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hi and welcome to the Neil Ashton podcast.

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In each episode,

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we explained some of the fascinating ways that science and engineering

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are changing the world around us.

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We talk to leading engineers from elite level sports like cycling and Formula One

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to some of the world's top academics.

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To understand how fluid dynamics, machine learning and supercomputing

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are bringing in a new era of discovery,

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we also hear some of their life stories, their career advice

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and lessons they've learned on the way that I hope will be helpful to you too.

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So sit back and enjoy this episode.

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So this episode

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focuses on computational fluid dynamics. CFD.

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I say that upfront because some of the episodes from this

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podcast are going to be at a higher level of focusing more

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on a vertical like cycling or Formula One,

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whereas this is very much focused on on CFD. So I just

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warn, if you are somebody from the CFD community,

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I'm hoping you'll find this interesting and engaging.

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But if you don't even know what CFD stands for, I just want to call out that, um,

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this episode may not be for you,

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but nevertheless, maybe give it a try

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today.

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In this episode,

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I've been speaking to Dr Florian Menter,

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one of the people,

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and there's not very many of them that have a surname that precedes them.

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I would say

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of the tens of thousands, maybe hundreds of thousands of people

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within the CFD community globally.

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I would say most people know who he is

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because he developed one of the key turbulence models,

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the k–ω

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SST model that really has been responsible for so many

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engineering things. Cars, planes,

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pretty much everything you use on a day to day may have been developed,

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using his model as a fundamental part of the CFD set up.

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See, you know,

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the model, the turbulence model,

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just very briefly to say that for maybe people on

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the borderline of being in the CFD community or not,

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is really important because turbulence is such a complex thing.

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Um, we have to come up with models most of the time and

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and certainly in the past few decades and and arguably still today.

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Although it's slightly changing now due to a higher Fidelity methods,

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people needed to be able to approximate

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turbulence in a in a steady state way through

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some sort of mathematical model to be able to predict

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industrial flows like over a car,

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and he and he is one of those people who developed a model that has been so widely used.

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So

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the episode is really trying to understand some of the motivations

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how he came up with the model in the first place,

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but more about him as a as a person and his life story and his journey into CFD.

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We talk about how he started at the von Kármán

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Institute, then moved to DLR

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I. I found particularly interesting the the area around his move to to NASA Ames

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for people who are not aware at that time when he was at NASA Ames Research Centre

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was also when Philippe

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Spalart, arguably another person with a surname that is, uh, very famous

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and a model that is widely used.

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So we talked a little bit about that,

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and, uh, I thought was quite interesting. Was his choice to move

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back to Germany to move away from the US and I. I

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sensed somebody who is deeply, um,

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motivated by life and not just work.

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He's somebody who arguably, um,

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you don't see going around and giving keynotes everywhere and put, you know,

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flashing his name.

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He's not in the social media world.

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He's some. In some ways, stay stays quite quiet, considering everything he's done.

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And I think through this episode you get a bit of a sense of that.

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I think he he said at one point. You know, he he wanted to go back home.

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He liked that area, and I can certainly, you know,

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understand that that sort of thought process,

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you know, he's spent many decades

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at Ansys

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and has

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gone beyond, of course, just the k–ω

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SST model

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did real innovations in transition modelling and also scale resolving model, um,

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scale resolving methods

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and and I'm sure, is a

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key figure inside Ansys in terms of what they do.

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So we talk about all those we talk a little bit about machine learning at the end, but

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I think it's an interesting one in just

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how somebody who's been so key to our community

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how they did what they did and and a little bit more about them. So,

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yeah, I hope you enjoy this, um, episode and what we talked about

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um

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I have to apologise a little bit.

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The audio quality probably isn't as good as I'd like it to be.

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Hands up. I'm still learning this whole podcast thing.

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We just got this decent microphone,

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so I hope you'll bear that maybe the audio quality on both sides is not as good

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as it could be. But nevertheless, uh, I hope you enjoy this this episode.

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Yeah. Thanks very much for doing this, though. I appreciate you taking your time.

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Your busy time to do this,

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you know, loads of people. Actually, when I

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said to a few people that I was thinking of doing this podcast,

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you were one of the people,

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um,

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that they said I should speak to.

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And I guess, um,

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this isn't just trying to be, you know,

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nice to you. Artificially. There's not that many people

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in the CFD world

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that their surname

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is, like, instantaneously recognisable.

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It's kind of something that people think about when they're starting their career.

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But not many people actually get the the surname,

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uh, thing and, um

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yeah, I. I guess Maybe that's why I wanted to start, which is

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I know you've done a lot since and we'll get on to that.

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But arguably, what a lot of people know you for is,

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you know, the k–ω SST model.

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But I'm more interested to know,

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like, the journey to get there. You know, when

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When did that start? Was this something already

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in your sort of PhD days? Was this something?

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Once you

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went to NASA, where did that

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sort of idea come from?

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Well, actually, I I came to CFD. Of course, everything happens in in

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in in life by coincidence, right? So I was studying technical mechanics, Really?

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And the emphasis was on structural mechanics.

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And the way I got into CFD is there was on the blackboard.

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There was an announcement from the von Kármán

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Institute

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and you could get a small, you know,

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fellowship or something when you go there for half

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and write you some. Some thesis.

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So I wrote my diploma thesis there, so that's, you know,

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purely by that coincidence on the blackboard,

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I got into CFD. And I had a diploma thesis on on numerics there,

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And, uh, after that—I was in Clausthal-

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Zellerfeld,

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a very small university.

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My professor was working actually mainly at the DLR in Göttingen.

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So he asked me if I would want to join, uh, DLR. So I spent about five years at DLR.

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But I didn't do any. Really. Didn't do any turbulence modelling. It was mostly, uh,

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code development, parabolized Navier–

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Stokes equations, actually, at the time,

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Uh, my boss was

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Uli

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Meier. It was an experimental group, and they had done a lot of measurements.

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You probably know this prolate spheroid

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test case, right?

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So

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still still well known.

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And he he wanted to have somebody to compute that case. And

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there was some tension between the experimental

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and the and the computational department.

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And and then he decided, you know, he opens his own computational department,

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which was me, essentially.

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And and so I ended up in in CFD.

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But in an experimental department, and I, you know,

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I had to essentially write my own code because

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I didn't get any access to the other code there or I didn't try hard enough. Maybe

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so. That's how I got into CFD. And there were some, you know, interactions with

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international groups because everybody was computing the prolate

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spheroid at the time.

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And there was a working group with fairly high level people there to

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C

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Cebeci. You might know the name. He was head of aerodynamics

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at McDonnell Douglas, and

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some people from from NASA Ames like

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Tom Coakley

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and others.

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And

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And I was there because my boss always took me along because he said, you know,

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they talk about CFD.

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I don't understand about anything about it.

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So I take you along, you know, and then you can tell me what they want.

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And, uh

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so that's how you know, I got a bit more into this, uh,

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CFD community there Still not doing any turbulence modelling.

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And

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the interesting thing about that working group was

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that developed eventually a kind of a controversy.

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And it was a high level meeting.

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And high level meetings typically don't like controversy. But

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I was there, and I you know, there was something wrong with

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what was argued there, and I think everybody else felt the same way,

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But nobody said anything and I was, you know, young and stupid.

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So I said, You know, I don't think that

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that's correct what he's saying. It was essentially between

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Tuncer

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Cebeci

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and myself.

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Of course, he was the head of the whole group there.

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And, uh,

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anyway so, yeah, we we managed to get around that and, uh,

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write it into the final report.

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But

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after that I I had the idea to go to, uh, wanted to spend a bit of time in the US.

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And, uh, my boss,

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Uli Meier,

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he had some context to Iowa.

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But then, you know, I looked at the map and I said, No, I want to go to California,

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you know?

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So II I sent

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I sent my resume to to the Center for Turbulence Research CTR Stanford

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aim high, you know,

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and, uh

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and I got turned down because they didn't have any open positions at the time.

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But, uh, they did send on my resume because apparently it was not so uninteresting.

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So they passed it on to NASA Ames

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and it ended up on Joe

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Steger’s desk, and he said, Well, you know, I know the guy.

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That was the guy who picked a fight with Tuncer Cebeci, you know, in this

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in

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this workshop there, this working group

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and so you know, that's how I eventually ended up at NASA Ames and, uh,

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luck wanted it that I ended up again.

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Not in the CFD department where I actually wanted to be, but I ended up in a

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essentially experimental group again. It was Joe Marvin's group.

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They did a lot of experiments on high speed flows, and it was

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the supersonic transport times which apparently comes and goes, you know, as

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we know,

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And

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so I ended up.

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But there were some people who had some CFD turbulence, modelling background.

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Obviously, it was Tom Coakley was one of the old hands there, And

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and, uh, Dennis Johnson, you might know from the Johnson King model at the time.

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And so there was There was enough know how

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on turbulence modelling to get into that subject area.

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But I really didn't know anything about it.

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And that was

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1990 when I started.

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So, um,

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was that So where was that at Ames? Was this

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This wasn't the NA

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What is now the NAS

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building was this was this near one of the wind tunnels because I, I

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can't

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remember the building. I think it was 248 or something.

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It was one of the smaller buildings next to the

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wind tunnel there and not a very memorable building altogether.

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And it was kind of mixed offices.

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And I think there was even some experimental facilities there. So,

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uh,

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yeah, it was just one of the standard buildings having been there for a long time,

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I guess not.

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Not the modern building across the street where all the, you know,

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the computer guys were sitting.

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And But anyway,

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it was still very interesting because

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for turbulence model,

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it's maybe not that bad to be in

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a mixed group with experimentalists because of obviously,

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00:12:11,109 --> 00:12:14,109
they understand a lot about the physics of these things, and

253
00:12:14,609 --> 00:12:15,679
and they had a

254
00:12:15,919 --> 00:12:19,270
good understanding of turbulence modelling. So they had their own codes.

255
00:12:19,280 --> 00:12:19,869
Mostly, you know,

256
00:12:19,880 --> 00:12:24,260
everybody had their own little code there and then implemented different models.

257
00:12:24,650 --> 00:12:29,409
And I had a lot of discussions with Tom Coakley and with Dennis Johnson,

258
00:12:29,419 --> 00:12:30,590
who is a good friend of mine

259
00:12:30,979 --> 00:12:32,400
by now. And, uh,

260
00:12:32,679 --> 00:12:36,500
so I learned quite a little bit about turbulence modelling.

261
00:12:36,510 --> 00:12:40,320
And of course, you know, you start somewhere, you buy a book, and

262
00:12:40,770 --> 00:12:43,000
I bought Wilcox's book.

263
00:12:43,010 --> 00:12:45,960
Obviously not because I think it was certainly

264
00:12:45,969 --> 00:12:48,479
one of the better books from a practical standpoint

265
00:12:48,890 --> 00:12:50,989
and not so many books were there anyway. So

266
00:12:51,299 --> 00:12:54,450
II I read through that book and I found it rather interesting, you know?

267
00:12:54,460 --> 00:12:56,750
And of course, it was a bit controversial.

268
00:12:56,760 --> 00:12:59,500
Also the book. It was this, you know, the struggle between k–

269
00:12:59,650 --> 00:13:00,299
ω and k–ε

270
00:13:00,440 --> 00:13:02,799
groups and so forth. And

271
00:13:03,119 --> 00:13:06,859
But I did see I mean, I. I picked up something there, and that was the

272
00:13:07,159 --> 00:13:09,469
the merits of k–ω model

273
00:13:09,739 --> 00:13:11,409
and and that being, you know,

274
00:13:11,419 --> 00:13:13,820
it can integrate it to the wall without a

275
00:13:13,830 --> 00:13:16,799
lot of additional blending and damping and whatever functions.

276
00:13:17,489 --> 00:13:20,400
And, uh, that was always the downfall, essentially, of the

277
00:13:20,580 --> 00:13:20,619
k–ε

278
00:13:20,760 --> 00:13:21,559
model: there

279
00:13:21,840 --> 00:13:23,770
there were so many different versions and

280
00:13:23,780 --> 00:13:26,099
they had all the highly nonlinear functions and

281
00:13:26,109 --> 00:13:29,950
they were all essentially unstable at the end of the day for complex flows.

282
00:13:30,609 --> 00:13:33,590
So So I learned that from from that book and then

283
00:13:34,479 --> 00:13:36,349
I mean, the challenge at the time

284
00:13:36,849 --> 00:13:40,739
was also becoming fairly clear. It was, you know, it's

285
00:13:40,890 --> 00:13:43,340
30 some years ago, 35 years ago.

286
00:13:43,940 --> 00:13:46,830
It was just about the time the computing power on the

287
00:13:46,840 --> 00:13:51,530
Cray Y-MP was about sufficient to do 3D simulations.

288
00:13:51,539 --> 00:13:53,599
Really? So a few 100,000 cells

289
00:13:54,030 --> 00:13:57,489
on a more or less regular basis and

290
00:13:58,539 --> 00:14:03,150
in the aerodynamics is they had all used algebraic models. So they had the

291
00:14:03,289 --> 00:14:05,039
Cebeci–Smith model or Baldwin–Lomax

292
00:14:05,359 --> 00:14:08,150
model. Or then you know, the Johnson King model

293
00:14:08,630 --> 00:14:12,489
because it was mostly two dimensional simulation. So they had

294
00:14:12,679 --> 00:14:15,130
for the sections, and you could find the boundary-layer edge, you know,

295
00:14:15,140 --> 00:14:17,320
and they could then go and march along and

296
00:14:17,729 --> 00:14:19,289
and and use these models. But

297
00:14:19,640 --> 00:14:24,489
in a 3D flow, you could hardly find the boundary-layer edge anymore in a 3D

298
00:14:24,989 --> 00:14:27,750
separation. It's pretty difficult and ambiguous, So

299
00:14:28,940 --> 00:14:34,010
it was clear that there had to be a change in the paradigm from an algebraic

300
00:14:34,150 --> 00:14:36,390
model to a transport-equation model.

301
00:14:37,650 --> 00:14:38,130
And

302
00:14:38,340 --> 00:14:41,640
of course, there was a number of transport equation models out there,

303
00:14:41,650 --> 00:14:43,460
most noticeably k–ε.

304
00:14:43,469 --> 00:14:45,890
But that was industrially mostly used with wall functions,

305
00:14:45,900 --> 00:14:49,969
which they also didn't like, and certainly not very suitable for aeronautical

306
00:14:50,070 --> 00:14:51,559
simulation. So

307
00:14:52,159 --> 00:14:54,169
So you had these different elements and

308
00:14:54,539 --> 00:14:57,650
and and the third element was, of course, you have to be able to

309
00:14:57,780 --> 00:14:58,929
predict separation

310
00:14:59,760 --> 00:15:01,000
onset of separation,

311
00:15:01,010 --> 00:15:04,659
onset of stall because that's the safety envelope of the aircraft.

312
00:15:04,669 --> 00:15:05,609
And and so

313
00:15:05,750 --> 00:15:06,599
so the k–ε

314
00:15:06,710 --> 00:15:06,830
model

315
00:15:06,960 --> 00:15:09,299
was really not an option because we all know

316
00:15:09,830 --> 00:15:12,520
it's going way too high in the angle of attack,

317
00:15:12,530 --> 00:15:15,039
way too large in maximum lift and so forth.

318
00:15:15,719 --> 00:15:16,289
So

319
00:15:17,010 --> 00:15:17,510
I mean,

320
00:15:17,880 --> 00:15:21,830
what I did, I think I connected the dots which were there, right?

321
00:15:21,840 --> 00:15:23,650
So there was a demand for something

322
00:15:23,859 --> 00:15:25,950
and there was different elements out there.

323
00:15:26,619 --> 00:15:31,260
And, uh, the k–ω model also had its kind of weaknesses at the boundary-layer

324
00:15:31,390 --> 00:15:33,929
edge. And it also didn't separate all that well.

325
00:15:34,580 --> 00:15:37,320
And so what you do essentially,

326
00:15:37,330 --> 00:15:42,859
you try to combine these elements into one unit that you can then put into a code and,

327
00:15:42,869 --> 00:15:44,010
you know, get it to run there.

328
00:15:44,020 --> 00:15:44,539
It's

329
00:15:44,830 --> 00:15:46,570
it sounds a bit easier than it was.

