Guide.
A note about ASCII data, whenever Tecplot 360 loads an ASCII file we actually convert it to binary as it’s being loaded. Because of this, it is in your best interest, if you need to load that file over and over, to do the conversion only once. You will save yourself a lot of time!
During the webinar, one user was trying to create an arc of streamlines (which we call streamtraces in Tecplot 360). This is not possible using the onscreen “Rake” tool! The Rake tool only places straight lines of streamlines.
If you want to define an arc of streamlines, the best way to do it would be with PyTecplot (but that will be the topic of a future blog). I explain here how to do it with a Tecplot macro.
Here is the Tecplot macro for placing the streamtrace object associated with the frame. In the code below, I’m adding a streamtrace, defining it as a volume line, setting the direction to “both” (default is forward), and placing it at X= 0.5, Y=0.5, Z=0.5. You can create an arc by having multiple of these add commands at different locations.
$!STREAMTRACE ADD
NUMPTS = 1
STREAMTYPE = VolumeLine
STREAMDIRECTION = Both
STARTPOS { X = 0.5 Y = 0.5 Z = 0.5 }
How do I share data across frames without having to reload the data? In this example, I want to tile frames and then save the style from the upper left plot and load it into the lower right frame. You can do this, but not directly in the Tecplot 360 user interface. We will use a macro to do this (It can also be done with PyTecplot).
and draw a new frame). Then change the plot type to 2D Cartesian. (Now I could easily click on the upper left frame and save the frame style (Frame>Save Frame Style). Then, I could click in the new frame and load the frame style, but that will also copy the frame size and position which will overlay the original position. I want to retain the position. So, I don’t want to do it this way.)The magic is in the macro’s last command “INCLUDEFRAMESIZEANDPOSITION=NO,” which loads the frame style but ignores the position.
If you want these macro functions always available, you can put them in the tecplot.mcr file (located in Tecplot 360 installation directory).
If you are sharing the Tecplot 360 installation, for example in a Linux environment, you can put the “.tecplot.mcr” file in your Linux home directory so as not to impact other users on your network.
More information can be found in the Tecplot 360 User’s Manual (search for “tecplot.mcr”).
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In this Webinar, customers ask questions of Tecplot 360 Product Manager, Scott Fowler. Scott will help you develop your own internal expertise as he answers your questions interactively. You’ll learn new and improved techniques, current best practices and implementation considerations.
Topics include:
Download the files used in the webinar containing the presentation, data files, macros and Pytecplot scripts.
The post Ask the Expert about Tecplot 360 – Learn New Techniques! appeared first on Tecplot.
These questions were asked during the Webinar, From Zero to Hero: Tecplot 360 Basics. Tecplot 360 Product Manager, Scott Fowler, provides the answers below. Most of the answers show Webinar timestamps so that you can follow along in the Webinar. Learn more about Tecplot 360.
The steps taken in the Webinar (timestamp 36:55) can be recorded to a macro file. Macros are the legacy scripting language of Tecplot 360, and the language on which many of our file formats are based.
Tecplot 360 also has a Python API, called PyTecplot, which is available to customers on TecPLUS maintenance. Learn more about PyTecplot.
The cylinder in the Webinar example (timestamp 38:07) is a no-slip wall. Because it is a boundary condition, there are no velocities on the wall. If I want to see the velocities near the wall, I could change J planes to, for example, J=2.
Yes. That is a variable that can be computed. From the main menu (Webinar timestamp 38:46), select Analyze>Calculate Variables and click Select…, then choose Stagnation Energy. See Section 21-3.2 Identifying State Variables in the Tecplot 360 User’s Manual.
You can hide the border by editing the active frame. To edit a frame (Webinar timestamp 36:55), right-click on the edge of your frame, then choose Edit Active Frame. Uncheck Show border.
Some graphics cards will have a little drop shadow on the right and bottom of plots. This is graphics card dependent. If you see a drop shadow after turning off borders, please see this article on how to correct it in the Tecplot Knowledge Base.
When you use the menu command File>Save Layout As, there is a toggle (under the Save as type) that says Use relative paths (Webinar timestamp 36:55).
You can also edit the layout file because they are saved as human readable macro files. (timestamp 39:50). I’ll quickly open a macro file and you can see what the Tecplot macro language looks like.
Here is the layout that I saved earlier. You can see that I have a command to point to linedata.plt. To move this file to a different folder, I need to change the file path to an absolute path. I can update the path in the macro file.
