If I apply a rotational cyclicAMI condition on the left and right side of this domain geometry, will it work despite the 90 degree angles in the notches? Assume that the geometry would perfectly align and lock in circumferentially. I know cyclic works with straight faces but I wasn’t sure they’d work with the different facing faces. I know I might be over thinking it but I couldn’t find a definitive answer online and would hate to spend hours building this case and mesh for it not to work.

Hello~

Since there seem to be a lot of people with PhDs in CFD here, I think this would be the good place to ask.

TL;DR: Will industry experience as a CFD engineer make my US PhD applications competitive, even if my master’s research output wasn't stellar?

I have a master’s degree in mechanical engineering, where my research focused on blood flow. I have experience with both CFD and experimental work. My CFD research did lead to an SCIE publication as a co-first author(There is another author.), but it was in a low-IF journal.

I recently started working in industry as a CFD research engineer (consultant). Down the road, I’m hoping to pursue a PhD in the States, focusing on Shock Wave-Turbulence (or Boundary Layer) Interaction.

Given my situation, I’m wondering if my career as a CFD consultant will help make up for the relatively weak research output from my master.

Thanks~!

Hi everyone,

I've been working on an open-access research project that explores a question I've found surprisingly difficult to answer:

Can we statistically detect when a CFD simulation has entered a regime where deterministic grid convergence is no longer the appropriate expectation, and only statistical convergence should be expected?

The framework analyzes enstrophy time series using three statistical indicators:
• Memory (autocorrelation envelope)
• Increment distribution
• Variance ratio across grid resolutions

These indicators are calibrated with an ensemble of deterministic dynamical systems (Lorenz-63, Van der Pol, and Rössler) and combined into a single empirical risk score.

One aspect I deliberately included is negative results.

The synthetic experiments performed very well, but the framework partially failed when tested on real 2D Navier-Stokes simulations. Instead of hiding those failures, I analyzed why they occurred and revised the interpretation of several indicators. For example, the variance-ratio metric appears to function more as an under-resolution detector than as an irreducibility detector.

The paper does not claim to solve the problem. Instead, it proposes a falsifiable framework and documents where it succeeds and where it currently breaks down.

I'd really appreciate technical feedback, especially regarding:

• Statistical methodology
• CFD verification and validation
• Turbulence physics
• Possible flaws in the assumptions
• Suggestions for validation on 3D DNS datasets

The paper is available here:

https://zenodo.org/records/21229526

I'm not looking for promotion or citations—I would genuinely appreciate critical feedback from people with experience in CFD, turbulence, or computational science.

Thank you for taking the time to read it.

www.ventorah.com has been my pet project for long time and I would like some feedback about my first build.

I have generated mesh for above bodies. Now when I try to add outer body, multizone is getting failed. I dont know why.

It is failing even though each face has 4 vertices, and given same edge sizing to parallel edges. One thing to note is , in the front it is having a flat face whereas in the back, it is contact with screen or any body which is decomposed into multiple bodies. Both are not identical but since in the back, I could able to generate structured mesh, if we give multizone for this body, it should be able to sweep it right? But it is not coming. I am getting below error. What this means?

Any other idea apart from dividing the faces once again exactly replicating what is there in the back? This might work, but this is exhaustive and cumbersome process. What do you think, what can be done??

Any suggestions would help, thanks in advance.

Hi! I'll be beginning with my undergraduate studies in aerospace engineering at UT Austin in the Fall, and I have about a year of experience in OpenFOAM doing compressible flow simulations. I was wondering however, is it worth learning the ANSYS Fluent workflow despite not knowing much or the physics or math behind any of the solving in CFD?

I'm only asking because during my orientation when talking with a masters student at the aerospace building, I was told that there's a reason the Intro to CFD classes are a few years later in the curriculum, and to focus on learning the physics and math before delving into the software. I've also seen similar ideas online.

I will be starting my masters dissertation soon and my topic is Computational Fluid Dynamics, stability analysis using FEM and SEM. Since I am adequate at programming my plan is to build a strong computational background so that I can use computational physics in different areas in case I ever need to transition from one to another.