330
00:15:46,580 --> 00:15:47,049
I mean, first,

331
00:15:47,059 --> 00:15:48,989
you have to play around and get it robust and get it

332
00:15:49,000 --> 00:15:53,239
stable and also have these blending functions which clearly are necessary.

333
00:15:53,250 --> 00:15:53,520
Then,

334
00:15:53,960 --> 00:15:56,409
to distinguish between the boundary layer and the free-shear

335
00:15:56,570 --> 00:15:56,950
flow

336
00:15:57,409 --> 00:16:00,359
to get those reliable to switch really at the

337
00:16:00,369 --> 00:16:03,229
place where you wanted to switch and not randomly,

338
00:16:03,239 --> 00:16:06,070
depending on whatever free stream conditions we have.

339
00:16:07,080 --> 00:16:12,219
But essentially these three elements then, so you could say it was the near the wall.

340
00:16:12,229 --> 00:16:13,159
It's kind of, uh,

341
00:16:14,400 --> 00:16:16,059
Wilcox, right?

342
00:16:16,530 --> 00:16:16,969
K

343
00:16:17,219 --> 00:16:20,280
ω. Then in the middle of the boundary layer it was Johnson–King.

344
00:16:20,289 --> 00:16:23,320
So I took some of the elements of the Johnson King model, really,

345
00:16:23,330 --> 00:16:26,349
which Dennis had explained to me in large detail.

346
00:16:26,729 --> 00:16:29,690
And then near the edge of the boundary layer, we blended in a bit of k–ε

347
00:16:29,849 --> 00:16:33,140
to avoid the k–ω free-stream problem.

348
00:16:33,590 --> 00:16:35,799
And that was the SST model. And,

349
00:16:35,979 --> 00:16:36,270
you

350
00:16:36,400 --> 00:16:36,419
know,

351
00:16:36,700 --> 00:16:40,909
I didn't think too much about it. You could calibrate it, obviously. And

352
00:16:41,609 --> 00:16:45,989
I mean, what I had learned then is you cannot calibrate a model

353
00:16:46,390 --> 00:16:46,520
for,

354
00:16:47,359 --> 00:16:48,859
for

355
00:16:49,000 --> 00:16:51,520
for boundary-layer separation and for free-shear flows.

356
00:16:51,530 --> 00:16:52,909
You have to have some distinction.

357
00:16:52,919 --> 00:16:55,190
You have to have some wall-distance information there.

358
00:16:56,609 --> 00:16:58,690
And the same is true actually for

359
00:16:59,159 --> 00:16:59,570
Spalart–Allmaras, right?

360
00:16:59,580 --> 00:17:01,849
It also has this wall-distance-dependent term,

361
00:17:01,859 --> 00:17:03,799
so it can be calibrated differently for the

362
00:17:03,809 --> 00:17:05,449
boundary layer than for the mixing layer.

363
00:17:06,420 --> 00:17:08,650
And and and then we put it in the code.

364
00:17:08,660 --> 00:17:12,270
And I wrote the NASA technical memorandum, and, uh,

365
00:17:12,660 --> 00:17:15,729
I remember I talked to one of my older colleagues there,

366
00:17:16,130 --> 00:17:17,079
Uh, Michael

367
00:17:17,339 --> 00:17:17,689
Horstman.

368
00:17:18,290 --> 00:17:20,479
He was a high speed guy, but he looked at it and he said,

369
00:17:20,900 --> 00:17:22,569
Hey, that's fantastic. You know,

370
00:17:22,900 --> 00:17:24,589
that model is gonna make you famous.

371
00:17:25,239 --> 00:17:27,368
And I thought he's kidding me, you know?

372
00:17:27,380 --> 00:17:31,390
I mean, this is you know, I was two years into turbulence modelling, and, you know,

373
00:17:31,400 --> 00:17:35,790
I had just put together some stuff which I had picked up on the road, so to speak.

374
00:17:35,800 --> 00:17:36,150
And, uh,

375
00:17:36,829 --> 00:17:38,489
but essentially, he was right.

376
00:17:38,500 --> 00:17:39,180
I mean, of course,

377
00:17:39,189 --> 00:17:42,369
the model spread like wildfire into all different

378
00:17:42,380 --> 00:17:45,150
codes and not not terribly difficult to implement and

379
00:17:45,609 --> 00:17:52,109
fill the need and the gap at the time. And then that's how it then went from there.

380
00:17:52,119 --> 00:17:52,390
Really?

381
00:17:53,160 --> 00:17:54,770
So what was the, um

382
00:17:55,859 --> 00:17:57,199
what was it like, though?

383
00:17:58,619 --> 00:18:00,040
At Ames, you know,

384
00:18:00,270 --> 00:18:00,819
because

385
00:18:01,479 --> 00:18:05,510
from my you know, brief time there in speaking with people,

386
00:18:05,520 --> 00:18:07,640
everyone talks about that era

387
00:18:07,949 --> 00:18:09,310
as one of the golden eras

388
00:18:09,790 --> 00:18:15,469
of turbulence modelling within within Ames and the link with Stanford and the

389
00:18:15,699 --> 00:18:16,989
so. So what was it?

390
00:18:17,420 --> 00:18:21,640
Was there any kind of noticeable people that you worked with or experiences there?

391
00:18:21,650 --> 00:18:23,410
Did you find it was that sort of

392
00:18:24,170 --> 00:18:25,489
collaborative environment.

393
00:18:25,500 --> 00:18:28,550
Did you Did you feel that or did at the time, it just seem normal.

394
00:18:28,920 --> 00:18:30,859
And something later you look back.

395
00:18:32,540 --> 00:18:35,359
I mean, I did not have too many

396
00:18:35,890 --> 00:18:39,829
connections outside that department where I was in. Yeah,

397
00:18:40,510 --> 00:18:45,439
So my my colleagues there, of course, we went to lectures in Stanford.

398
00:18:45,449 --> 00:18:49,390
If there was some, you know, we drove over there. It's not so far, obviously. And, uh,

399
00:18:49,599 --> 00:18:53,319
there was officially there was actual interaction between CTR and NASA.

400
00:18:53,329 --> 00:18:55,800
There was some sort of a project joint

401
00:18:55,910 --> 00:18:57,180
collaboration there,

402
00:18:57,760 --> 00:19:01,599
but overall, I, I know there were other people at the time there, So Spalart

403
00:19:01,839 --> 00:19:07,329
was in the, you know, in the orbit there. I don't think we ever met at that time. And, uh

404
00:19:08,030 --> 00:19:11,819
and of course, there was the people from the computational group.

405
00:19:11,829 --> 00:19:15,250
And of course, we had our meetings and our interactions and seminars,

406
00:19:15,260 --> 00:19:17,250
and I also got the code from there.

407
00:19:17,459 --> 00:19:18,949
So I got from Stuart Rogers.

408
00:19:18,959 --> 00:19:20,530
I got the INS to D code,

409
00:19:20,540 --> 00:19:23,780
and he explained to me how to put turbulence models in there and so forth.

410
00:19:23,790 --> 00:19:26,050
So there was that kind of Cooper operation, but

411
00:19:26,510 --> 00:19:30,790
there wasn't much outside my my group in terms of, uh,

412
00:19:30,959 --> 00:19:34,410
kind of active turbulence model discussions.

413
00:19:34,900 --> 00:19:35,550
Uh,

414
00:19:35,800 --> 00:19:36,229
but

415
00:19:36,390 --> 00:19:38,239
I, I think that for me,

416
00:19:38,880 --> 00:19:42,000
the main aspect was first of all, they left me alone.

417
00:19:42,010 --> 00:19:45,359
You know, they didn't tell me what to do. It was pretty in that respect.

418
00:19:45,369 --> 00:19:46,619
It was extremely liberal.

419
00:19:46,630 --> 00:19:50,369
I mean, I you know, if I didn't do anything, they wouldn't mind either.

420
00:19:50,380 --> 00:19:53,339
You know, So every year you had kind of a little chat with your

421
00:19:53,449 --> 00:19:56,589
head of your department, Joe Morin, and he said, Well, great, you know,

422
00:19:56,599 --> 00:19:58,140
you're doing a great job and that was it.

423
00:19:58,150 --> 00:19:58,300
Yeah.

424
00:19:58,930 --> 00:19:59,500
And,

425
00:19:59,510 --> 00:20:01,699
uh and so you could really play around and

426
00:20:01,709 --> 00:20:04,030
fool around there and nobody would bother you.

427
00:20:04,040 --> 00:20:07,489
And I think that was very helpful to me, because

428
00:20:07,810 --> 00:20:10,359
otherwise you kind of, you know, forced to

429
00:20:10,510 --> 00:20:11,800
think about other things.

430
00:20:12,439 --> 00:20:17,060
And, uh, so II I enjoyed actually that kind of atmosphere. And

431
00:20:17,459 --> 00:20:20,060
there was another element which I

432
00:20:20,560 --> 00:20:22,599
could compare DLR and

433
00:20:22,760 --> 00:20:25,979
NSA, which are more or less, you know, similar organisations.

434
00:20:27,310 --> 00:20:27,890
And

435
00:20:28,180 --> 00:20:32,140
if you would say something at DLR, I exaggerate a little bit at DLR.

436
00:20:32,150 --> 00:20:33,530
They would say that never gonna work.

437
00:20:34,369 --> 00:20:36,229
But now they would say, Hey, that’s a great idea.

438
00:20:37,989 --> 00:20:38,270
Yeah,

439
00:20:38,280 --> 00:20:43,770
and and so they have always a bit more of a kind of a positive feedback loop and which,

440
00:20:43,780 --> 00:20:47,569
which allows you then as a kind of a younger researcher, to go out, you know,

441
00:20:47,579 --> 00:20:51,170
and do your thing there without being discouraged

442
00:20:51,390 --> 00:20:55,109
immediately from from the start, which makes things a bit harder.

443
00:20:56,030 --> 00:20:58,170
But do you remember what it was? Because I can imagine.

444
00:20:58,180 --> 00:21:02,530
So you were basically a couple of years. If I translate it to like somebody now,

445
00:21:02,949 --> 00:21:03,890
so you'd done

446
00:21:04,589 --> 00:21:06,689
your sort of education, so to speak.

447
00:21:07,150 --> 00:21:09,849
You were basically in your first

448
00:21:10,660 --> 00:21:14,209
post. Well, not post op. But first job outside of

449
00:21:14,709 --> 00:21:16,400
PhD work,

450
00:21:16,910 --> 00:21:17,640
and

451
00:21:18,890 --> 00:21:23,930
you then came up with a model. Do you remember going to the AIAA? Did people

452
00:21:24,459 --> 00:21:26,770
take you seriously? Were people sort of doubting?

453
00:21:26,780 --> 00:21:29,609
I'm trying to translate it to now if someone literally was to come

454
00:21:29,619 --> 00:21:33,589
and say I've made a model two years after they're like graduating.

455
00:21:34,229 --> 00:21:37,969
Do Do you remember what? What reception you got presenting those?

456
00:21:38,630 --> 00:21:43,369
Well, I mean, clearly, NASA helps, right? If you have NASA

457
00:21:43,489 --> 00:21:45,050
standing on the paper

458
00:21:45,260 --> 00:21:49,780
and and, of course, if we went. We always went to Reno. There was every year in

459
00:21:50,030 --> 00:21:53,770
winter, there was the AIAA conference in Reno.

460
00:21:54,369 --> 00:21:55,089
And, of course,

461
00:21:55,099 --> 00:22:00,189
then you were already in a group of people from NASA because almost everybody went.

462
00:22:00,199 --> 00:22:01,920
It's not a long distance either. So,

463
00:22:02,229 --> 00:22:04,489
uh, you were already kind of

464
00:22:04,640 --> 00:22:06,209
part of the family there.

465
00:22:06,699 --> 00:22:09,979
And in that respect, you know, you never know what people think.

466
00:22:09,989 --> 00:22:14,449
Obviously, you know, I had some meetings with Wilcox, and of course, he you know,

467
00:22:14,459 --> 00:22:17,780
I don't know whether they ever met Wilcox when he was still alive.

468
00:22:18,219 --> 00:22:20,239
I mean, he was also pretty special character.

469
00:22:20,250 --> 00:22:24,170
I liked him, but he was, you know, was a bit different than other people. And

470
00:22:24,380 --> 00:22:28,780
clearly he didn't, you know, he didn't think much of it, but, uh, yeah,

471
00:22:28,949 --> 00:22:29,410
I mean,

472
00:22:29,709 --> 00:22:30,739
at the

473
00:22:30,859 --> 00:22:35,859
end of the day, uh, I think it's it's only the results and the test cases,

474
00:22:35,869 --> 00:22:37,150
the calibration of the model.

475
00:22:37,160 --> 00:22:37,469
And then,

476
00:22:37,989 --> 00:22:39,890
of course, being at NASA, of course.

477
00:22:39,900 --> 00:22:43,089
Then you have the opportunity that people pick up that model

478
00:22:43,219 --> 00:22:47,180
rather quickly because by direct communication, they ask, you know,

479
00:22:47,189 --> 00:22:50,290
can I can you show me the modelling and put it in the code.

480
00:22:50,390 --> 00:22:52,010
So of course, it goes much more

481
00:22:52,290 --> 00:22:56,780
quickly into code than if you are at the university of whatever, you know,

482
00:22:56,790 --> 00:22:59,829
and then you publish something and it takes much longer until

483
00:22:59,839 --> 00:23:02,920
somebody notices it and somebody puts it in their code.

484
00:23:02,930 --> 00:23:03,719
And then in

485
00:23:04,130 --> 00:23:06,949
that respect, NASA was was clearly a kind of,

486
00:23:07,540 --> 00:23:10,140
uh, you know, accelerating factor in that

487
00:23:10,319 --> 00:23:11,589
in that whole game.

488
00:23:11,849 --> 00:23:13,819
But I, I mean, I did have that

489
00:23:14,050 --> 00:23:19,020
kind of direct or indirect feedback that some of the you know, the the

490
00:23:19,510 --> 00:23:22,469
the the professionals. Let me put it in turbulence modelling.

491
00:23:22,479 --> 00:23:26,219
Uh, they they looked a little bit, you know, surprised at this,

492
00:23:26,819 --> 00:23:31,449
uh, insurrection there and about, you know, looking at the the citation counts.

493
00:23:31,459 --> 00:23:32,439
Over the years, you know,

494
00:23:32,449 --> 00:23:35,489
people had worked their entire life on turbulence modelling.

495
00:23:35,500 --> 00:23:37,180
And this guy comes along, you know,

496
00:23:37,189 --> 00:23:40,819
and has probably 10 times or even more citations on a single paper.

497
00:23:40,829 --> 00:23:41,050
And I said

498
00:23:41,390 --> 00:23:46,239
so, I. I think not everybody took that with, you know, uh, delight.

499
00:23:46,250 --> 00:23:49,160
Let me put it that way. But, you know, that's the way it is.

500
00:23:50,020 --> 00:23:51,359
So it it spread.

501
00:23:52,030 --> 00:23:55,290
Was it kind of an organic thing. Did you start getting email? Well,

502
00:23:55,660 --> 00:23:57,920
yeah, emails or messages from

503
00:23:58,849 --> 00:24:02,400
people wanting to implement it. You know, like like now

504
00:24:03,010 --> 00:24:06,390
I suppose on translate is now, if someone comes up with a new model,

505
00:24:06,849 --> 00:24:09,390
it feels like one of the challenges is just

506
00:24:09,849 --> 00:24:11,630
convincing other people

507
00:24:12,079 --> 00:24:13,790
to implement it and test it. You know, it's

508
00:24:15,339 --> 00:24:16,550
I mean, how did you find that

509
00:24:17,229 --> 00:24:19,609
II? I think it was It was easier, because

510
00:24:19,819 --> 00:24:22,209
now there is already models out there, right?

511
00:24:22,219 --> 00:24:25,250
And everybody has their their history on these models.

512
00:24:25,260 --> 00:24:27,689
They have done all their test cases on these models.

513
00:24:27,699 --> 00:24:31,969
So there is a strong reluctance to take up another

514
00:24:31,979 --> 00:24:35,969
model if there is not enormous benefit from it,

515
00:24:35,979 --> 00:24:37,569
which typically isn't the case.

516
00:24:38,260 --> 00:24:39,430
At that time, of course,

517
00:24:39,439 --> 00:24:43,020
nobody had a model which was really working for this application.