Relative paths are often used by people doing optimization studies when they have a folder hierarchy with, for example, Mach alpha sweep and data within each sub folder. Using a relative path, they can copy a single layout into each subfolder and load that data by relative path.
Yes. Use Tools>Probe To Create Time Series Plot, then single click on the plot, and that will update the time series plot (Webinar timestamp 41:20). You can see that as I single click, the line plot updates through time.
For more on extracting, see the Tecplot 360 User’s Manual
Images cannot be cropped in the traditional sense. However, selecting File>Export from the main menu gives you some options (Webinar timestamp 41:53). You can export three different regions of your plot:
To export only a portion of your image, you can do what we call a paper zoom. Zoom in on your plot by holding down the shift key, middle mouse button while moving the mouse up. A work area export will export the zoomed-in plot you see in the work area.
In XY line plots you can plot up to five X-axes and up to five Y-axes (Webinar timestamp 41:20).
In this case we’ll use multiple Y-axes. In the line plot, go into the Mapping Style dialog from the Plot sidebar, and you will see that there are multiple Y axes. For this example, select RHO and Velocity Magnitude to plot in the same frame, then press CNTL+F to fit the view. You can see that these have two very different scales. In the Mapping Style dialog, set RHO to Y2, and now row is on the Y2 axis.
This will give you two plots in the same frame. Select the adjuster tool
to move the label.
.That will mimic adding LaTeX symbols to the legend.
Watch the Video on LaTeX Fonts
In 3D plots, zone effects (translucency and lighting) cause color variation (shading) throughout the zone(s). Shading can also help you discern the shape of the plot. To add shading to your plot, toggle-on Shade in the Plot sidebar. Use the Shade page of the Zone Style dialog to customize shading.
For information on translucency and lighting zone effects refer to Chapter 13, and for information on shade, refer to section 12-1 in the Tecplot 360 User’s Manual.
In XY line plots, navigate to the Data>Fourier Transform… menu option. Follow along in this video for a demonstration: Loading Excel Data and FFT in Tecplot 360.
The post 11 Questions About Tecplot 360 Basics appeared first on Tecplot.
Case study contributed by Michael Callaghan, PhD, P.Eng – Senior Applications Engineer, Aquanty
Aquanty is a leading-edge water resources science and technology firm specializing in predictive analytics, simulation and forecasting, research services, and IoT. Aquanty’s solutions and services are deployed globally across a broad range of industrial sectors including; agriculture, oil and gas, mining, watershed management, contaminant remediation, and nuclear storage and disposal. Aquanty’s flagship platform, HydroGeoSphere, is a class leader in fully integrated three-dimensional surface/subsurface modeling.
Figure 1. Hydrological Cycle.
HydroGeoSphere (HGS) is a three-dimensional control-volume finite element simulator which is designed to simulate the entire terrestrial portion of the hydrologic cycle. It uses a globally-implicit approach to simultaneously solve the 2D diffusive-wave equation and the 3D form of Richards’ equation.
The basis for HGS’ integrated computation is multiple 1D, 2D, 3D ‘domains’ that interact with each other, including:
Data intensive model output for the hydrologic cycle benefits from the flexibility of Tecplot 360 for visualization, which often requires plotting of multiple domains simultaneously and in 3D.
“We chose Tecplot 360 because of the quality of the plots. We need to present our results to clients, and for that plot quality there is no substitute to Tecplot 360.”
– Michael Callaghan, PhD, P.Eng, Senior Applications Engineer, Aquanty
Simulating groundwater-surface water interaction in complex topography such as hummocky terrain has traditionally been viewed as a significant challenge to the hydrologic modelling community, for example so-called fill and spill behavior.
However, with HGS, the complex processes by which water movement is influenced by the combination of surface topography and highly variable subsurface hydrostratigraphy or preferential flow pathways can be readily reproduced.
In the example shown in Figure 2, precipitation is falling on an upland area which then initiates overland flow, filling, and spilling of surface depressions. The Tecplot 360 animation illustrates depression-focused groundwater recharge occurring beneath the depressions, with both a perched water table and a fractured aquitard influencing sub-surface water movement.
HGS models may range across a number of scales from centimeters to meters to kilometers to 100’s of kilometers. Use of an unstructured finite element mesh makes this possible.
Tecplot 360’s inherent flexibility with unstructured grids makes it a very useful visualization tool across many scales of problems.