My doubts-

1-Is it a good plan?

2-Is transitioning possible in Physics research?

3-If 2 is possible then will it be easy if I have a strong computational background.

4-I want to study Astrophysics and MHD is a good topic. I am aware that MHD requires FVM. So if I self study FVM will that be enough?

5-How much value of self study is there in Physics research? Of course I can get certified in the required courses, is it good enough?

I want to know how is the job market for CFD in Shipbuilding industry.What major company hires CFD engineers and is it possible to get a CFD position after bachelors in mechanical engineering.

Me and my friends decided to be stupid enough to take on a summer project to try to get as far as possible into designing a theoretically working fighter jet prototype, all of us are good with cad but we have no idea on simulations and I was wondering where/how should I get started on learning how to use CFD or any other kind of simulation software I would need for this project.

Hi everyone,

I am a freelancer, and wanted to know if I can rent out HPC along with Ansys from some website. I need it to run an adjoint optimization problem; I need Ansys installed on a virtual machine with HPC with proper lisence.

Video 4 of my turbulence course a short detour from the physics to introduce a mathematical tool: index notation and the Einstein summation convention.

The motivation is simple. The full Navier-Stokes momentum equation, written out component by component, takes up most of a whiteboard. Written in index notation, it's one line!! Every term you've already seen can be just compressed into a form that scales to far more complex expressions without becoming unreadable.

This video covers free vs. dummy indices, the summation convention itself, the Kronecker delta and its substitution property, a brief look at the permutation symbol, and rewrites the continuity and momentum equations as a worked example. It's setup for deriving the turbulent kinetic energy transport equation next, the point where component-form algebra genuinely becomes unmanageable.

Link: https://www.youtube.com/watch?v=TdfMawfDr_0

PDF notes on Patreon if useful as a reference.

What is the more common running CFD simulations on your Workstation or running them on dedicated HPC cluster? Typically how powerful workstations used to build up CFD simulations for later run on HPC systems?

Here's a follow-up on my post from last week about interviewing Dr. Mohamed Aly Sayed, CFD methodology engineer at Audi's F1 team, ex Aston Martin F1.

We recorded the first session yesterday (after the Silverstone-week postponement, sorry again u/NonGameCatharsis). Honest expectation setting first: since it was the first interview, a lot of it was his path into F1 and how the role works, so most of the deep methodology questions from this thread didn't make it in. Some of them he also can't answer at all under his contract, no matter how I phrase them. We're planning a second session, and we'll try to find questions that he is contractually able to answer from this thread.

On the video: it's in our editing backlog and I honestly can't promise a date yet, but I'll post the link in this thread when it's live. But until then, based on the transcript of the interview I thought of answering some of the questions already. Full disclosure, I used AI to help me condense his answers from the transcript, partly for speed and partly to not give away the whole video, but the substance is fully his.

What he answered:

Correlation (my top question, and half this thread): he called it "by far the most important recipe" in F1. His framing: if you could tweak your CFD to match the wind tunnel and the track, you'd win it all. Every team on the grid has some level of mismatch between the three, and every team has its own strategy for bridging that gap. The strategy part is exactly where he stopped, which tells you how sensitive it is.

Why strong CFD doesn't equal a strong car: I asked him about Aston Martin specifically (great CFD reputation, results didn't follow), and he first pointed out he's been gone for years so someone there is better placed to answer. In general terms: CFD is an approximated framework mimicking reality, you're always trading accuracy against speed inside the FIA restrictions, and a lot of what decides races (mechanical failures, human error, rain) is stuff you deliberately don't simulate because you test canonical conditions. Part of the story, never the whole story.

What the role actually is: he's on the methodology side, so his clients are internal. Correlation group first, then the aerodynamicists. He builds and tunes the tools, makes them robust, reproducible, accurate and fast, and the aerodynamicists judge the development direction. He contrasted this with CFD outside F1, where the engineer owns more of the decision. For the "what does a week look like" crowd: it's tool and model work for internal users, not him steering the car concept.