518
00:24:43,030 --> 00:24:44,660
So that's also the same with this Spalart–

519
00:24:45,040 --> 00:24:47,300
Allmaras model. I mean, that was also at that time.

520
00:24:47,550 --> 00:24:51,459
I think 92 was the first version of the publication 94. So,

521
00:24:52,109 --> 00:24:53,699
and and it served

522
00:24:53,819 --> 00:24:55,459
a kind of a similar purpose. And

523
00:24:55,900 --> 00:24:59,609
so at that time, I didn't feel it was very difficult.

524
00:24:59,619 --> 00:25:04,099
Actually, I was surprised how quickly. People implemented it into the codes. And

525
00:25:04,209 --> 00:25:08,750
I didn't have to do a hard push on that, either. Being there,

526
00:25:09,510 --> 00:25:13,670
that's amazing. Yeah, but so what about that's often the question people ask is

527
00:25:14,130 --> 00:25:15,359
So you were coming up,

528
00:25:15,780 --> 00:25:18,550
you know? 1990 9294

529
00:25:19,300 --> 00:25:20,079
And then,

530
00:25:20,520 --> 00:25:21,170
Yeah, the Spalart–

531
00:25:21,369 --> 00:25:24,079
Allmaras model—very similar time in the same place. So did you

532
00:25:24,890 --> 00:25:25,709
Did you

533
00:25:25,890 --> 00:25:28,829
interact? Did you see the present? Did you get a sense?

534
00:25:28,839 --> 00:25:30,630
Was there like, a competition?

535
00:25:30,680 --> 00:25:32,189
You know, you could easily think of it in,

536
00:25:32,199 --> 00:25:35,079
like a modern day movie scenario where two people are trying to

537
00:25:35,219 --> 00:25:38,310
get models out. I mean, what was the reality

538
00:25:38,560 --> 00:25:40,219
of the situation that you remember?

539
00:25:40,530 --> 00:25:41,670
Well, I mean, turbulence

540
00:25:42,000 --> 00:25:46,469
models are competitive, and I learned that also from Wilcox;

541
00:25:46,479 --> 00:25:47,910
he was very competitive.

542
00:25:47,920 --> 00:25:50,180
I liked it. Yeah, So he was comp

543
00:25:50,359 --> 00:25:52,380
competitive, actually. And, uh,

544
00:25:52,989 --> 00:25:58,069
and, uh, the the link that I had to this one equation models was actually I mean,

545
00:25:58,079 --> 00:26:02,439
the first one equation model that was kind of circulating as an idea

546
00:26:02,680 --> 00:26:06,089
also came from two very unlikely candidates. So it was the

547
00:26:06,199 --> 00:26:08,189
Baldwin–Barth model at the time,

548
00:26:08,640 --> 00:26:10,280
and they were both numerics guys.

549
00:26:10,410 --> 00:26:13,680
So they had not much of a background in turbulence modelling. But

550
00:26:13,959 --> 00:26:15,849
they asked a reasonable question.

551
00:26:15,859 --> 00:26:19,680
They asked, Well, you know, if I, uh, if I have only a eddy viscosity,

552
00:26:19,689 --> 00:26:22,160
I would need two equations to solve before you

553
00:26:22,290 --> 00:26:23,989
get an eddy viscosity. So

554
00:26:24,329 --> 00:26:27,219
they, uh, came up with a transport equation for the

555
00:26:27,810 --> 00:26:28,280
eddy viscosity. And

556
00:26:28,900 --> 00:26:31,079
the problem with the

557
00:26:31,319 --> 00:26:32,430
eddy-viscosity equation is

558
00:26:32,650 --> 00:26:35,589
you need a sink term, and that sink term doesn't come naturally.

559
00:26:35,599 --> 00:26:39,209
So if you transform k–ω or k–ε

560
00:26:39,319 --> 00:26:39,800
into an eddy

561
00:26:39,979 --> 00:26:41,449
viscosity equation, you don't have an

562
00:26:41,709 --> 00:26:43,020
algebraic sink term.

563
00:26:43,030 --> 00:26:45,790
You have cross diffusion terms and all sorts of things, which is not

564
00:26:46,109 --> 00:26:49,719
which are not so numerically, uh, you know, easy to handle.

565
00:26:49,770 --> 00:26:53,079
So they had a cross-diffusion term as a sink term, which was gradient

566
00:26:53,180 --> 00:26:55,849
eddy viscosity, gradient eddy viscosity,

567
00:26:56,479 --> 00:26:57,869
and and that term, uh,

568
00:26:57,880 --> 00:27:01,910
turned out to be catastrophic near the boundary-layer edge because it had

569
00:27:01,969 --> 00:27:05,300
about the same destructive behaviour near the wall where you wanted it,

570
00:27:05,479 --> 00:27:09,689
as it had at the boundary-layer edge, where it was also going with a large gradient.

571
00:27:09,699 --> 00:27:11,180
And then, if you refine the mesh,

572
00:27:11,569 --> 00:27:13,030
the shear layers would collapse,

573
00:27:13,040 --> 00:27:16,380
and it would have a strange S shaped profiles and things like that.

574
00:27:16,390 --> 00:27:16,579
But

575
00:27:16,849 --> 00:27:18,250
But the idea was out there, and

576
00:27:18,469 --> 00:27:20,930
I actually at the time wrote a small,

577
00:27:20,939 --> 00:27:24,650
also technical memorandum about one equation model.

578
00:27:24,660 --> 00:27:28,310
So the question was, how would you translate more,

579
00:27:28,520 --> 00:27:34,060
uh, more mathematically two equation model into a one equation model? And

580
00:27:34,250 --> 00:27:36,250
of course, you need an additional assumption

581
00:27:36,420 --> 00:27:40,650
to get rid of the second transport equation. Additional assumption is essentially

582
00:27:41,650 --> 00:27:42,939
that omega.

583
00:27:42,989 --> 00:27:46,359
The turbulent turnaround time of the eddies, or frequency of the eddies,

584
00:27:46,369 --> 00:27:48,099
is proportional to the strain rate.

585
00:27:49,959 --> 00:27:53,920
And in shear layers and log layers, that's exact,

586
00:27:54,329 --> 00:27:54,709
so that

587
00:27:55,060 --> 00:27:57,329
if you look at S over omega

588
00:27:57,849 --> 00:27:59,780
in these layers of 0.3 years, so

589
00:28:00,579 --> 00:28:05,420
and so if you make that assumption, you can actually eliminate the second equation.

590
00:28:05,430 --> 00:28:08,089
Get an exact transformation of k–ε

591
00:28:08,300 --> 00:28:10,839
to k–ω, and it has the von Kármán

592
00:28:11,079 --> 00:28:11,199
length

593
00:28:11,300 --> 00:28:12,079
scale in there,

594
00:28:13,270 --> 00:28:14,530
uh, by transformation.

595
00:28:14,540 --> 00:28:17,560
So you get the first derivative divided by the second derivative, Uh,

596
00:28:17,569 --> 00:28:21,500
and, of course, the von Kármán length scale is, yeah, a bit

597
00:28:21,939 --> 00:28:25,489
difficult to implement second derivatives in finite volume codes, Uh,

598
00:28:25,500 --> 00:28:27,079
is a bit fragile.

599
00:28:27,410 --> 00:28:31,609
Uh, if you have non-perfect meshes, it can be a bit, uh,

600
00:28:32,180 --> 00:28:37,099
bumpy. Uh, but, uh, it shows that you you can actually transform these things.

601
00:28:37,109 --> 00:28:37,689
And, uh,

602
00:28:38,170 --> 00:28:40,219
and at the same time, I think Spalart

603
00:28:40,479 --> 00:28:44,890
also picked up that idea. And there's basically a third concept. So

604
00:28:45,069 --> 00:28:49,829
the first source term is the eddy-viscosity gradient squared. The second is eddy

605
00:28:50,010 --> 00:28:53,010
viscosity squared, divided by von Kármán length-scale squared.

606
00:28:53,119 --> 00:28:53,900
And the third

607
00:28:54,089 --> 00:28:54,630
sink term is

608
00:28:55,099 --> 00:28:56,989
eddy viscosity squared by wall distance squared.

609
00:28:57,189 --> 00:28:57,880
And Spalart

610
00:28:58,010 --> 00:29:00,630
I picked the third option there,

611
00:29:01,069 --> 00:29:05,569
uh, which is the least problematic in terms of numerics.

612
00:29:05,579 --> 00:29:10,420
And that's why that model was also fairly easy to implement and is, you know,

613
00:29:10,430 --> 00:29:13,410
numerically relatively easy to handle.

614
00:29:13,800 --> 00:29:15,250
So then it also, you know,

615
00:29:15,260 --> 00:29:19,689
that's the source of this whole development. At the time it was

616
00:29:20,119 --> 00:29:21,380
Baldwin and Barth.

617
00:29:21,540 --> 00:29:25,400
And so Barrett Baldwin, also for some reason, had ended up in our

618
00:29:25,800 --> 00:29:26,479
department.

619
00:29:26,489 --> 00:29:28,959
He was fairly old there, but I had some conversation,

620
00:29:28,969 --> 00:29:31,239
was a really interesting nice guy, actually.

621
00:29:31,829 --> 00:29:34,260
And, uh, very interesting character.

622
00:29:34,959 --> 00:29:36,880
Uh, but that was the source, and that was

623
00:29:37,089 --> 00:29:38,890
later turned out. Uh,

624
00:29:39,180 --> 00:29:42,739
I mean, that was the time when the Iron Curtain was coming down.

625
00:29:43,949 --> 00:29:44,439
Right

626
00:29:46,030 --> 00:29:49,010
now we're putting it up again at great cost.

627
00:29:49,020 --> 00:29:53,520
But at that time, we were putting it down or somebody somebody was pulling it down,

628
00:29:53,530 --> 00:29:54,089
not us.

629
00:29:54,890 --> 00:29:55,689
And, uh

630
00:29:56,339 --> 00:29:59,829
and it turned out that in Russia there was also, uh, Professor

631
00:30:00,589 --> 00:30:01,239
Sekundov’s group.

632
00:30:01,250 --> 00:30:05,250
They had also one equation model, which was actually fairly similar to Spalart’s

633
00:30:05,569 --> 00:30:10,000
model. And I think he even had contact that over over time, then with this group,

634
00:30:10,469 --> 00:30:13,560
and and had some more fruitful interaction with them.

635
00:30:13,920 --> 00:30:17,119
But nobody knew about that. Obviously at the time.

636
00:30:18,030 --> 00:30:19,550
Appeared later then,

637
00:30:20,160 --> 00:30:20,579
yeah,

638
00:30:21,329 --> 00:30:23,229
so But was it then

639
00:30:23,750 --> 00:30:27,060
in terms of at NASA, though, was there Did you feel like a bit of a

640
00:30:28,449 --> 00:30:29,849
You know, if I think now

641
00:30:30,479 --> 00:30:34,469
I don't know, there's some programme meeting or there's some thing and they're

642
00:30:34,719 --> 00:30:37,680
they're wanting to the space shuttle or whatever was Was there ever

643
00:30:37,689 --> 00:30:40,229
that sense of you being in a room and then showing Well,

644
00:30:40,239 --> 00:30:41,270
well, here's the line

645
00:30:41,449 --> 00:30:45,390
with the SST model, and here's the line with the SA. Did that ever

646
00:30:45,660 --> 00:30:46,469
sort of happen.

647
00:30:47,530 --> 00:30:50,390
Oh, yeah, Sure. I mean, you you, of course. You sit. Then

648
00:30:50,810 --> 00:30:54,989
over the decades in in in, you know, hundreds of thousands of meetings

649
00:30:56,560 --> 00:30:58,959
and and and And you look at these curves, right?

650
00:30:58,969 --> 00:31:03,469
And, of course, if the curve is completely wrong, you don't feel so good about it. Uh,

651
00:31:03,670 --> 00:31:05,150
that's clear. Of course.

652
00:31:05,160 --> 00:31:09,739
Over time, it kind of, uh, you know, it dampens out a little bit the anxiety, but, uh,

653
00:31:10,130 --> 00:31:13,310
there there's clearly that that element as said, I mean,

654
00:31:13,319 --> 00:31:15,959
modellers are competitive.

655
00:31:15,969 --> 00:31:17,489
I think anybody is competitive

656
00:31:18,310 --> 00:31:19,869
because what we want to see succeed

657
00:31:20,290 --> 00:31:22,260
and that element is certainly there.

658
00:31:23,849 --> 00:31:24,430
Um,

659
00:31:24,560 --> 00:31:26,699
So you were So you were aims.

660
00:31:27,089 --> 00:31:27,750
So what?

661
00:31:28,219 --> 00:31:29,819
What made you leave aims, then?

662
00:31:29,829 --> 00:31:33,189
Because, I mean well, first of all, what was the time, like

663
00:31:34,150 --> 00:31:36,589
in Palo Alto in Mountain View? Did you kind of?

664
00:31:36,920 --> 00:31:39,140
Because that was in some ways also the Silicon Valley, you know,

665
00:31:39,150 --> 00:31:41,199
at the time as well as other businesses growing, wasn't it?

666
00:31:41,209 --> 00:31:45,069
It was quite a hot time for that area. What was it like being there

667
00:31:45,239 --> 00:31:46,869
in those four or five years?

668
00:31:48,420 --> 00:31:50,130
Well, it was pretty.

669
00:31:50,140 --> 00:31:50,630
Uh I mean,

670
00:31:50,640 --> 00:31:53,109
of course we were outsiders to that whole

671
00:31:53,119 --> 00:31:56,770
business that was developing at the time there.

672
00:31:56,780 --> 00:32:01,839
But it was pretty amazing to see how these companies were growing. You know that

673
00:32:02,010 --> 00:32:03,260
this, uh

674
00:32:03,969 --> 00:32:07,069
dot com and software companies and so forth and

675
00:32:07,500 --> 00:32:11,910
and And they were building complexes of offices in, you know,

676
00:32:11,920 --> 00:32:15,540
in a year there was a huge office there and thousands of people

677
00:32:15,650 --> 00:32:20,750
I could I could not imagine how you actually managed such a company and such a growth,

678
00:32:20,760 --> 00:32:21,010
and

679
00:32:21,199 --> 00:32:23,239
yeah, And then, of course they would.

680
00:32:23,250 --> 00:32:26,969
They would be gone a few years later because somebody else had, you know,

681
00:32:26,979 --> 00:32:28,089
had a better idea,

682
00:32:28,479 --> 00:32:30,500
and and I had nothing to do with it.

683
00:32:30,510 --> 00:32:34,219
I just watched it with a certain amazement, you know, compared to Germany,

684
00:32:34,229 --> 00:32:37,060
where everything stands at the same place for hundreds of years.

685
00:32:37,540 --> 00:32:41,359
And but But that was as much as I had to do with it.

686
00:32:41,369 --> 00:32:42,540
I mean, of course,

687
00:32:42,550 --> 00:32:47,410
everybody at the time had his personal experiences with with the laptops, right?

688
00:32:47,420 --> 00:32:48,569
And with the desktops,

689
00:32:49,060 --> 00:32:52,810
uh, the the which were coming up, and every two years you bought a new one,

690
00:32:52,819 --> 00:32:54,959
and it was twice as fast and so forth,

691
00:32:55,410 --> 00:33:01,280
but with this whole industry I I had very little to do except watch in, you know,

692
00:33:01,290 --> 00:33:02,150
amazement there.

693
00:33:02,160 --> 00:33:02,589
And

694
00:33:03,589 --> 00:33:05,439
And I mean, it was of course,

695
00:33:06,130 --> 00:33:08,329
California is a very nice place,

696
00:33:08,339 --> 00:33:11,630
as we all know and very diverse in terms of what you

697
00:33:11,640 --> 00:33:15,270
can go out and eat and do and young guys and,

698
00:33:16,050 --> 00:33:19,989
uh, sometimes go to San Francisco. I like the countryside, Really.

699
00:33:20,000 --> 00:33:24,430
I mean, I I'm more a country boy than a city boy and so going to the

700
00:33:24,739 --> 00:33:28,109
Sierra Nevada and going to the ocean and, you know,

701
00:33:28,119 --> 00:33:30,229
the redwood forest and that kind of thing.

702
00:33:30,239 --> 00:33:30,630
So

703
00:33:30,930 --> 00:33:32,310
I like that.

704
00:33:33,189 --> 00:33:39,459
And of course I'm and and And that's maybe one of the more essential parts of my being.

