This Week in CFD

This week’s compilation of CFD news begins with a must-read article on how to choose colors properly when visualizing data. AI comes up twice this week as does Fortran which makes one wonder whether anyone’s programming AI in Fortran. There’s a hybrid event coming this November which is a great sign that we’ll get back to in-person conferences soon. If you’re a student seeking a scholarship to attend AIAA Aviation this August (online), their Diversity Scholars Program is accepting applications until the end of May. Of course there’s the whole suite of CFD applications for planes, trains, and automobiles. Shown here is a classic structured grid generated using Pointwise’s predecessor for an F-16.



A piston bowl simulation from an article about how Argonne is supporting Caterpillar’s engine design activities.

CFD for

Temperatures in a data center from an article about the design of HVAC systems for data centers by B-barcelona Consulting.


From Pointwise

Mesh generation for subsurface modeling is a unique breed of the technology and draws the Lego analogy for reasons obvious in this photo. Read more in a profile of Los Alamos’ Terry Miller. Image credit: LANL via


Intense Concentration, Confident Reserve

Audrey Stone cites Vija Celmins, Agnes Martin, and Mark Rothko as some of her influences. You might not get all of that from the work I’ve chosen to include here. But like Celmins and Martin, there is a patience and deliberation in this work of thread, ink, and pencil as she explores what defines a line. Read more at the artist’s website.

Audrey Stone, #59, 2011. source

Bonus: A candidate for the presidency of Nigeria is a mechanical engineer with an undergraduate specialization in CFD. Vote wisely.

Thank you to everyone who called or emailed to ask whether This Week in CFD and Another Fine Mesh will continue in the wake of Pointwise’s acquisition by Cadence. We appreciate your readership.

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2 Responses to This Week in CFD

  1. A Blog Reader says:

    Interesting to see the “AI to optimize the control of fluid jets”. The authors of that study used linear genetic programming, an optimization method developed in the late 90’s. No neural nets or deep learning was involved, but they take great care to use the term “AI” as frequently as possible. Not trying to detract from the paper at all – it is definitely a very interesting topic and very good work. But it does feel like the sort of thing that could have been done 20 years ago, and then it would not have been called “AI”. In fact, Koumoutsakos et al. did the same thing using CFD in 2001 – then it was called “Evolution Strategies for Automatic Optimization of Jet Mixing” – which was duly cited in this work.

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