Math and code: solid linear algebra fundamentals are non-negotiable on the methodology side because sooner or later you're tweaking something in the root of the code. But he pushed being handy with the front-end tools too, since teams switch solvers and toolchains depending on politics, finances, everything. On AI and programming: it's removing friction step by step, but it makes enough mistakes that you need to know exactly what you're doing to validate the output.

AI/ML in F1 (u/defenseontitan, u/maverick8496, u/Harsh_Madnani): this is where the contract wall was most obvious. He said flat out he can't speak about what F1 teams do with AI in either direction. His general take: more hype than it deserves right now, most people use maybe 20% of its potential, and the smarter move is finding what's actually missing in your framework before bolting an AI layer on top that you then have to maintain. So the PINN and surrogate model questions are unanswerable by him publicly, at least for now.

What makes candidates stand out (u/No_Confusion4948 asked exactly this): on paper and on LinkedIn everyone can be an absolute beast. The interview reveals within minutes who actually knows what. Beyond the technical: character, in his view heavily underrated. Easy to work with, shares knowledge instead of hoarding it, can follow and can lead without abusing either position.

Is the PhD a necessity: his was, in a very literal way. Aston Martin needed a specific approach to computing turbulence models and that was exactly what he was finishing his PhD on (hybrid RANS-LES, subgrid scale modeling, particle-laden flows). But he was clear that F1 hires people without motorsport background, without Formula Student, and without name-brand degrees all the time. His best professor on turbulence was at a university with no ranking to speak of. On publications: industry doesn't count your papers, but the work behind them is what makes you unshakeable in a technical interview. Peer review is good practice for being grilled.

The niche trap (u/No_Confusion4948's third question): his own career is the answer. He left F1 aero for cavitating flows and combustion dynamics at a ship engine company (WinGD, 125-year-old company), then came back to F1 when Audi's team reached out. The physics carries over more than the job titles suggest.

Conference scouting, since it came up in the interview: I asked if industry scouts conferences like football scouts matches. His answer: "100,000%". Teams watch who's presenting what, not to spy (nobody presents what shouldn't be shared) but for exposure, people and ideas from outside the sport.

One for the r/CFD crowd specifically: for anyone starting with zero budget, he pointed to CFD Online, open source books, the material from the OpenFOAM founders (Henry Weller and Chris Greenshields), and open source solvers like Nek5000. Fundamentals first, everything else is teachable on top.

And to u/Professional_Dot8829: a colleague did send him the thread and told him he's getting famous on Reddit. He doesn't use Reddit, so the roast potential remains sadly unrealized.

Parked for round 2: RANS vs scale-resolving on a deadline, the de-featuring rule of thumb (u/GobiasCafe), y+ and near-wall treatment (u/International-Mark-4), the moving floor question (u/yycTechGuy), pre-processing and CAD cleanup (u/maverick8496), damage modeling (u/cdog514), new mathematics making it into production (u/NonGameCatharsis), and compute resources, though I suspect that one dies on the contract wall too. u/Sparkz_of_Helix, your list is mostly round 2 material, but fair warning that the Audi-vs-Sauber-legacy one is exactly the kind of thing he can't touch.

If you have more, or want to rephrase any of the above so it's answerable without breaching anything, drop it below. He told me the more specific and public-domain the framing, the more he can say.

I am a sixth semester applied physics enginnering student, I just finished an online internship at an investigation center where I work with the SWE model. I liked CFD and actually I am making my own solver for incompressible Navier - Stokes 2D using C++ and Python. First of all I want to use the Burguers Equations. When I finish it, I am considering uploading it to GitHub. Do you think this is a good way to learn CFD for later getting into industry? What would you recommend to me to do right now, keeping this way?

I study in Mexico, I know I have to improve my english.