705
00:33:39,859 --> 00:33:39,869
Uh,

706
00:33:39,880 --> 00:33:45,319
I come from Bavaria and southern Bavaria that is very close to the mountains here,

707
00:33:45,819 --> 00:33:46,689
and, uh,

708
00:33:46,880 --> 00:33:49,869
we have specific characteristics, so

709
00:33:50,000 --> 00:33:51,410
people always come back.

710
00:33:52,219 --> 00:33:52,910
Mm.

711
00:33:53,599 --> 00:33:57,020
So if I If I look at my my friends and my family,

712
00:33:57,030 --> 00:34:02,020
I think the average radius people have moved is less than five kilometres Really?

713
00:34:02,369 --> 00:34:03,000
Truly.

714
00:34:03,010 --> 00:34:07,380
I mean, if somebody moves more than 10 kilometres, something wrong with them,

715
00:34:10,648 --> 00:34:16,059
and, uh and so I was a bit the exception for, you know, being gone for 15 years or so.

716
00:34:17,099 --> 00:34:21,349
But you never lose that kind of idea in the back

717
00:34:21,358 --> 00:34:24,947
of your mind that eventually you're going to go back and

718
00:34:25,438 --> 00:34:27,898
and and, of course, you socialise also with people.

719
00:34:27,908 --> 00:34:32,888
So in California, I met some, uh, there was a restaurant Bavarian restaurant.

720
00:34:32,898 --> 00:34:33,618
So, you know,

721
00:34:33,829 --> 00:34:37,628
eventually ended up in that place and had my my rice beer there. And,

722
00:34:38,129 --> 00:34:42,040
uh, yeah, there was a kind of a group of people from from that area.

723
00:34:42,050 --> 00:34:43,810
So, you know, it's it's it's like it is with

724
00:34:43,929 --> 00:34:43,949
you

725
00:34:44,120 --> 00:34:44,590
kind

726
00:34:44,699 --> 00:34:44,760
of

727
00:34:45,290 --> 00:34:47,590
immigrant. So you look for

728
00:34:47,728 --> 00:34:49,159
for people of your own

729
00:34:49,389 --> 00:34:50,030
background and

730
00:34:50,449 --> 00:34:50,879
origin.

731
00:34:51,610 --> 00:34:52,949
So you were there for

732
00:34:53,290 --> 00:34:56,679
five years, and then what? So what was the thing that made you

733
00:34:56,820 --> 00:34:59,939
leave? I mean, was there a temptation to stay at

734
00:35:00,310 --> 00:35:02,080
NASA to you know,

735
00:35:02,280 --> 00:35:04,389
um, at that time, what? What was the

736
00:35:05,510 --> 00:35:06,929
so driving factor?

737
00:35:08,040 --> 00:35:08,889
Well, I,

738
00:35:09,379 --> 00:35:12,610
I never had the idea to stay in the US forever.

739
00:35:13,020 --> 00:35:18,010
So from the beginning, I had actually I had thought to stay much shorter,

740
00:35:18,020 --> 00:35:20,209
maybe two years, maybe Max, three years.

741
00:35:20,419 --> 00:35:23,330
There was a bit of a recession at the time in Germany, at least,

742
00:35:23,340 --> 00:35:26,800
and it was kind of hard to get a, you know, a good job

743
00:35:26,929 --> 00:35:29,010
from the distance on top of that.

744
00:35:29,439 --> 00:35:30,659
So I stayed a bit longer.

745
00:35:30,870 --> 00:35:33,449
Of course, I was also not an American citizen.

746
00:35:33,459 --> 00:35:36,139
So you're basically a contractor, And, uh,

747
00:35:36,149 --> 00:35:38,790
the contracting was also kind of shaky there, you know,

748
00:35:38,800 --> 00:35:40,830
it goes from this company to that company.

749
00:35:40,840 --> 00:35:42,889
Kind of be pushed around a bit and so forth.

750
00:35:43,520 --> 00:35:48,139
And so it didn't look like, really a very attractive career path. Maybe, you know,

751
00:35:48,540 --> 00:35:49,020
as as

752
00:35:49,709 --> 00:35:51,989
staying there as contractor for forever. So

753
00:35:52,520 --> 00:35:56,530
and I I felt also eventually, you know, I had done my thing there,

754
00:35:56,540 --> 00:35:59,520
and there was time to, you know, to move on.

755
00:35:59,530 --> 00:35:59,989
I guess.

756
00:36:01,250 --> 00:36:03,219
So what was the decision? Because, I guess

757
00:36:04,159 --> 00:36:09,449
Did you ever think about academia like, actually going in in a more like pure,

758
00:36:10,040 --> 00:36:10,570
you know,

759
00:36:10,699 --> 00:36:13,899
professor role, essentially going down the academic track.

760
00:36:13,909 --> 00:36:15,459
Was that something you ever considered?

761
00:36:15,949 --> 00:36:19,070
Yeah, I did have that in the back of my mind too,

762
00:36:19,080 --> 00:36:22,520
but I didn't actively pursue that at that point.

763
00:36:22,530 --> 00:36:26,899
A little bit later, I did try. But at that point

764
00:36:27,399 --> 00:36:31,699
I essentially tried to get back to Germany and find a job and

765
00:36:32,280 --> 00:36:34,000
and there was actually coincidence there.

766
00:36:34,010 --> 00:36:39,209
I mean, sometimes there's really unbelievable things happening in life. So I was

767
00:36:39,750 --> 00:36:43,860
in my office and I had a Chinese friend, George Wong from Taiwan,

768
00:36:44,550 --> 00:36:45,969
colleague, and

769
00:36:46,169 --> 00:36:48,250
he came to my office and he had a letter.

770
00:36:48,260 --> 00:36:50,929
He had some correspondence with somebody from Germany

771
00:36:51,969 --> 00:36:54,209
and he showed me the letter and said, Do you know the guy? It said Georg Scheuerer.

772
00:36:54,540 --> 00:36:54,770
I

773
00:36:55,959 --> 00:36:59,780
said, Well, I know the name. He did some turbulence work with Rodi,

774
00:37:00,340 --> 00:37:01,699
but I didn't know the guy,

775
00:37:02,530 --> 00:37:03,419
and uh

776
00:37:03,909 --> 00:37:08,000
and then I read, OK, there is a CFD company, and it’s right next to my hometown.

777
00:37:09,469 --> 00:37:11,350
I wanted to go in the first place,

778
00:37:11,530 --> 00:37:13,919
you know how how big are the chances that, you know,

779
00:37:13,929 --> 00:37:16,239
somewhere on the planet there is a CFD company

780
00:37:16,250 --> 00:37:19,439
which you can reach almost there in 15–20 minutes.

781
00:37:20,209 --> 00:37:23,520
And so I said, George, I'm gonna work there, you know,

782
00:37:23,760 --> 00:37:26,080
that's my place. You know, that's destiny here.

783
00:37:26,780 --> 00:37:31,530
And, uh so I I moved to Germany and, of course, I. I contacted, uh,

784
00:37:32,270 --> 00:37:32,899
Georg and

785
00:37:33,090 --> 00:37:34,560
sent my resume and everything, but

786
00:37:34,800 --> 00:37:38,600
it was a very small company at the time, so he had maybe five or six people

787
00:37:39,300 --> 00:37:41,280
and clearly he didn't have an opening.

788
00:37:42,050 --> 00:37:43,179
And, uh, you know,

789
00:37:43,189 --> 00:37:48,070
it's not so easy for a small company to come up with funding for an additional person.

790
00:37:48,080 --> 00:37:49,600
And there was no project, nothing.

791
00:37:49,989 --> 00:37:55,310
So but I kept on bugging him, obviously, because I had made up my mind and, uh,

792
00:37:56,129 --> 00:37:57,820
eventually I got a bit lucky.

793
00:37:57,830 --> 00:38:01,750
So we met, I think, for lunch or so and he said, Well, we have our

794
00:38:02,429 --> 00:38:03,669
our users meeting,

795
00:38:05,020 --> 00:38:08,090
and so we all the customers would come. It wasn't big.

796
00:38:08,100 --> 00:38:10,439
It was maybe 50 people or something like that.

797
00:38:10,860 --> 00:38:11,280
I said,

798
00:38:11,419 --> 00:38:13,310
I'm going to give a presentation there, OK,

799
00:38:13,520 --> 00:38:17,540
he said, Oh, yeah, that's great. You know, somebody from NASA gives a presentation.

800
00:38:17,560 --> 00:38:19,010
I use a meeting. Let's do that. So

801
00:38:19,179 --> 00:38:20,250
I gave a presentation,

802
00:38:20,260 --> 00:38:23,510
and then apparently some of his customers approached him and said,

803
00:38:23,689 --> 00:38:25,239
Why don't you hire the guy you know?

804
00:38:27,899 --> 00:38:29,080
And, uh so E.

805
00:38:29,090 --> 00:38:32,120
Eventually I ended up in that small company,

806
00:38:32,129 --> 00:38:34,639
which was called Advanced Scientific Computing.

807
00:38:34,649 --> 00:38:36,239
Uh, the code was task flow,

808
00:38:36,939 --> 00:38:37,610
uh, turbo

809
00:38:37,909 --> 00:38:39,090
machinery code essentially,

810
00:38:39,100 --> 00:38:44,679
and it was originated from the university in Waterloo, Canada, near Toronto.

811
00:38:45,629 --> 00:38:51,010
And, uh, I said it was a fairly small company. The the main office was in, in, in, in

812
00:38:51,120 --> 00:38:51,959
Canada.

813
00:38:52,909 --> 00:38:53,699
And

814
00:38:54,300 --> 00:38:55,969
of course, you can't have everything.

815
00:38:55,979 --> 00:39:00,139
So I didn't really get, of course in a small company to get a research job, So I had to

816
00:39:00,310 --> 00:39:01,780
build up a group.

817
00:39:01,790 --> 00:39:07,280
So my boss wanted to have a development group because

818
00:39:07,290 --> 00:39:10,060
he felt it's much better to interact with customers.

819
00:39:10,070 --> 00:39:11,459
You know, if somebody can look at the code.

820
00:39:11,969 --> 00:39:13,810
But the Canadian company didn't want to hire

821
00:39:13,820 --> 00:39:16,100
anybody in Germany because we were too expensive.

822
00:39:16,989 --> 00:39:17,850
In Canada.

823
00:39:17,860 --> 00:39:21,850
They they get tax refunds and all sorts of things, so they didn't want to do that. So

824
00:39:21,979 --> 00:39:26,010
my job was really to to have acquire external projects,

825
00:39:26,739 --> 00:39:30,520
European projects, German research projects, customer projects.

826
00:39:30,790 --> 00:39:31,919
So external funding.

827
00:39:32,479 --> 00:39:35,729
I didn't have to make a profit, but I essentially had to pay for everybody.

828
00:39:35,739 --> 00:39:37,120
I would hire through that

829
00:39:37,409 --> 00:39:38,419
through that venue,

830
00:39:39,350 --> 00:39:43,219
and so I really didn't do any programming or anything like that.

831
00:39:43,229 --> 00:39:47,610
So, of course, the bias of the project was then going more and more into turbulence,

832
00:39:47,620 --> 00:39:51,879
modelling naturally, and I had them People work on turbulence modelling,

833
00:39:51,889 --> 00:39:53,189
and I could interact with them.

834
00:39:53,199 --> 00:39:55,260
And they developed the theory and

835
00:39:55,520 --> 00:39:57,500
they put it in the code. And then we worked on that.

836
00:39:57,899 --> 00:40:01,439
So that's how that developed. And I did that actually, fairly long.

837
00:40:01,449 --> 00:40:03,080
Maybe 10 years or so,

838
00:40:04,100 --> 00:40:05,949
Uh, in that function, I

839
00:40:06,290 --> 00:40:10,709
think we had at the end, of course, once the people had their project experience,

840
00:40:10,719 --> 00:40:12,070
we could channel them back

841
00:40:12,370 --> 00:40:14,669
into the main development, because then

842
00:40:14,770 --> 00:40:15,620
the cost

843
00:40:15,879 --> 00:40:17,550
factor was no longer relevant.

844
00:40:17,929 --> 00:40:21,580
So we built up maybe something like 1012 people over time in that

845
00:40:21,590 --> 00:40:26,120
office as a development and still exist as a development office today.

846
00:40:27,419 --> 00:40:28,899
So what was then?

847
00:40:29,229 --> 00:40:31,969
Because I I'm interested to know the history then. So this

848
00:40:32,090 --> 00:40:34,760
this happened for About what? Tenish years?

849
00:40:35,219 --> 00:40:38,239
So what? Where did you go to next? How what was this?

850
00:40:38,679 --> 00:40:40,320
I mean, we know where you are now.

851
00:40:40,790 --> 00:40:41,739
So where was

852
00:40:41,909 --> 00:40:43,959
I know? There’s a bit of a history with Ansys

853
00:40:44,189 --> 00:40:44,199
and

854
00:40:44,370 --> 00:40:47,370
acquisitions and different companies. So how did you

855
00:40:47,679 --> 00:40:49,139
end up being in the,

856
00:40:49,709 --> 00:40:51,479
um, family,

857
00:40:52,300 --> 00:40:52,750
so to speak?

858
00:40:53,550 --> 00:40:55,229
Well, I had made up my mind.

859
00:40:55,239 --> 00:40:59,330
I'm not going to leave here anymore, so I just stayed there companies coming,

860
00:41:00,120 --> 00:41:00,939
you know, And I sit there

861
00:41:01,370 --> 00:41:02,080
and stay,

862
00:41:02,300 --> 00:41:07,320
and everything else was just acquisitions. So we were bought up, then, by the

863
00:41:07,919 --> 00:41:10,110
Atomic Energy Authority in England.

864
00:41:10,120 --> 00:41:14,239
They developed the CFX-4 code for nuclear-safety applications. And

865
00:41:14,989 --> 00:41:17,090
it was the time of privatisation,

866
00:41:17,780 --> 00:41:21,409
right? So the British government had decided to float the

867
00:41:21,530 --> 00:41:22,370
AEA

868
00:41:22,739 --> 00:41:24,330
research lab

869
00:41:24,580 --> 00:41:26,629
into the into the market,

870
00:41:27,010 --> 00:41:28,969
and that was pretty much a disaster.

871
00:41:29,340 --> 00:41:29,389
You know,

872
00:41:30,500 --> 00:41:33,729
they had everything and anything, and they

873
00:41:34,620 --> 00:41:36,419
no clue what to do with it.

874
00:41:36,429 --> 00:41:42,810
And, uh, so we were always on the brink of financial collapse and didn't make a lot of

875
00:41:43,129 --> 00:41:45,199
money because there was no investment because they

876
00:41:45,209 --> 00:41:47,469
didn't understand the business and so forth.

877
00:41:47,479 --> 00:41:49,600
So it was It was not a good place to be.

878
00:41:50,270 --> 00:41:54,429
And then, fortunately, we were eventually bought by Ansys, which, of course,

879
00:41:54,439 --> 00:41:56,270
was a hardcore software company.

880
00:41:56,280 --> 00:41:58,090
And they understood the business. And

881
00:41:58,350 --> 00:41:58,689
they,

882
00:41:59,600 --> 00:41:59,620
uh

883
00:41:59,729 --> 00:42:03,100
they made a difference for us. And and, uh so we were quite happy.

884
00:42:03,699 --> 00:42:07,649
And, of course, then came the Big Bang and Ansys bought Fluent, which, of course,

885
00:42:07,659 --> 00:42:09,030
was our main enemy.

886
00:42:09,040 --> 00:42:09,790
Right. It was the

887
00:42:10,810 --> 00:42:13,989
was the big battleship. And it was the small Corvette. And, uh,

888
00:42:14,590 --> 00:42:18,030
and so that that was certainly a culture shock for, I think,

889
00:42:18,040 --> 00:42:20,449
for everybody fluent included, you know?

890
00:42:20,459 --> 00:42:20,870
And it

891
00:42:21,090 --> 00:42:23,600
took obviously quite a bit of time

892
00:42:24,139 --> 00:42:29,709
to to to straighten things out. But over time, you know, these things settle down.

893
00:42:29,719 --> 00:42:32,280
And I think we have a very good integrated

894
00:42:32,570 --> 00:42:34,639
unit after maybe some five years or so.