In this application, HydroGeoSphere is being used to recreate the Southern Alberta, Canada flooding that occurred in June 2013. The simulation results presented in Figure 3 depict a flood pulse derived from basin scale hydrologic simulations being routed across a local scale model of the City of Medicine Hat, Alberta, Canada. LiDAR-derived topography was used as input for this model, and results show excellent agreement between simulated and observed high water marks.
The above visualization cases are made possible with Tecplot 360. HGS is highly integrated with Tecplot ecosystem using a powerful post-processing tool to directly produce results in Tecplot file formats. Tecplot 360 is an essential tool for everyday 2D data plotting, high quality 3D model visualization, results inspection and evaluation.
Learn more about Aquanty and HydroGeoSphere »
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This tutorial is for using the Tecplot add-in for Excel. It is available for Windows and found in the \util folder of your Tecplot 360 installation.
To enable the add-in for Excel, run the file RunTecplot5.xla, and instruct Excel to enable macros. The tool will then be found under the add-ins tab of Excel.
This add-in will load cells from left to right and top to bottom, so make the selection of cells that you wish to load into Tecplot 360, start in the upper left and move to the lower right.
This first example is in table format with multiple dependent variables in one zone.
Once the cells have been selected, click Send to Tecplot. This add-in will then create a new ASCII file from the cell data that is then loaded into Tecplot 360. We can toggle on mappings for the other variables in the Mapping Style dialog to compare our data.
In another example in table format, we can load in multiple zones, where the breaks between cells indicate the separation between the zones. The Tecplot add-in for Excel also supports loading and cell data in carpet format. This will load in a 2D dataset from the cells and assign variables X, Y, and V.
This concludes the tutorial for the Tecplot add-in for Excel. Thank you for watching.
The post Tecplot Add-in for Excel appeared first on Tecplot.
Get started with Tecplot 360 while learning best practices for plotting and analyzing your data. This 30-minute Webinar will walk you through everything from loading your data to exporting images and animations.
The agenda includes (but won’t be limited to!):
The post From Zero to Hero: Tecplot 360 Basics appeared first on Tecplot.
I had a number of questions about the new / upgraded relationship between ANSYS and Aras that was announced last week and reached out to both companies for more details. ANSYS VP Strategy and Partnerships Sin Min Yap told me the following; his comments are preceded by his initials, my comments are below.
Why Aras? What were the most important reasons for ANSYS to go for an SPDM solution based on Aras PLM technology?
SMY: Both ANSYS and Aras have a vendor neutral and open approach, and customers have already validated this approach based on several ongoing engagements with ANSYS Minerva. It should be noted that ANSYS Minerva leverages the underlying Aras platform, and not the PLM offerings from Aras. For e.g. ANSYS Minerva leverages the core platform capabilities such as configuration management and PDM/PLM connectivity, and not Comet technologies or other PLM applications, from Aras.
MS: I didn’t realize that Minerva didn’t use the PLM offerings – I thought it was a customized-for-CAE implementation of Aras Innovator, excluding Comet. So this new arrangement is clearly an expansion.
Why now?
SMY: Our partnership and collaboration have been ongoing for ~2 years, with early customers being made aware of this under NDA. The first commercial release of ANSYS Minerva was only in 2019R3, and also without a significant marketing push. Now that we are doing a more focused messaging and launch of ANSYS Minerva as part of 2020R1, it was the right time to make the public announcement on our Aras partnership.
Is this real, with a serious investment in this platform, or is it PR?
SMY: Open ecosystem support is one of the pillars of the pervasive simulation strategy at ANSYS to connect simulation to the engineering processes at our customers. The release of ANSYS Minerva in 2019R3 (leveraging the Aras platform mentioned in the OEM agreement) demonstrates our commitment to provide solutions to support this digital transformation journey of our customers.
MS: Early days, but let’s take that as a commitment of real investment.
What happens to ANSYS Minerva, which I understand is “powered by Aras”? How will this go to market — via Aras’ app store or ANSYS’ sales teams or both or something new? Who supports what?
SMY: As mentioned above, this partnership announcement is directly related to ANSYS Minerva (and not a creation of something new or different), and it was just a matter of timing the PR with the first broad announcement of ANSYS Minerva is 2020R1. The go to market of ANSYS Minerva is only as an ANSYS product through S sales effort. ANSYS will support the customers directly for the ANSYS Minerva solution, and Aras and ANSYS will coordinate second line support to manage the OEM level interactions.