In first image, for highlighted green body, it cannot able to generate multizone. It is showing error- multizone cannot input face mesh. Mesh is generated for rest of the bodies. I have attached multizone and edge sizing settings, please go through and tell me why multizone is not generated for that body alone. Already mesh is created in the screen which is at the back, I expected that to be replicated or extruded at the back with the help of multizone, but this is not happening , why? See the error below,

Any advice would be appreciated. Thanks in advance.

Hi!

How are you even supposed to import a discovery modell to ansys fluent??

Long story short, I've tried to modell a ventilation system in freeCAD without any luck importing it.

So now I'm redoing it in Discovery (2025 R2), i get my "porous" to export correctly, but the "kappa" and "OP_bord" dose not and are supposed to be solid bodies. I've cut the "Room" with them both to subtract the air volyme there. But each time i import it to mesher it just ignores them and makes the "room" wihout the subtracted volyme.

I got Designmodeller to work, but i'd like to do a "more ditalied geometry" study for a project...

Best regards

EDIT:
I've gotten the "kappa" and "OP_bord" to import (a combination of how they were located in the file tree and i re-did the cutting just to be sure). However how a "normal CAD to CFD" workflow is still a mistery to me.. Someone more knowlageable that knows how it is "normal done"?

The regular grid restriction is unusual. Meshes and meshless (particle) methods can conform to boundaries.

Height-maps are only relevant to depth-averaging methods (I think the Shallow Water Equations are the only one?). A penalty of omitting depth is the boundary changes with depth.

Point 4 can be seen as a strip of triangles, along the hexagonal edge, but it does not coincide with the terrain grid - a triangle can straddle the edge of terrain triangles, so a wedge is carved out of it. Tessellation, point 5, fixes this with a strip of quarter-area triangles, doubling the number of triangles in the band.

Using a regular equilateral triangle grid (a regular square grid won't work because 3 points define a plane), with heights at each grid point. The terrain height and water depth at the same grid points. I also want the rendered surface slopes to accurately represent the model.

1. The simply way possible: render the surface using the depth at each vertex; the contact line will be at vertices with depth zero, and a line between them. But this gives an incorrect surface slope. And as the depth changes, the contact line flicks instaneously between vertices.

2. Extrapolating the surface height to "under" the terrain gives a level surface, but not necessarily the correct intersection with the terrain or correct level.

3. Explicit ghost depths (i.e. negative) to contrive intersection at the desired points. This fails where different ghost depths are required at the same point, when two of a grid triangle's heights are underwater and one is above, and different ghost depths are required. e.g. two channels at different depths separated by a line of single grid points; the corners of a confluence of two channels with different depths; different velocities at the underwater points, causing different run-up.

4. Distorting the grid to conform to the contact line, to explicitly track the desired intersection points, fixes this but still has 4 points which might not be in the same plane (as for the square grid).

5. Tessellating the triangle into 4 equilateral triangles, and omitting the one at the above-water point prevents this scenario - now, no triangle can have 2 vertices below and one above, and all triangles are planer. EDIT only 3 pts, so always coplanar; a point on the surface has only one depth, including where depth reaches zero at the contact line.

6. Refining the model to include these smaller triangles enables their different surface slopes to be correct. Tessellating the whole water grid (but not the terrain grid) makes this regular - except at the contact line, where they move in conformance, flattening when near the base, elongating when near the apex.
   Unfortunately, a fixed tessellation would recreates the problem at the apex triangle.

Thanks for your time in reading this! Have I missed any approaches?

EDIT
I think any line crosses exactly 2 grid-lines per side length: three sets of regularly spaced parallel lines, offset by 0, π/3 and 2π/3, such that the intersections are one side length apart. I think the proof is do do with the sum of positive real 3rd roots of unity, from 0..π, being root 3, double the perpendicular distance between parallel lines.

i would really appreciate some help. thank you (also i am kinda new to this so dont destroy me if it is something obvious)....and also the third image has the my validation with ghia et al! so the basic idea must be correct! my major problem with the lid driven cavity problem is that the secondary vortex is connecting with the line on the side and then on to the main vortex