895
00:42:35,389 --> 00:42:37,419
Yeah. OK, so then

896
00:42:37,879 --> 00:42:40,320
so would that be fair to say that, then?

897
00:42:41,669 --> 00:42:45,169
So you did, obviously. You know, the SST model comes out 94.

898
00:42:46,010 --> 00:42:49,550
Then I guess you're slightly more on the

899
00:42:50,500 --> 00:42:53,919
project Based, Uh, well, I mean, from what you're saying,

900
00:42:55,110 --> 00:42:56,360
it was mainly about a

901
00:42:56,760 --> 00:43:00,330
personal reason, right? You wanted to go back home essentially, you wanted to be.

902
00:43:00,340 --> 00:43:00,989
And I can

903
00:43:01,409 --> 00:43:03,580
sort of emphasise that like I live in Oxfordshire.

904
00:43:03,590 --> 00:43:06,909
And people always say, Why did you work for Oxford University?

905
00:43:06,919 --> 00:43:09,209
And I genuinely said, It's nothing to do with. I

906
00:43:09,570 --> 00:43:11,969
just wanted to live here. I didn't want to go anywhere else,

907
00:43:12,219 --> 00:43:17,479
and I don't want to move. So it's that or nothing. Um, so I guess the same for you.

908
00:43:17,489 --> 00:43:18,570
You could have gone

909
00:43:19,020 --> 00:43:20,360
many other places,

910
00:43:21,080 --> 00:43:23,370
but actually, it was about you wanting to stay.

911
00:43:24,959 --> 00:43:29,080
Exactly. So that was mostly a private decision. And, of course,

912
00:43:29,199 --> 00:43:30,919
with a portion of luck. Because,

913
00:43:31,469 --> 00:43:31,830
of course,

914
00:43:31,840 --> 00:43:35,080
I wouldn't have wanted to work for an outfit that I

915
00:43:35,090 --> 00:43:38,610
wouldn't want to work for something that wouldn't be interesting,

916
00:43:38,620 --> 00:43:39,159
so to speak.

917
00:43:39,169 --> 00:43:39,479
But

918
00:43:39,729 --> 00:43:43,159
that turned out to be quite interesting. And so why leave?

919
00:43:43,169 --> 00:43:44,760
You know, there was nothing wrong with it.

920
00:43:44,770 --> 00:43:48,159
And over time, things developed quite quite well.

921
00:43:48,840 --> 00:43:50,679
So that's so you were doing a lot of turbo

922
00:43:50,979 --> 00:43:52,810
machinery. Um,

923
00:43:52,919 --> 00:43:54,669
work or or some turbo

924
00:43:55,010 --> 00:43:55,449
machinery.

925
00:43:56,379 --> 00:43:57,760
So what was the

926
00:43:57,889 --> 00:43:59,229
it sounded like then? For a bit?

927
00:43:59,239 --> 00:44:03,679
You weren't maybe on hardcore research as much on the turbulence-modelling side.

928
00:44:04,429 --> 00:44:05,530
And then I guess

929
00:44:06,870 --> 00:44:07,489
you know,

930
00:44:08,139 --> 00:44:09,510
you've done the SST.

931
00:44:09,870 --> 00:44:13,530
That was already a very well known model. I guess you know, it was growing.

932
00:44:13,540 --> 00:44:14,850
It was being implemented in code.

933
00:44:15,679 --> 00:44:18,270
So what, then? Fast forward, I guess.

934
00:44:18,600 --> 00:44:20,860
What was the next big thing? Because I guess two,

935
00:44:21,340 --> 00:44:21,489
at

936
00:44:21,610 --> 00:44:24,419
least from my eyes, what I've seen is one

937
00:44:25,149 --> 00:44:29,340
the transition modelling, which I think is, you know, had a huge impact.

938
00:44:29,739 --> 00:44:33,149
Um and then obviously more the scale resolving side. So

939
00:44:33,360 --> 00:44:36,010
on the transition modelling side. Where where did that

940
00:44:36,629 --> 00:44:37,409
come from?

941
00:44:37,419 --> 00:44:41,389
You know, did you purposely think this is an interesting problem I want to get into

942
00:44:41,580 --> 00:44:44,530
What? What was the drive to, you know, focus in that area?

943
00:44:46,209 --> 00:44:50,399
Yeah. I mean, the transition modelling was the big black hole in CFD, right?

944
00:44:50,409 --> 00:44:52,229
I mean, we had everything and anything,

945
00:44:52,239 --> 00:44:55,879
a model for everything and anything in the code Multiphase, combustion,

946
00:44:55,889 --> 00:44:56,939
radiation, whatever.

947
00:44:57,899 --> 00:44:58,639
And of course,

948
00:44:58,649 --> 00:45:02,469
there were specific transition models for aeronautics—the e^N method.

949
00:45:02,510 --> 00:45:05,739
But they were not code compatible, really, with an industrial code.

950
00:45:06,350 --> 00:45:09,409
And and and And And I learned later that Spalding had

951
00:45:09,419 --> 00:45:13,110
given a talk and had had said in 19 seventies,

952
00:45:13,120 --> 00:45:14,550
Well, eventually we're gonna also have

953
00:45:14,820 --> 00:45:17,919
transition models. Right? And that was 19 seventies.

954
00:45:18,620 --> 00:45:19,080
And

955
00:45:19,250 --> 00:45:20,760
I remember we had

956
00:45:21,820 --> 00:45:21,830
a

957
00:45:22,050 --> 00:45:24,979
European project. It wasn't really a project was

958
00:45:26,040 --> 00:45:27,739
we had some funding to meet

959
00:45:28,949 --> 00:45:30,489
and there was a meeting every half a year,

960
00:45:30,500 --> 00:45:33,250
and it was mostly institutions which were working on transition.

961
00:45:33,260 --> 00:45:35,530
And somehow I was also because I had they

962
00:45:35,540 --> 00:45:38,149
built up several connections into the different community.

963
00:45:38,159 --> 00:45:39,889
I was there, but I didn't have a project,

964
00:45:40,229 --> 00:45:42,090
so we would meet every half a year

965
00:45:42,550 --> 00:45:45,300
and everybody would present what they're doing on transition.

966
00:45:45,310 --> 00:45:50,030
And after three years, there was nothing that was just the same as before, as always,

967
00:45:50,040 --> 00:45:51,489
in transition modelling.

968
00:45:52,270 --> 00:45:52,969
And

969
00:45:53,780 --> 00:45:54,780
then I had this.

970
00:45:54,959 --> 00:45:56,679
It’s a small idea.

971
00:45:57,020 --> 00:46:01,429
You have to relate something local quantity to these integral

972
00:46:01,479 --> 00:46:04,840
quantities that we know how they correlate with transition.

973
00:46:05,449 --> 00:46:06,020
And,

974
00:46:06,030 --> 00:46:09,060
uh so So the Vorticity Reynolds number was obviously

975
00:46:09,070 --> 00:46:12,850
the the key element in in in recognising that vorticity

976
00:46:12,860 --> 00:46:15,830
Reynolds number is proportional to the maximum of the

977
00:46:15,840 --> 00:46:17,899
of that number in the boundary is proportional to the

978
00:46:18,030 --> 00:46:19,800
momentum thickness Reynolds number.

979
00:46:20,320 --> 00:46:20,909
And

980
00:46:21,360 --> 00:46:22,919
and then I realised you could do something.

981
00:46:22,929 --> 00:46:25,909
It was actually one of these meetings, so I walked around, you know, I was thinking,

982
00:46:25,919 --> 00:46:27,530
Oh, wow, that could actually work, you know?

983
00:46:27,540 --> 00:46:27,929
And

984
00:46:28,149 --> 00:46:30,159
I remember I was sitting there. It was something

985
00:46:30,330 --> 00:46:30,340
I

986
00:46:30,889 --> 00:46:32,870
think, protocol in this upon. And

987
00:46:33,310 --> 00:46:35,489
I was drinking these small bottles of wine, you know,

988
00:46:35,500 --> 00:46:36,939
scribbling wrong and drinking.

989
00:46:36,979 --> 00:46:39,879
And eventually I was so drunk I could hardly find my hotel.

990
00:46:41,939 --> 00:46:43,050
That sounds like a T

991
00:46:43,330 --> 00:46:44,239
modelling conference.

992
00:46:45,320 --> 00:46:46,959
So I was

993
00:46:47,060 --> 00:46:48,790
carried away on that. But

994
00:46:49,570 --> 00:46:51,870
then I had a student,

995
00:46:54,120 --> 00:46:57,489
Sławomir Kubacki from Poland, to work, you know, for half a year.

996
00:46:57,500 --> 00:47:00,110
And we made a very simple version of that model and

997
00:47:00,250 --> 00:47:02,760
had a ETMM conference paper on that.

998
00:47:03,689 --> 00:47:04,540
And, uh

999
00:47:04,909 --> 00:47:07,189
but then, of course, I had no funding. And

1000
00:47:07,479 --> 00:47:10,070
the problem is, with the job I had, you could get funding,

1001
00:47:10,080 --> 00:47:12,459
but not for the things you wanted to do because you had to

1002
00:47:12,560 --> 00:47:14,550
respond to some. Whatever calls so

1003
00:47:15,699 --> 00:47:21,320
And the company also didn't have enough interest to to to pay for it at the time.

1004
00:47:22,439 --> 00:47:25,780
So what I did, actually, I wrote a letter

1005
00:47:26,469 --> 00:47:27,280
to all turbo

1006
00:47:27,439 --> 00:47:29,050
machinery companies on the planet.

1007
00:47:29,889 --> 00:47:31,659
Not all of them, but the big ones.

1008
00:47:31,780 --> 00:47:35,629
GE and Pratt & Whitney and Rolls-Royce and so forth.

1009
00:47:36,500 --> 00:47:38,739
And customers are not. I just

1010
00:47:39,409 --> 00:47:40,800
sent an email, a letter,

1011
00:47:41,610 --> 00:47:46,399
and it took a while, and it was a bit back and forth and eventually GE decided,

1012
00:47:46,409 --> 00:47:47,330
You know that

1013
00:47:47,620 --> 00:47:51,989
that that of course, SST help name recognition help, obviously,

1014
00:47:52,459 --> 00:47:55,280
and they decided, Well, let's let's give it a shot.

1015
00:47:55,290 --> 00:47:59,540
And then they funded it for, I think, three years and we hired Robin Langtry,

1016
00:47:59,899 --> 00:48:04,919
who was a Canadian. He had already worked on similar ideas in Canada,

1017
00:48:05,020 --> 00:48:07,239
So he had a good flying start and

1018
00:48:07,520 --> 00:48:08,780
and that was then the γ–Reθ

1019
00:48:08,959 --> 00:48:09,540
model.

1020
00:48:09,550 --> 00:48:14,699
Eventually, which resulted from his PhD thesis that he wrote on on that topic. And

1021
00:48:15,040 --> 00:48:19,270
of course, these models then multiplied and multiplied, so there’s a whole

1022
00:48:19,929 --> 00:48:20,879
of different models. But

1023
00:48:21,100 --> 00:48:22,989
the idea is essentially always the same, right?

1024
00:48:23,000 --> 00:48:26,610
You have an indicator function and a triggering function, and then eventually

1025
00:48:26,739 --> 00:48:29,580
you trip it into going into going turbulent.

1026
00:48:30,090 --> 00:48:32,159
But that was that was a much more

1027
00:48:33,050 --> 00:48:36,250
challenging problem, obviously compared to SST,

1028
00:48:37,020 --> 00:48:38,800
because in SST, you know,

1029
00:48:38,810 --> 00:48:42,889
I had already elements there which could needed combining.

1030
00:48:42,899 --> 00:48:47,419
But in that case, there wasn't all that much of elements out there how to do that.

1031
00:48:47,989 --> 00:48:48,620
And, uh

1032
00:48:49,250 --> 00:48:52,330
and of course, it's extremely fragile physical process,

1033
00:48:52,340 --> 00:48:55,169
which makes it also fragile numerically so you can

1034
00:48:55,919 --> 00:48:57,590
have all sorts of problems, and and

1035
00:48:58,129 --> 00:49:01,120
and so we went back, you know, starting with this γ–Reθ

1036
00:49:01,389 --> 00:49:03,919
model. And then we had the gamma one-equation model.

1037
00:49:03,929 --> 00:49:08,560
And now we have this algebraic model, and every time you have a bit of an idea go back.

1038
00:49:08,570 --> 00:49:09,989
And every time you regret it,

1039
00:49:10,360 --> 00:49:15,090
because the effort is much higher than you think is every time you touch it,

1040
00:49:15,100 --> 00:49:15,510
you know it.

1041
00:49:15,639 --> 00:49:18,550
It starts to accumulate time.

1042
00:49:19,080 --> 00:49:19,090
Uh,

1043
00:49:19,270 --> 00:49:23,689
but overall, I mean, it's it's pretty, pretty fascinating that

1044
00:49:23,949 --> 00:49:25,659
now we have an algebraic model,

1045
00:49:26,389 --> 00:49:30,600
and it can essentially do bypass transition, natural transition, separation,

1046
00:49:30,610 --> 00:49:31,699
induced transition and cross

1047
00:49:31,810 --> 00:49:32,969
flow transition by now,

1048
00:49:33,270 --> 00:49:33,280
uh,

1049
00:49:33,580 --> 00:49:37,050
just an algebraic equation. It costs nothing

1050
00:49:37,260 --> 00:49:42,879
to to numerically do that really And, uh, and and that all on on on local quantities.

1051
00:49:42,889 --> 00:49:44,810
So it's beyond actually what

1052
00:49:45,340 --> 00:49:47,209
the hope had been at the time.

1053
00:49:47,820 --> 00:49:49,659
And, of course, accuracy

1054
00:49:50,090 --> 00:49:52,620
is RANS. I mean, it’s not

1055
00:49:53,689 --> 00:49:56,919
the last 5% and sometimes it's it's worse,

1056
00:49:56,929 --> 00:49:59,399
but at least it gives you the first order effect.

1057
00:49:59,409 --> 00:49:59,620
And

1058
00:50:00,419 --> 00:50:05,199
we have a lot of customers, which use it in very different applications from

1059
00:50:05,340 --> 00:50:05,560
wind turbines.

1060
00:50:05,739 --> 00:50:07,810
And there’s a huge range of applications: turbo-

1061
00:50:08,179 --> 00:50:08,610
machinery, wind turbines,

1062
00:50:09,610 --> 00:50:14,669
Formula One cars, you know, it's it's it's drones, I'm certain

1063
00:50:15,550 --> 00:50:17,110
have the same type of problems,

1064
00:50:17,639 --> 00:50:21,239
so it’s filled kind of a niche there.

1065
00:50:22,050 --> 00:50:24,080
I remember that being, uh,

1066
00:50:25,290 --> 00:50:28,080
a pretty key moment. I remember working in

1067
00:50:28,780 --> 00:50:32,840
you know, people were waiting to have that implemented in the code, or or, you know,

1068
00:50:32,850 --> 00:50:33,600
or that to be.

1069
00:50:33,610 --> 00:50:36,570
I think there was a little bit of a controversy. Well, not controversy that

1070
00:50:36,989 --> 00:50:39,110
I don't think you were able to fully publish it.

1071
00:50:39,459 --> 00:50:40,610
This is always a

1072
00:50:41,449 --> 00:50:42,719
like Was this always

1073
00:50:42,820 --> 00:50:42,830
a,

1074
00:50:43,489 --> 00:50:45,830
You know, a challenge for you. In a way.

1075
00:50:45,840 --> 00:50:49,429
I think it's always a sensitive point of like, At what point

1076
00:50:49,899 --> 00:50:53,199
do you fully make something open? At what point is it commercial like?

1077
00:50:55,250 --> 00:50:57,620
Yeah, of course. That’s a—that’s

1078
00:50:57,729 --> 00:50:57,739
a

1079
00:50:57,840 --> 00:50:59,709
field of, uh

1080
00:50:59,899 --> 00:51:01,699
that we we live in, right.

1081
00:51:01,709 --> 00:51:04,939
We we we are a company like any other company and and also that

1082
00:51:05,090 --> 00:51:08,979
that the project was funded by A by a company. So,

1083
00:51:09,189 --> 00:51:10,540
uh, you know, they were not

1084
00:51:10,760 --> 00:51:14,469
that we would go out and publish it the next day,

1085
00:51:14,800 --> 00:51:15,590
So

1086
00:51:15,760 --> 00:51:19,350
we made a kind of a compromise. I think it was a fair compromise.