Do you have any similar plans regarding other PLM technology vendors?
SMY: We are not ready to share future plans at this time. We continue to engage very closely with Minerva customers and will respond based on their needs.
So there you have it. Mr. Yap was quite candid and I thank him for his help. I’ve also reached out to Aras but we keep missing one another — I look forward to learning more.
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I believe in science. In the null hypothesis. In finding the root cause. And science says we are not doing nearly enough to reverse the damage already done to our planet by our homes, factories, cars, ships and planes, and to prevent further climate change. So when I got a press release about a PLM-centric initiative to deal with the ecological crisis facing our planet, I had to share.
The PLM Green Global Alliance is in its infancy, currently made up of Richard McFall from PLM Alliances, Jos Voskuil from TacIT, Oleg Shilovitsky from Beyond PLM, and Bjorn Fidjeland from plmPartner and has a
mission to create a global connection and community between professionals who use, develop, market, or support Product Lifecycle Management (PLM) enabling technologies and software solutions that have value in addressing the causes and consequences of climate change due to human-generated greenhouse gas emissions.
PGGA wants to help the PLM community of developers, users, researchers, and academics to use PLM as intended to make products and processes more efficient (and, presumably, therefore using less power and other inputs). PGGA also intends to promote work on renewable and other sources of energy, power storage and transmission, lowering carbon emissions, and greening manufacturing, among other goals.
Why? Because, as PGGA founder (and ex-CIMdata member) Rich McFall comments,
“We face an urgent challenge to create a more sustainable and green future for our industries, economies, communities, and all life forms on our planet that depend on healthy interdependent ecosystems. In our informal alliance we seek to educate, advocate, and collaborate for greater recognition of the role of PLM to help assess, reduce, mitigate, and adapt to the effects of climate change now being experienced across the globe. There are many examples of how PLM-related technologies are doing just that, but few focused non-politicized platforms where resources and application case studies can be researched, shared, and promoted for the collective good. We plan to change that as our modest contribution.”
And they welcome other members: Joining the PGGA is available to anyone with an interest in the intersection of PLM and Green. There’s no website yet, but you can reach the PGGA at https://plmalliances.com/plm-green-alliance/.
It’s interesting (and likely not coincidental) that this comes at the same time as major investors like Blackrock are rethinking their strategy, saying they’ll shift away from environmentally risky projects to a greener stance. (They’ve been slammed by some for not doing this whole-heartedly enough, but any change in the right direction is good, in my view. But let’s be careful about green-washing, where misleading information makes something appear more environmentally sound than it really is.)
Climate change will be a huge topic at this week’s Davos World Economic Forum — in fact, Greta Thunberg has already chided the grownups for doing too little. Money talks and Blackrock’s toe-in-the-water makes it look like it’s starting to listen, too. We’ve all seen the impact consumers can have when we decide to ditch disposable plastic straws or use reusable water bottles. If we weight “green” options during product design, and explore less damaging material choices via, for example, simulation and other PLMish technologies, who knows what we can accomplish?
The title image is of smokestacks, photographer unknown. I got it using a Creative Commons license from https://garryrogers.com/.
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Last December I went to Amsterdam to attend Siemens’ Simcenter Symposium. It’s one of my favorite events because it brings together so many different … everythings: auto talks to aero, who talk to marine; STAR-CCM+ people learn about Amesim; and we all learned that pigeons can detect cancer (yep, a scientist was paid to figure that out).
In total, over 600 people attended the more than 150 sessions, on everything from CFD to EMAG to process changes needed to move simulation into the hands of more designers. That’s a huge increase in all ways that matter from just a year ago: more presentations, more attendees, more diversity of application … it’s hard to know why with any certainty but it would appear that Siemens’ intentions to create a portfolio multiplier is working, as more of its users take advantage of more of the tools. This used to be a CFD-weighted event (since it’s based on the old CD-adapco event) that’s now truly multi-physics, if I may be allowed to use the term in this way.
Many take-aways. Here are my top items:
The customer presentations were, as always, fascinating and highlight how much there is to learn about the world around us and the products we rely on every day. They all had a couple of things in common: a problem, some exploration and discovery and then a solution — that’s engineering, after all. But in many cases, the discovery led to new questions which led to new discoveries, which led to … and so on. Somewhat new for me was how many of the speakers mentioned using Teamcenter (Siemens’ data management and PLM solution) to drive these processes and manage the massive amounts of data generated.
