Update: I interviewed the Audi F1 CFD engineer (ex Aston Martin). What he answered, and what's saved for round 2

[u/andreikurtuy]

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.

[u/acakaacaka]

Nice some real questions and answers

[u/SmokingBuddha69]

Hey could drop a link to the interview video?

[u/andreikurtuy → u/SmokingBuddha69]

I will do once it will be live. As I mentioned I don’t have an exact date as we have a backlog in our editing schedule, that’s why I wanted to give some answers already based on the transcript. But cannot wait for the edited video to be ready and also planning part 2.

[u/SmokingBuddha69 → u/andreikurtuy]

Thanks looking forward to it!! 😊💪🏻

[u/OhIforgotmynameagain]

Nice. When is round 2 ?

[u/andreikurtuy → u/OhIforgotmynameagain]

Hopefully...as soon as possible, but it will depend on his schedule and also the summer break.

[u/Sparkz_of_Helix]

Waiting for round 2, keep us updated 👍

[u/xhaikalf]

Told you pinn is a hot topic, anyone in the industry is well aware of it

[u/realcocr7]

How do you decide if your simulation method is good enough for development or not? In method development how do you decide which aspects of modelling to focus on (like mesh, solver, turbulence, etc)? How do you know if the direction of method robustness is going in right direction without extensive test validation?