1087
00:51:19,600 --> 00:51:21,830
We published the basic idea, right?

1088
00:51:21,929 --> 00:51:24,870
So So we said, OK, that's the idea behind the model.

1089
00:51:25,370 --> 00:51:30,620
And then we didn't publish the correlations that we were using, so it was clear

1090
00:51:30,729 --> 00:51:32,320
people would pick it up and

1091
00:51:32,540 --> 00:51:38,709
recalibrate it and re invent these calibration. These correlations there

1092
00:51:39,020 --> 00:51:42,639
and it's clear these things they have, they give you maybe

1093
00:51:42,760 --> 00:51:43,300
a five

1094
00:51:43,510 --> 00:51:43,879
year,

1095
00:51:44,659 --> 00:51:47,639
if you like, a little bit more time scale,

1096
00:51:47,649 --> 00:51:50,209
and then eventually somebody's going to reconstruct it,

1097
00:51:50,449 --> 00:51:53,479
and I think everybody accepts that. But of course, on the other hand,

1098
00:51:53,729 --> 00:51:57,199
company pays a salary, so you have to also provide some, you know,

1099
00:51:57,209 --> 00:52:00,850
some benefit and some commercial advantage to that company.

1100
00:52:00,860 --> 00:52:01,080
And

1101
00:52:01,969 --> 00:52:05,300
we try to do our best to balance things there.

1102
00:52:05,310 --> 00:52:09,350
Of course, from the outside, academics are sometimes a bit frustrated,

1103
00:52:09,360 --> 00:52:11,320
same as SST clearly, and

1104
00:52:12,500 --> 00:52:12,760
the gecko

1105
00:52:12,989 --> 00:52:13,800
model now. But

1106
00:52:14,409 --> 00:52:18,790
yeah, eventually it's gonna get there, that somebody else is getting close enough,

1107
00:52:18,800 --> 00:52:18,989
you know?

1108
00:52:19,000 --> 00:52:22,129
And then he said, Well, you know, the advantage is really not that big,

1109
00:52:22,760 --> 00:52:24,590
so you can push it out.

1110
00:52:25,310 --> 00:52:29,489
So where do you? I mean, one of the key questions I was interested is,

1111
00:52:29,959 --> 00:52:30,560
um,

1112
00:52:32,260 --> 00:52:35,010
you I actually wrote this down, which I thought was interesting,

1113
00:52:35,040 --> 00:52:36,689
I think it says in your paper,

1114
00:52:37,620 --> 00:52:37,679
uh,

1115
00:52:37,689 --> 00:52:39,620
there’s a discrepancy between the large

1116
00:52:39,629 --> 00:52:41,750
number of publications about two equation models

1117
00:52:41,760 --> 00:52:45,320
and the slow pace of improvement in accuracy that has been achieved,

1118
00:52:46,239 --> 00:52:48,600
and it kind of goes to the broader question of

1119
00:52:49,600 --> 00:52:50,899
how much?

1120
00:52:53,590 --> 00:52:54,649
How much

1121
00:52:55,679 --> 00:52:58,020
will there be a point when RANS models

1122
00:52:59,030 --> 00:52:59,760
are not

1123
00:53:00,260 --> 00:53:02,100
used? You know it is.

1124
00:53:02,340 --> 00:53:04,449
There seems to be in this weird thing where

1125
00:53:04,800 --> 00:53:07,929
you know, the seventies, the eighties, arguably the nineties

1126
00:53:08,159 --> 00:53:09,909
were that kind of key era.

1127
00:53:10,719 --> 00:53:13,760
And then I know there have been plenty of models published since.

1128
00:53:14,100 --> 00:53:16,179
But if I go round to most companies,

1129
00:53:16,459 --> 00:53:18,780
they still are using them on from the nineties.

1130
00:53:19,030 --> 00:53:19,629
Um,

1131
00:53:20,149 --> 00:53:23,639
and it's now the 20 twenties. So it's it's quite a few years.

1132
00:53:24,229 --> 00:53:24,889
So

1133
00:53:25,810 --> 00:53:27,659
and with the advances

1134
00:53:28,020 --> 00:53:30,899
in scale, resolving thing like, where do you see?

1135
00:53:32,199 --> 00:53:34,239
Yeah, that that kind of transition.

1136
00:53:35,270 --> 00:53:39,590
Is it purely a computational thing? Are they going to die out? And people will just,

1137
00:53:40,510 --> 00:53:43,689
you know, scale. Resolving will be so cheap that people would just go to them.

1138
00:53:44,219 --> 00:53:44,409
III.

1139
00:53:44,419 --> 00:53:49,729
I just remember that when I started in in Germany and after some time, you know,

1140
00:53:49,739 --> 00:53:52,169
maybe five years later we tried to hire somebody.

1141
00:53:52,179 --> 00:53:52,830
Maybe,

1142
00:53:53,439 --> 00:53:56,770
you know, just around 2000, maybe 25 years ago.

1143
00:53:57,070 --> 00:53:59,750
So I had a PhD and he

1144
00:54:00,300 --> 00:54:01,030
sent

1145
00:54:01,330 --> 00:54:02,699
me. So we had an interview and

1146
00:54:03,310 --> 00:54:04,129
I asked you about R.

1147
00:54:04,310 --> 00:54:05,699
He said, I know nothing about R.

1148
00:54:06,169 --> 00:54:06,260
We

1149
00:54:06,489 --> 00:54:07,209
do LES, and

1150
00:54:07,429 --> 00:54:10,889
RANS will be replaced anyway by LES in the next years.

1151
00:54:11,219 --> 00:54:13,729
And, you know, that was 25 years ago.

1152
00:54:14,080 --> 00:54:17,459
And, uh, nothing not much happened in between, Really?

1153
00:54:17,860 --> 00:54:18,949
And

1154
00:54:19,149 --> 00:54:21,409
I don't think RANS is going to go away.

1155
00:54:21,879 --> 00:54:26,179
I mean, if it's never gonna go away, because if you look at the design system,

1156
00:54:26,310 --> 00:54:29,580
you always start from cheap to expensive, right? I mean, people in the

1157
00:54:29,820 --> 00:54:29,989
machine

1158
00:54:30,189 --> 00:54:30,679
they have inviscid

1159
00:54:30,879 --> 00:54:30,909
flow

1160
00:54:31,030 --> 00:54:35,310
codes, they have 1D codes where they have the channel, and then they have inviscid

1161
00:54:35,479 --> 00:54:35,510
flow

1162
00:54:35,649 --> 00:54:39,179
codes. And then they have this and that. So they they will always

1163
00:54:39,899 --> 00:54:41,530
have some place where they do a quick RANS

1164
00:54:41,770 --> 00:54:46,679
simulation going from there and then eventually, If if LES becomes more

1165
00:54:47,159 --> 00:54:47,979
accurate

1166
00:54:50,179 --> 00:54:53,830
and reasonably affordable, of course, then the emphasis will shift.

1167
00:54:53,840 --> 00:54:54,780
But the models will

1168
00:54:54,959 --> 00:54:59,520
never go away unless we go to quantum computing, you know, and then

1169
00:54:59,659 --> 00:55:00,939
changes everything, but

1170
00:55:01,189 --> 00:55:03,090
But otherwise

1171
00:55:03,479 --> 00:55:06,060
the RANS models will be around. Why would anybody

1172
00:55:06,449 --> 00:55:08,479
who designs a water turbine

1173
00:55:09,419 --> 00:55:10,270
and gets

1174
00:55:10,600 --> 00:55:12,929
almost perfect results? Why would they change,

1175
00:55:15,479 --> 00:55:16,169
But do you?

1176
00:55:17,189 --> 00:55:19,020
But do you see that? The, um,

1177
00:55:19,469 --> 00:55:21,810
the reason I say that is because there's some

1178
00:55:22,520 --> 00:55:24,580
codes and things coming out

1179
00:55:25,260 --> 00:55:27,360
where they don’t even have RANS implemented?

1180
00:55:28,530 --> 00:55:32,760
Um, you know, and so I’m wondering: is that almost a mistake? That there’s a

1181
00:55:33,459 --> 00:55:35,389
a sort of sense of because I say very

1182
00:55:35,399 --> 00:55:37,459
interesting what you say If the correlation is good,

1183
00:55:37,469 --> 00:55:41,149
if you're just doing flat plates or channels or you're doing something where, like,

1184
00:55:41,889 --> 00:55:43,989
it's propellers, you know, propellers are a very good example.

1185
00:55:44,000 --> 00:55:45,280
They're actually perfect with with

1186
00:55:45,570 --> 00:55:46,800
most of the time.

1187
00:55:48,770 --> 00:55:51,060
But, um, what has been your

1188
00:55:51,219 --> 00:55:54,629
so obviously one of the other big ones was SAS, and now looking at

1189
00:55:54,850 --> 00:55:58,689
wall-modelled LES. Where do you see that, though? Is it becoming far more common?

1190
00:55:59,100 --> 00:56:01,820
Are you seeing far more people use it. I mean, it used to be

1191
00:56:02,510 --> 00:56:03,449
research,

1192
00:56:03,629 --> 00:56:05,979
you know that everyone came up with these methods like

1193
00:56:06,520 --> 00:56:08,209
wall-resolved LES or hybrid RANS.

1194
00:56:08,219 --> 00:56:12,250
But when you actually went to a company who was designing a real thing,

1195
00:56:12,649 --> 00:56:13,370
they were still using

1196
00:56:13,510 --> 00:56:13,530
RANS.

1197
00:56:13,939 --> 00:56:15,919
Have you seen that transition

1198
00:56:16,379 --> 00:56:19,649
of people actually in production using,

1199
00:56:20,179 --> 00:56:22,010
you know, have you observed that shift.

1200
00:56:23,090 --> 00:56:24,370
There's no question about it.

1201
00:56:24,629 --> 00:56:26,560
Uh, there’s a lot of applications.

1202
00:56:26,570 --> 00:56:29,120
It started probably with combustion being one of

1203
00:56:29,129 --> 00:56:32,409
the main LES or hybrid application areas,

1204
00:56:32,419 --> 00:56:32,870
because

1205
00:56:33,020 --> 00:56:33,179
RANS

1206
00:56:33,340 --> 00:56:33,449
in

1207
00:56:33,719 --> 00:56:34,570
this—I mean, RANS

1208
00:56:34,739 --> 00:56:37,260
works fine if you have a very nicely guided flow.

1209
00:56:37,270 --> 00:56:39,129
But if the flow has degrees of freedom,

1210
00:56:39,620 --> 00:56:40,899
then it becomes dangerous

1211
00:56:41,179 --> 00:56:43,989
because it can lock itself into topology

1212
00:56:44,360 --> 00:56:45,719
and not get out of it, right?

1213
00:56:45,729 --> 00:56:45,929
I mean,

1214
00:56:45,939 --> 00:56:48,370
we saw these pizza shaped separations on the

1215
00:56:48,379 --> 00:56:51,050
high-lift airfoils, and then that’s that.

1216
00:56:51,300 --> 00:56:51,689
So

1217
00:56:51,949 --> 00:56:55,550
uh, uh, in that respect, if you have these topological

1218
00:56:55,850 --> 00:56:59,850
options scale resolving is obviously necessary,

1219
00:57:00,250 --> 00:57:02,409
and free-shear flows often have that.

1220
00:57:02,419 --> 00:57:06,790
So, uh, free-shear flows are also much cheaper to compute with LES.

1221
00:57:07,340 --> 00:57:09,629
If you go to the if you go to the kind of

1222
00:57:09,639 --> 00:57:14,550
mixed flows like aerodynamics and automotive as we are all familiar with,

1223
00:57:14,560 --> 00:57:15,070
I mean I,

1224
00:57:15,260 --> 00:57:15,649
I

1225
00:57:16,379 --> 00:57:17,040
our

1226
00:57:17,550 --> 00:57:18,879
experiences And

1227
00:57:19,199 --> 00:57:20,689
of course, we we see

1228
00:57:21,860 --> 00:57:24,439
things become feasible with GPU power. Now

1229
00:57:25,149 --> 00:57:25,520
I mean

1230
00:57:26,729 --> 00:57:29,780
wall-bounded LES in any form—wall-function, wall-modelled, anything—

1231
00:57:29,790 --> 00:57:31,979
It was completely unrealistic

1232
00:57:32,250 --> 00:57:36,500
with CPUs. But now with GPUs OK, we have a factor. Almost factor 10

1233
00:57:36,790 --> 00:57:40,439
in cost reduction and so that that gets you

1234
00:57:40,449 --> 00:57:42,830
into the range of where that becomes feasible.

1235
00:57:43,580 --> 00:57:48,120
If I look, for example, at the automotive DrivAer simulations,

1236
00:57:49,250 --> 00:57:54,389
if I look at the boundary layer between the SST and wall-function LES,

1237
00:57:55,179 --> 00:57:58,459
I think the boundary layer is better captured with the RANS

1238
00:57:58,629 --> 00:57:59,129
model.

1239
00:57:59,469 --> 00:58:00,040
Still,

1240
00:58:00,580 --> 00:58:04,510
because what we found doing diffuser simple diffuser flows.

1241
00:58:05,159 --> 00:58:07,120
You need a lot of points. You can

1242
00:58:07,540 --> 00:58:10,929
You can do a pretty good job, actually, on a fairly coarse mesh, surprisingly,

1243
00:58:11,300 --> 00:58:14,120
with LES with wall modelled LES of wall function, LES.

1244
00:58:14,929 --> 00:58:19,949
But then, to get that last bit of accuracy, you need massive refinement,

1245
00:58:20,699 --> 00:58:21,959
which is very costly.

1246
00:58:21,969 --> 00:58:27,189
And and that step we are certainly not there in terms of resolution.

1247
00:58:27,199 --> 00:58:29,659
So you have you have LES

1248
00:58:30,449 --> 00:58:33,370
in the boundary layer, sometimes not more accurate than the k–

1249
00:58:33,530 --> 00:58:33,699
ε

1250
00:58:33,840 --> 00:58:35,939
model in terms of separation prediction

1251
00:58:36,340 --> 00:58:37,260
and the like.

1252
00:58:38,030 --> 00:58:43,469
I think the main benefit that we seem to see there in these automotive simulations,

1253
00:58:43,479 --> 00:58:44,100
for example,

1254
00:58:45,050 --> 00:58:50,229
is that wall-function LES forces you to have a more uniform, fine mesh.

1255
00:58:50,330 --> 00:58:54,550
So if you go from wall-bounded flows into a separation,

1256
00:58:55,040 --> 00:58:57,659
You don't have this area of undefined

1257
00:58:57,820 --> 00:58:58,550
flow

1258
00:58:58,830 --> 00:59:00,659
where you have the grey area.

1259
00:59:00,669 --> 00:59:02,860
Whatever you want to call this transition zone between RANS

1260
00:59:03,040 --> 00:59:03,580
and LES

1261
00:59:04,070 --> 00:59:06,820
and often that transition zone is also pretty coarsely

1262
00:59:07,739 --> 00:59:10,439
discretized because you don’t know where it is, and

1263
00:59:10,739 --> 00:59:13,090
with RANS, you don’t need to have a very fine mesh.

1264
00:59:13,449 --> 00:59:13,929
So

1265
00:59:14,120 --> 00:59:15,449
having that

1266
00:59:15,899 --> 00:59:19,290
that forcing you to have that refinement in

1267
00:59:19,419 --> 00:59:20,469
different areas

1268
00:59:20,699 --> 00:59:23,179
seems to me the main benefit.

1269
00:59:23,189 --> 00:59:26,719
But if I look, for example, at the separation from the A pillar,

1270
00:59:27,870 --> 00:59:31,649
but you need to get right to get, for example, the noise on the side window

1271
00:59:32,340 --> 00:59:35,679
impacting there from the from the mirror and from the a pillar.

1272
00:59:36,300 --> 00:59:36,689
Uh,

1273
00:59:37,120 --> 00:59:38,540
if you do that with LES,

1274
00:59:38,550 --> 00:59:42,060
typically vortex is at the wrong place because it separates too late,

1275
00:59:42,070 --> 00:59:43,510
and then it's too far downstream.