This conference used to be a simulation guru experience (the early STAR-CCM+ events I attended were expert-fests). In 2019, it broadened to include new roles (designers, material specialists, test engineers and others) because they need to understand simulation –how it works, where it does and doesn’t apply, what the limitations might be for their use case– to make a bigger business impact. That was true again this year, as several presenters talked about using the automation tools across Simcenter to create advanced simulation tools for designers.
One last thing: One of the most interesting presentations had absolutely nothing to do with CAE. Dr. Hannah Fry, co-author of The Indisputable Existence of Santa Claus: The Mathematics of Christmas (actually a lot of fun; I read it on the flight home), talked about AI and its impact on humans. She covered whether pigeons can diagnose cancer (yes), that AI can go hideously wrong when applying algorithms to prison sentencing guidelines, and pointed out that humans write the algorithms and need to both plan for the worst and take responsibility for the outcomes. It was an interesting choice for Siemens, since many people wonder if they will lose their jobs to an algorithm. Dr. Fry’s examples make clear that some tasks can be done by pigeons or humans or algorithms — but not all. My take: humans can’t outsource the hard choices, at least for now.
The title shot is a view of one of Amsterdam’s many canals in an early morning fog. Gorgeous, no?
This is a video, produced by Statoil, of the Aker undersea compressor: https://www.youtube.com/watch?v=Ew1h9aU4odo . They don’t mention Aker, so here’s a video Aker has created about the subsea compressor system, hinting at the analytics at 1:30 or so: https://www.youtube.com/watch?v=ZzQhfIv3mgc . If I can find a public video of the analytics, I’ll post that — it’s brilliant.
Note: Siemens graciously covered some of the expenses associated with my participation in the event but did not in any way influence the content of this post.
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This just came into the inbox:
Aras Licenses Platform to ANSYS in Strategic OEM Deal
Partnership will enable better processes and data management of simulations for digital thread traceability across the lifecycle
You know Aras, the open source PLM platform, and you know ANSYS, the CAE powerhouse. What are they doing together? Aras is licensing the Aras Innovator platform to ANSYS so that ANSYS can use it to build configuration management, interoperability with PDM/PLM solutions, and “simulation-specific capabilities to … connect simulation and optimization to the business of engineering”.
Allrighty. What does THAT mean? It’s likely a recognition of the fact that Siemens, with Simcenter+Teamcenter, and Dassault Systemes, with SIMULIA on the V6 3DEXPERIENCE platform, offer something it cannot. Even though ANSYS has had a simulation lifecycle management tool for a decade now, modern innovation cycles need simulation to be governed by the processed and connections made possible by PLM. Simulation can help identify the perfect design, but it’s pointless if it can’t be economically manufactured and supported in the market. Connecting ANSYS’ broad CAE bench to a PLM like Aras lets its users tap into those workflows, ensuring consistent processes and traceability. And since Aras maintains APIs to lots of other platforms, an ANSYS user can get to all sorts of enterprise system via Aras Innovator.
According to the info made public today, ANSYS isn’t buying Aras. Right now, any way. ANSYS says it will deliver commercial offerings for simulation process and data management, process integration, design optimization and simulation-driven data science. on top of Aras Innovator.
Peter Schroer, CEO of Aras, says “We believe that simulation is essential to developing tomorrow’s next generation products, and that better data and process management of simulations is required to enable the digital processes of the future which will support these products. We see the ANSYS and Aras partnership as a potential game changer in connecting simulation to engineering processes for traceability, access and reuse across the product lifecycle.”
For its part, ANSYS’ Navin Budhiraja, VP of cloud and platform business said, “… this unique collaboration combines the strengths of ANSYS’ industry-leading multiphysics portfolio and the resilient platform from Aras for digital connectivity to dramatically enhance customer value … [W]e see the ability of ANSYS solutions to interoperate and link with heterogeneous systems as an important step to accelerate the digital transformation for our customers.”
So many questions: How much ANSYS is investing in this platform? What happens to ANSYS Minerva, the multiphysics collaboration application which carries the logo, “powered by Aras”? Is today’s announcement an outgrowth of a demonstrator AMC Bridge showed last summer (see it here)? AMC Bridge says it “enables users to access Aras Innovator directly from ANSYS AIM, store ANSYS AIM files on the Aras Innovator server, and use the functionality of Aras Innovator to manage ANSYS AIM projects.” How will this go to market — via Aras’ app store or ANSYS’ sales teams or both or something new? Who supports what?