1276
00:59:43,520 --> 00:59:46,949
And if you do it with SAS, with the SST model,

1277
00:59:47,030 --> 00:59:49,429
Then you tend to get that much more

1278
00:59:49,639 --> 00:59:50,620
accurately, So

1279
00:59:50,959 --> 00:59:53,540
I don't think we are there, where you can really claim

1280
00:59:53,919 --> 00:59:55,510
that the wall LES

1281
00:59:55,639 --> 00:59:55,669
in

1282
00:59:55,959 --> 00:59:58,939
itself is more accurate than RANS with the

1283
00:59:58,949 --> 01:00:01,080
meshes we can afford these days.

1284
01:00:01,090 --> 01:00:01,389
But,

1285
01:00:02,360 --> 01:00:05,060
uh, we're gonna probably get there eventually.

1286
01:00:05,989 --> 01:00:08,229
Yeah, I thought for that the, um

1287
01:00:08,689 --> 01:00:10,780
the whole wall-modelled LES versus hybrid

1288
01:00:10,790 --> 01:00:13,570
RANS versus RANS is an interesting one because

1289
01:00:14,120 --> 01:00:18,810
in some ways it seems to be not a completely fair comparison because

1290
01:00:19,229 --> 01:00:21,610
most people when they do hybrid RANS,

1291
01:00:22,300 --> 01:00:26,219
um, they're probably using a low Y Plus, you know, in a more academic setting

1292
01:00:26,600 --> 01:00:27,629
whereas a

1293
01:00:27,750 --> 01:00:29,840
wall-modelled LES, by definition,

1294
01:00:30,399 --> 01:00:31,169
you're

1295
01:00:31,419 --> 01:00:33,010
you don't have many points to the wall.

1296
01:00:33,280 --> 01:00:36,870
So probably a fairer comparison would be a wall function hybrid RANS.

1297
01:00:37,479 --> 01:00:38,370
LES.

1298
01:00:38,860 --> 01:00:39,540
Um

1299
01:00:39,870 --> 01:00:42,120
And I guess this is I mean, just for people.

1300
01:00:42,530 --> 01:00:43,770
You you've

1301
01:00:44,510 --> 01:00:46,270
you've coined, not coined the term.

1302
01:00:46,280 --> 01:00:49,310
But you you've popularised the term maybe wall function

1303
01:00:49,719 --> 01:00:50,459
LES.

1304
01:00:50,739 --> 01:00:51,969
So how do you,

1305
01:00:52,189 --> 01:00:52,250
in a

1306
01:00:52,469 --> 01:00:55,139
simple way, would you explain the difference between a wall

1307
01:00:55,300 --> 01:00:55,830
modelled

1308
01:00:56,000 --> 01:00:57,719
LES and a wall function

1309
01:00:58,070 --> 01:00:58,810
LES?

1310
01:00:59,699 --> 01:01:03,820
Well, it’s really only the grid that you use. In wall-function LES,

1311
01:01:04,020 --> 01:01:07,520
you basically have your first grid point somewhere in the log layer—cell centre,

1312
01:01:07,530 --> 01:01:09,800
whatever you’re using—you have that in the log layer.

1313
01:01:09,959 --> 01:01:12,939
And then you bridge everything without the grid. You can have a separate—

1314
01:01:12,949 --> 01:01:15,530
Some people have separate meshes into separate simulations.

1315
01:01:15,649 --> 01:01:22,320
But on the CFD mesh itself, you have no resolution between the log layer and the wall,

1316
01:01:22,689 --> 01:01:28,040
and in wall-modelled LES, we have resolution—we can put in y+ ≈ 1 meshes—but

1317
01:01:28,280 --> 01:01:31,899
we don't have the computing power to resolve all three dimensions.

1318
01:01:31,909 --> 01:01:34,270
So we only refine in one, and then you have to put in this RANS

1319
01:01:34,580 --> 01:01:35,129
kind of algebraic

1320
01:01:35,379 --> 01:01:35,500
RANS

1321
01:01:35,679 --> 01:01:36,360
model there

1322
01:01:36,620 --> 01:01:37,739
to bridge

1323
01:01:37,969 --> 01:01:39,139
to bridge the gap.

1324
01:01:39,590 --> 01:01:43,840
And so it is essentially the mesh that they are using

1325
01:01:44,080 --> 01:01:45,889
that defines

1326
01:01:46,340 --> 01:01:49,620
between wall function and wall modelled in our terminology.

1327
01:01:50,429 --> 01:01:53,659
Now, the problem with wall-modelled LES: you can put a kind of Prandtl model there.

1328
01:01:53,669 --> 01:01:55,139
It's very simple.

1329
01:01:55,629 --> 01:01:56,330
And, uh

1330
01:01:56,879 --> 01:01:59,179
but the question is, then how do we handle transition?

1331
01:02:00,780 --> 01:02:01,689
Because then the RANS

1332
01:02:01,889 --> 01:02:06,199
model starts to interfere with the laminar boundary layer, and you have to turn it off,

1333
01:02:06,209 --> 01:02:07,300
you know, So that's something.

1334
01:02:07,310 --> 01:02:09,000
We are currently experimenting with our

1335
01:02:09,290 --> 01:02:10,629
friends in Russia.

1336
01:02:10,639 --> 01:02:14,179
So we had this, as you know, this strong connection with the Strelets

1337
01:02:14,580 --> 01:02:16,360
group, the NTS,

1338
01:02:16,909 --> 01:02:19,760
which was actually also triangular relationship because

1339
01:02:20,020 --> 01:02:21,570
Spalart had a contract with them, and we had

1340
01:02:21,959 --> 01:02:21,969
a

1341
01:02:22,209 --> 01:02:24,959
we had, you know, kind of circulating information there.

1342
01:02:25,419 --> 01:02:26,320
And, uh,

1343
01:02:26,449 --> 01:02:30,830
of course, with this Russian situation now, these contracts are gone, but

1344
01:02:31,129 --> 01:02:33,370
we still communicate, and they

1345
01:02:33,580 --> 01:02:36,280
they still work on certain aspects of it.

1346
01:02:36,919 --> 01:02:37,629
And, uh,

1347
01:02:38,280 --> 01:02:39,620
but, yeah, that's

1348
01:02:39,889 --> 01:02:44,770
that’s where that stands. And they started, actually, this wall-modelled LES.

1349
01:02:44,949 --> 01:02:45,699
Professor Shur

1350
01:02:45,889 --> 01:02:47,219
actually had the first

1351
01:02:47,580 --> 01:02:48,989
of these algebraic

1352
01:02:50,469 --> 01:02:51,820
models where he used

1353
01:02:52,159 --> 01:02:54,260
a Prandtl mixing-length model near the wall.

1354
01:02:55,080 --> 01:02:55,260
Yeah,

1355
01:02:55,909 --> 01:02:57,439
so do you. Um

1356
01:02:59,570 --> 01:03:02,590
what, You know, everything we've spoken about has been,

1357
01:03:03,129 --> 01:03:05,790
well, not everything but largely low speed flows.

1358
01:03:06,639 --> 01:03:09,300
And one of the things I always find interesting is,

1359
01:03:09,310 --> 01:03:12,560
if you look now into the sort of hypersonics world,

1360
01:03:12,939 --> 01:03:15,300
the big question is always Well,

1361
01:03:15,489 --> 01:03:19,780
we're trying to use models that were designed for low speed flows

1362
01:03:20,530 --> 01:03:24,870
for high speed flows. And you know, how much do you see that as being

1363
01:03:25,370 --> 01:03:29,669
an important area of research or even just heat transfer?

1364
01:03:29,679 --> 01:03:31,590
You know, it seems like a lot of focus is on,

1365
01:03:32,129 --> 01:03:34,860
um, single phase simple stuff where

1366
01:03:35,020 --> 01:03:38,449
the real, like complex and I guess real physics

1367
01:03:38,820 --> 01:03:40,370
is often a lot harder, isn't it?

1368
01:03:41,719 --> 01:03:44,389
Yeah. II, I must say ahead of time.

1369
01:03:44,399 --> 01:03:48,169
I'm not an expert in high speed flows because we didn't have

1370
01:03:48,860 --> 01:03:51,760
a lot of customers historically. But now we have We have some.

1371
01:03:51,770 --> 01:03:53,929
We have done quite a lot of work on high speed

1372
01:03:54,080 --> 01:03:55,919
numerics. So we have a pretty good

1373
01:03:56,310 --> 01:03:57,689
numerics offering there.

1374
01:03:57,699 --> 01:04:01,330
And but on the modelling side, we we haven't done much else than anybody else.

1375
01:04:01,340 --> 01:04:04,330
And of course, there were these compressibility corrections of Sarkar

1376
01:04:04,709 --> 01:04:09,179
and others, which were mainly for free-shear flows and not for boundary layers.

1377
01:04:09,189 --> 01:04:10,290
So it actually has a

1378
01:04:10,610 --> 01:04:14,040
has a negative effect. If you put them into into boundary layer flows

1379
01:04:15,149 --> 01:04:15,209
II,

1380
01:04:15,219 --> 01:04:18,889
I follow it with some interest and I discuss with

1381
01:04:18,899 --> 01:04:22,239
colleagues who are working with customers in that area.

1382
01:04:22,709 --> 01:04:25,479
I don't see anything, which is

1383
01:04:26,120 --> 01:04:29,570
kind of convincing me that that's better than doing nothing

1384
01:04:29,939 --> 01:04:30,969
for boundaries.

1385
01:04:31,770 --> 01:04:33,050
Uh, on transition.

1386
01:04:33,060 --> 01:04:36,870
It might be a little bit different because we have additional transition effects,

1387
01:04:36,879 --> 01:04:37,350
obviously.

1388
01:04:37,360 --> 01:04:39,370
So I think that we have to do something that would

1389
01:04:39,379 --> 01:04:43,750
be clearly higher priority because there is a clearly visible,

1390
01:04:43,760 --> 01:04:46,280
different effect that we have to model.

1391
01:04:46,770 --> 01:04:49,389
But on the on the fully turbulent boundary layer,

1392
01:04:50,030 --> 01:04:55,050
I have not seen, but I might not be aware of all and everything that's out there.

1393
01:04:55,060 --> 01:04:57,729
There’s a lot of stuff now. Of course, there’s a lot of funding there,

1394
01:04:58,489 --> 01:04:59,209
and

1395
01:04:59,459 --> 01:05:03,120
it used to be also an area where he had a severe lack of data.

1396
01:05:03,129 --> 01:05:04,570
So he had very limited data,

1397
01:05:04,580 --> 01:05:07,020
and it was very hard to reproduce them because sometimes you didn't

1398
01:05:07,030 --> 01:05:10,389
know where the transition was and what other conditions you could match—wall

1399
01:05:10,639 --> 01:05:10,669
roughness

1400
01:05:10,860 --> 01:05:11,489
and so forth.

1401
01:05:11,899 --> 01:05:16,229
But now, with DNS, at certain Reynolds numbers—not that high—

1402
01:05:16,239 --> 01:05:19,020
so it's accessible partly to DNS.

1403
01:05:19,510 --> 01:05:22,300
So we're gonna have more, more data, and we'll see

1404
01:05:22,459 --> 01:05:27,530
how the models without corrections work and whether there is a systematic,

1405
01:05:27,919 --> 01:05:31,050
the systematic recognition that we need to do something.

1406
01:05:31,060 --> 01:05:32,979
I I'm I'm currently not at that

1407
01:05:33,350 --> 01:05:36,379
level of knowledge that I would I would see that

1408
01:05:36,610 --> 01:05:38,000
it might come.

1409
01:05:38,820 --> 01:05:41,800
So the other one that is on everyone's minds is

1410
01:05:42,070 --> 01:05:43,159
machine learning.

1411
01:05:43,409 --> 01:05:46,429
You know, it's one of these topics that divides the the industry.

1412
01:05:46,439 --> 01:05:49,169
You know, some people are extremely negative on it.

1413
01:05:49,419 --> 01:05:51,840
Others are extremely positive on it.

1414
01:05:52,189 --> 01:05:55,679
Where do you sit in that? Uh,

1415
01:05:55,879 --> 01:05:56,639
in that realm?

1416
01:05:57,820 --> 01:06:01,969
Well, I mean, from a CFD company standpoint,

1417
01:06:01,979 --> 01:06:04,820
Of course, machine learning is a is a huge thing, right?

1418
01:06:04,830 --> 01:06:07,530
It's it's it's, it's it's massive because it has a

1419
01:06:07,780 --> 01:06:10,340
lot of implications on a lot of things. It's not

1420
01:06:10,459 --> 01:06:11,500
just modelling.

1421
01:06:11,810 --> 01:06:16,669
It's how you guide how you guide the user, how you automate your processes. How

1422
01:06:16,810 --> 01:06:21,870
I mean if you read, if you read the geometry into A into a meshing geometry tool,

1423
01:06:22,520 --> 01:06:26,189
shouldn’t that recognise that that’s a car and that’s an aeroplane, you know,

1424
01:06:26,199 --> 01:06:28,080
and not be totally stupid about

1425
01:06:28,530 --> 01:06:33,090
it. And then and then set all sorts of things already in the right place. Uh,

1426
01:06:33,290 --> 01:06:37,179
III. I think that's clearly there's clearly a lot of potential in

1427
01:06:37,620 --> 01:06:39,280
in in that in that area.

1428
01:06:39,790 --> 01:06:45,679
And, of course, then if it gets down to the physics and numerics

1429
01:06:46,020 --> 01:06:46,969
and so forth,

1430
01:06:47,659 --> 01:06:53,469
Uh, of course, we haven't seen a lot of, uh, dynamic progress there, uh,

1431
01:06:53,760 --> 01:06:55,719
on turbulence modelling.

1432
01:06:55,729 --> 01:07:00,679
I think it's nothing that I would see that has really emerged as

1433
01:07:00,810 --> 01:07:04,919
being vastly superior, maybe a bit of work

1434
01:07:05,290 --> 01:07:07,669
on wall functions, which is interesting,

1435
01:07:07,679 --> 01:07:11,479
but I don't think wall functions are all that all that dominating in these flows

1436
01:07:11,580 --> 01:07:13,280
more an add on.

1437
01:07:13,620 --> 01:07:14,540
But on the RANS

1438
01:07:14,669 --> 01:07:15,320
side,

1439
01:07:15,709 --> 01:07:21,570
there’s a lot of—I mean, sometimes it’s shocking what people do there,

1440
01:07:22,669 --> 01:07:26,090
because what you have. I mean, you put in input and you get an output

1441
01:07:26,610 --> 01:07:30,929
and they use input, which no turbulence model would ever use,

1442
01:07:31,800 --> 01:07:34,159
because it's not invariant or because it's

1443
01:07:34,260 --> 01:07:35,909
it's Reynolds number dependent.

1444
01:07:35,919 --> 01:07:40,300
So I watched the presentation not so long ago with the guy you know had

1445
01:07:40,679 --> 01:07:43,070
30 minutes talkers or 20 minutes, and

1446
01:07:43,379 --> 01:07:45,870
he showed all sorts of complicated stuff.

1447
01:07:46,290 --> 01:07:52,000
But then he he examined what his machine learning tool was focusing in on,

1448
01:07:52,219 --> 01:07:54,360
and it was essentially μ_t/μ

1449
01:07:54,969 --> 01:07:58,100
in a case which essentially was Reynolds number independent.

1450
01:07:58,979 --> 01:08:02,360
And, of course, that is the essence of Reynolds number dependency.

1451
01:08:02,370 --> 01:08:03,750
So he could you know it.

1452
01:08:03,760 --> 01:08:06,449
It pick different cases and apparently had different levels

1453
01:08:06,459 --> 01:08:08,780
of μ_t/μ by coincidence.

1454
01:08:09,209 --> 01:08:13,199
And and that's what the model learned, which was actually worse than nothing.

1455
01:08:13,699 --> 01:08:14,370
And, uh,

1456
01:08:14,379 --> 01:08:17,089
and so people have go to this machine

1457
01:08:17,100 --> 01:08:21,879
learning tools very often with no understanding of

1458
01:08:22,209 --> 01:08:24,250
of the basic physics and, of course,

1459
01:08:24,259 --> 01:08:26,490
then the machine learning doesn't help you anything.