I’ll let you know if I find out more. But for now, the key things are 1. it’s a partnership, not an acquisition; 2. Aras is serious about CAE; and 3. ANSYS continues to reach outside its traditional comfort zone. Partnerships with SAP, PTC and nowAras change the conversation about CAE and make it relevant in new and interesting contexts.
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Did I go to AU in 2019? Why, yes, I did. But I had the flu so only participated in a fraction of what was going on and only now have the time to share what I learned. 13,000 people, 13,000 stories and I only got a tiny bit, sigh. Since I had to narrow down, I focused on AEC (architecture, engineering and construction) — which makes sense since that’s where Autodesk’s 2019 acquisitions took place.
Here, in no particular order, are my key discoveries:
My AU in 2019 was a bit limited –I didn’t get to nearly as many sessions as I wanted and had only one quick lap around the massive show floor– but even so it’s clear that Autodesk wants to help customers create and leverage useful data. Not just drawings. In the past, I’d ride from the airport to the hotel with AutoCAD users who didn’t even know what else Autodesk offers; those days are probably gone. Whether it’s generative design, Construction IQ or something else, the quest now is to find that data nugget that will move a business in a new direction.
The title image is of BuildChange’s Dr. Hausler during the keynote.
Note: Autodesk graciously covered some of the expenses associated with my participation in the event but did not in any way influence the content of this post.
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Altair started the year with an acquisition but we have to give props to Hexagon for the pace of their deals: closing one deal from 2019 and two brand new ones today.
First the new news. Hexagon is acquiring Blast Movement Technology (BMT), maker of monitoring technology and analysis for open pit mines. Miners will discover a vein of ore, and blast away surrounding material to make it simpler to extract. BMT’s solution collects data from sensors that move with the blasted material. BMT’s algorithms then calculate things like the post-blast location of ore and precise dig lines based on the measured movement.
BMT currently supports 100 customer sites mining nine commodities in nearly 40 countries. Sales in 2019 were €19 million. The purchase price wasn’t disclosed.
I live in an area of the US where there is little (no?) mining but attended a sub-conference on mining a couple of years ago. It’s far more technical and data-driven than I had realized, and the days of randomly setting off charges to see what can be found are long gone — a movie stereotype that’s simply not true. Using sensors, ground-penetrating radar, and advanced modeling techniques enable miners to operate more safely and profitably.
Hexagon President and CEO Ola Rollén adds, “Today’s acquisition of BMT is a powerful addition to our Smart Mine portfolio, further closing the drill and blast loop for our customers, and ultimately, improving their ability to measure, manage and improve mining operations from pit to plant.”
Next, Geopraevent, which makes monitoring and alarm systems for early detection and warning of landslides, rockfalls, and avalanches. Geopraevent’s turnkey systems use sensors such as radar, webcams and cameras, and measuring technologies and software to detect these natural hazards. Then, algorithms evaluate this data in real time to detect critical trends and, when indicated, launch alarms and other actions.
I don’t know if these are Geopraevent solutions, but my mind immediately went to the warning systems put in place in the Pacific Rim after the devastating tsunami in Japan in 2011. Those combine earthquake sensors, cameras and other sensors that trigger sirens and other alerts in case of potential danger. Geopraevent says it has nearly 100 live systems in operation, serving governments and private infrastructure operators in transportation, public safety, tourism, mining and energy.
About this deal, Mr. Rollén said, “Natural hazard monitoring improves the safety of roads and railways, especially when traditional constructive measures like tunnels or dams aren’t feasible … By combining [Geopraevent’s] domain knowledge – along with its proven technologies and services – with Hexagon’s global footprint and complementary solutions, we can offer more customers the early detection and warning systems necessary for protecting human lives.”
Geopraevent is also fully consolidated as of today. Hexagon says that the acquisition will have no significant impact on Hexagon’s earnings.
Finally, Hexagon says it completed the acquisition of Volume Graphics, a deal announced in November (my writeup). Quick recap: Volume Graphics makes industrial computed tomography (CT) software and had revenue of €26 million in 2019.
In total, Hexagon said, the three deals will have a negative impact of €25 million on its fourth quarter 2019 earnings statement to cover one-off items related to overlapping technologies, and transaction and integration costs.
We should learn more about all of this when Hexagon announce its 2019 results on 5 February.
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