1460
01:08:26,500 --> 01:08:26,770
But

1461
01:08:27,608 --> 01:08:31,809
And the other thing is, of course, data. How many data we had? There was this

1462
01:08:32,508 --> 01:08:36,599
project where they tried to generate data,

1463
01:08:37,330 --> 01:08:41,279
uh, to use them to train RANS

1464
01:08:41,529 --> 01:08:44,120
models, Reynolds-stress models, Essentially.

1465
01:08:44,129 --> 01:08:48,089
So the goal was to have all the terms in the Reynolds-stress equations

1466
01:08:48,330 --> 01:08:50,169
averaged out of DNS.

1467
01:08:50,180 --> 01:08:52,750
And then at every grid point you have all the pressure,

1468
01:08:52,759 --> 01:08:54,160
strain and whatever terms.

1469
01:08:54,839 --> 01:08:55,370
But

1470
01:08:55,509 --> 01:08:59,129
even with a lot of computing power and relatively simple flows,

1471
01:08:59,140 --> 01:09:01,580
they could never get to that level of refinement that

1472
01:09:01,589 --> 01:09:04,379
the balances between the different terms would add up to

1473
01:09:04,799 --> 01:09:05,140
zero.

1474
01:09:05,799 --> 01:09:07,640
So the error in that game was

1475
01:09:08,509 --> 01:09:09,189
too large.

1476
01:09:09,750 --> 01:09:12,399
And then, of course, you still have the scale equation, the epsilon,

1477
01:09:12,709 --> 01:09:12,729
the

1478
01:09:12,879 --> 01:09:15,189
the omega equation, which is not a solid basis.

1479
01:09:15,200 --> 01:09:18,279
There is not an exact equation to build upon.

1480
01:09:18,640 --> 01:09:19,529
So if

1481
01:09:19,700 --> 01:09:23,450
suppose that equation causes 30% of all errors,

1482
01:09:24,279 --> 01:09:27,839
then even this huge effort you still have 30% of all error, right?

1483
01:09:27,850 --> 01:09:32,669
They have not an order of magnitude improvement, but, you know, 70% improvement,

1484
01:09:33,160 --> 01:09:38,299
and, uh, and that's probably not worthwhile the effort. So I don't say

1485
01:09:38,549 --> 01:09:40,979
there might not be an improvement here and there, but,

1486
01:09:42,509 --> 01:09:45,729
uh, it's currently not. It's currently not visible,

1487
01:09:46,819 --> 01:09:50,089
but what about the bigger picture? So, ignoring turbulence

1488
01:09:50,200 --> 01:09:50,959
modelling per se,

1489
01:09:51,589 --> 01:09:54,649
You know, there's growing now interest in,

1490
01:09:56,009 --> 01:09:59,899
um I don't know how you'd call it, um, surrogate modelling, essentially, you know,

1491
01:09:59,910 --> 01:10:00,859
reinventing,

1492
01:10:00,990 --> 01:10:03,490
you know, reduced order modelling the idea of

1493
01:10:04,009 --> 01:10:07,569
mapping an input to an output. You know, don’t try and develop a turbulence

1494
01:10:07,729 --> 01:10:10,200
model—just going straight from a geometry to

1495
01:10:10,490 --> 01:10:11,500
an output or

1496
01:10:12,029 --> 01:10:14,379
maybe a lack of physics, but, uh,

1497
01:10:14,540 --> 01:10:16,430
something pragmatically at work. I mean,

1498
01:10:16,580 --> 01:10:17,089
do you?

1499
01:10:17,379 --> 01:10:21,200
There’s a role. That is very interesting, because

1500
01:10:21,629 --> 01:10:23,529
if you, I mean, we should not forget.

1501
01:10:23,870 --> 01:10:27,430
A lot of engineers do the same thing over and over and over and over again, right?

1502
01:10:27,439 --> 01:10:28,180
So if you if you

1503
01:10:28,319 --> 01:10:32,490
a blade designer, you design blades and you do that and the department does it for,

1504
01:10:32,500 --> 01:10:34,060
you know, 50 100 years,

1505
01:10:34,580 --> 01:10:39,770
and so, of course, why would you have to repeat every single computation

1506
01:10:40,029 --> 01:10:41,890
time and over and over again?

1507
01:10:41,899 --> 01:10:44,990
So of course, you can learn something from the previous ones, and

1508
01:10:45,430 --> 01:10:49,310
I think that’s a much more fruitful

1509
01:10:49,990 --> 01:10:53,060
usage of machine learning because it also fits

1510
01:10:53,069 --> 01:10:56,209
much better into that framework of interpolation,

1511
01:10:56,220 --> 01:10:56,419
Right?

1512
01:10:56,430 --> 01:10:58,229
It’s kind of a functional interpolator.

1513
01:10:58,350 --> 01:10:59,740
And if you have enough space

1514
01:10:59,990 --> 01:11:04,040
and you can generate data because you only want to learn your RANS solution,

1515
01:11:04,049 --> 01:11:06,899
you don't want to learn the absolute truth.

1516
01:11:07,149 --> 01:11:09,290
You can calibrate against RANS,

1517
01:11:09,299 --> 01:11:13,830
which you can produce very lot of data in a very limited cost and time frame.

1518
01:11:13,990 --> 01:11:19,040
So that's clearly something of of, of benefit and and interest.

1519
01:11:19,879 --> 01:11:21,160
So where do you see

1520
01:11:21,270 --> 01:11:24,620
if you were to fast forward in 20 years time? So Oh, no.

1521
01:11:25,109 --> 01:11:25,740
20

1522
01:11:27,160 --> 01:11:29,750
No. 35. So basically from when you

1523
01:11:30,209 --> 01:11:32,660
did your F like the SST model to now

1524
01:11:33,450 --> 01:11:34,910
go that time again,

1525
01:11:35,709 --> 01:11:36,759
what do you think

1526
01:11:37,959 --> 01:11:38,899
the CFD

1527
01:11:39,709 --> 01:11:40,600
will look like?

1528
01:11:42,279 --> 01:11:47,779
The interesting thing about CFD is that it now it seems actually

1529
01:11:48,000 --> 01:11:49,359
more uncertain

1530
01:11:49,549 --> 01:11:52,259
what future it has than it had maybe 10 years ago.

1531
01:11:52,270 --> 01:11:55,500
10 years ago, to me, at least, it looked much more kind of stable.

1532
01:11:56,410 --> 01:11:58,700
But But now we have these elements, As you say,

1533
01:11:58,709 --> 01:12:01,870
there is machine learning and who knows what the impact of machine learning.

1534
01:12:01,879 --> 01:12:04,490
Maybe somebody comes up with some something very smart.

1535
01:12:04,500 --> 01:12:06,759
You know, maybe you can replace the linear solver with machine learning,

1536
01:12:07,009 --> 01:12:09,299
whatever. There could be all sorts of things happening,

1537
01:12:09,850 --> 01:12:10,959
so we don't know that,

1538
01:12:11,270 --> 01:12:13,339
uh, on a CFD level.

1539
01:12:13,350 --> 01:12:16,629
It could be that maybe we learn so much about the solutions

1540
01:12:16,640 --> 01:12:20,339
that CFD will play a smaller and smaller role because it’s a functional

1541
01:12:20,819 --> 01:12:21,399
interpolator.

1542
01:12:21,910 --> 01:12:25,290
You need only a very limited number of CFD solutions.

1543
01:12:26,160 --> 01:12:31,500
Who knows? And of course, the big factor in the back is is is quantum computing.

1544
01:12:31,509 --> 01:12:31,729
I mean,

1545
01:12:32,390 --> 01:12:34,600
in 35 years, you know,

1546
01:12:35,430 --> 01:12:37,290
it's of course, it's, uh

1547
01:12:37,740 --> 01:12:40,410
it's now it looks unrealistic, but

1548
01:12:40,549 --> 01:12:43,970
who knows? Maybe we have quantum computing and, uh,

1549
01:12:44,319 --> 01:12:45,359
and, uh,

1550
01:12:45,720 --> 01:12:49,180
you know, fusion reactors driving them, who knows? And,

1551
01:12:49,479 --> 01:12:54,160
of course, then the game is DNS or fine-grid LES and

1552
01:12:54,319 --> 01:12:56,709
and all these these things there. So

1553
01:12:57,350 --> 01:12:59,359
I think you cannot,

1554
01:12:59,799 --> 01:13:05,490
with any reasonable accuracy, predict in our time

1555
01:13:05,859 --> 01:13:05,870
a

1556
01:13:06,020 --> 01:13:09,709
period of 35 years in Not in, not in

1557
01:13:10,109 --> 01:13:14,129
technology, not in politics, not in social dimensions.

1558
01:13:14,140 --> 01:13:16,459
I mean, everything is kind of

1559
01:13:17,500 --> 01:13:17,819
yeah,

1560
01:13:18,509 --> 01:13:19,669
getting unr

1561
01:13:19,790 --> 01:13:21,009
it in a way and

1562
01:13:21,270 --> 01:13:25,740
who knows what what machine learning what an impact it has on on all of us.

1563
01:13:26,299 --> 01:13:26,379
It

1564
01:13:26,500 --> 01:13:27,689
could be

1565
01:13:27,950 --> 01:13:30,319
it could be good. It could be catastrophic. And

1566
01:13:31,140 --> 01:13:34,890
and the CFD is probably the smallest thing to worry about in

1567
01:13:35,060 --> 01:13:36,330
35 years.

1568
01:13:38,209 --> 01:13:43,910
Yeah, Yeah, I did get too. Yeah, gloomy on it, but yeah, I. I do know what you mean.

1569
01:13:43,959 --> 01:13:46,910
Well, one final thing I wanted to ask you actually was,

1570
01:13:47,379 --> 01:13:49,069
you know, there’s a lot of people,

1571
01:13:49,720 --> 01:13:50,549
um,

1572
01:13:51,129 --> 01:13:55,410
you know who look up to you and what you've done and

1573
01:13:55,729 --> 01:13:59,990
I guess, want to replicate or or or or have a career successful.

1574
01:14:00,270 --> 01:14:04,310
But what would be your advice to maybe people who are, I don't know,

1575
01:14:04,689 --> 01:14:08,000
in their PhD S right now or or or in their first job?

1576
01:14:08,140 --> 01:14:09,870
Is there anything that you've

1577
01:14:10,149 --> 01:14:13,740
learned along the way, or or an attitude or a thing that,

1578
01:14:14,200 --> 01:14:16,029
you know, you think has made a difference?

1579
01:14:17,600 --> 01:14:19,799
I think there’s a few things, actually,

1580
01:14:20,359 --> 01:14:25,140
Uh, one thing I I always remember was I had a professor at the university.

1581
01:14:25,149 --> 01:14:29,379
He was a mathematics professor, Professor Juan Fick, and he was a very accurate,

1582
01:14:29,390 --> 01:14:29,819
very

1583
01:14:29,950 --> 01:14:33,020
slim from my perspective, older man.

1584
01:14:33,029 --> 01:14:36,459
And he was very accurately writing at the blackboard. He had no notes.

1585
01:14:36,470 --> 01:14:39,040
Nothing in was was a perfect mathematician.

1586
01:14:39,589 --> 01:14:43,299
And he never had any personal relations with his students either.

1587
01:14:43,890 --> 01:14:44,319
Uh,

1588
01:14:44,529 --> 01:14:47,629
but one day he wrote something on the blackboard which,

1589
01:14:47,640 --> 01:14:49,560
apparently he liked that idea.

1590
01:14:49,569 --> 01:14:52,000
You know, he was somehow that was something he found.

1591
01:14:52,009 --> 01:14:53,709
That was a great idea at the time.

1592
01:14:54,529 --> 01:14:57,549
And so he turned around to us. He looked at us and he said,

1593
01:14:58,729 --> 01:15:00,939
Should you ever have an idea,

1594
01:15:01,779 --> 01:15:03,229
you have to pursue it

1595
01:15:03,589 --> 01:15:05,750
because it will not have many of them.

1596
01:15:05,779 --> 01:15:08,040
And then he turned around and he kept on writing.

1597
01:15:08,709 --> 01:15:09,520
And, uh,

1598
01:15:09,720 --> 01:15:10,319
I mean, this

1599
01:15:10,660 --> 01:15:13,990
cognitive that you say, Well, should you ever have an idea,

1600
01:15:15,009 --> 01:15:16,439
it might be You have none

1601
01:15:16,720 --> 01:15:16,910
that

1602
01:15:17,279 --> 01:15:18,950
that was the implication there.

1603
01:15:19,290 --> 01:15:23,810
And of course, as a young guy, you think I have so many ideas, you know,

1604
01:15:23,819 --> 01:15:25,720
why would you even think about that?

1605
01:15:25,970 --> 01:15:29,799
But in reality, it's not. It's not that you have only, uh,

1606
01:15:30,129 --> 01:15:32,569
what you could really consider is an idea which makes

1607
01:15:32,580 --> 01:15:34,720
a difference which moves something from a to B,

1608
01:15:34,729 --> 01:15:35,100
right?

1609
01:15:35,430 --> 01:15:37,890
There's not so many of these, and really,

1610
01:15:37,899 --> 01:15:41,979
you cannot give up on an idea that in your mind has a value.

1611
01:15:42,520 --> 01:15:45,589
You have to fight for these ideas because sometimes there is.

1612
01:15:45,600 --> 01:15:47,569
You know, I had no funding for transition modelling,

1613
01:15:47,580 --> 01:15:50,240
and we have to write a letter to all the companies to

1614
01:15:50,509 --> 01:15:50,609
machine

1615
01:15:50,770 --> 01:15:52,390
companies just to get the funding. But

1616
01:15:52,609 --> 01:15:53,359
you cannot.

1617
01:15:53,370 --> 01:15:53,839
First of all,

1618
01:15:53,850 --> 01:15:57,740
that’s the first thing: you cannot give up on an idea if you think it’s a valuable idea.

1619
01:15:58,910 --> 01:16:00,160
The second thing is,

1620
01:16:01,000 --> 01:16:02,660
be aware of complexity.

1621
01:16:03,879 --> 01:16:08,379
There are a lot of people who go immediately. If there is a problem

1622
01:16:09,259 --> 01:16:11,209
into the path of complexity

1623
01:16:13,250 --> 01:16:19,379
and in engineering, that's always almost always a recipe for failure

1624
01:16:20,279 --> 01:16:24,330
because you have to explore first the simple passages.

1625
01:16:24,339 --> 01:16:27,120
And then and then you can go into the more complex

1626
01:16:27,299 --> 01:16:28,029
because

1627
01:16:28,430 --> 01:16:32,430
complexity always breeds complexity. So it’s a complexity of maths that

1628
01:16:32,640 --> 01:16:36,169
breeds, complexity of implementation breeds complexity of convergence,

1629
01:16:36,180 --> 01:16:37,790
breeds all sorts of other problems.

1630
01:16:37,939 --> 01:16:40,310
So so you have to try

1631
01:16:41,169 --> 01:16:42,350
to keep it simple,

1632
01:16:42,830 --> 01:16:46,870
and and that's actually difficult at universities because

1633
01:16:47,330 --> 01:16:49,990
people are not valued for doing something simple.

1634
01:16:50,000 --> 01:16:51,930
You are valued for doing something complex.

1635
01:16:51,939 --> 01:16:56,169
The most valued papers are the ones nobody understands, right? I mean that

1636
01:16:56,359 --> 01:16:58,029
in a way, that's how it works.

1637
01:16:58,580 --> 01:17:02,919
And, uh and so, but still, you know, from a from a pragmatic standpoint, uh,

1638
01:17:02,930 --> 01:17:05,689
that's the the kind of the second,

1639
01:17:06,799 --> 01:17:09,450
the second thing that you, uh

1640
01:17:10,069 --> 01:17:11,850
I think which is important.

1641
01:17:12,540 --> 01:17:13,399
And, uh,

1642
01:17:14,370 --> 01:17:18,750
what else? The other thing. And I think that's maybe the most important one is

1643
01:17:20,069 --> 01:17:23,250
you have to picture because every everything you do is

1644
01:17:23,430 --> 01:17:27,399
takes a lot of effort and a lot of brain power and a lot of work and so forth.

1645
01:17:27,899 --> 01:17:32,250
You have to picture before I get into it. What happens if I'm successful,

1646
01:17:33,970 --> 01:17:37,750
right? What's the outcome? If I do that, all that work

1647
01:17:39,120 --> 01:17:41,540
and it achieves what I want to achieve,

1648
01:17:42,600 --> 01:17:45,379
what is the outcome? Does it make any difference or not?

1649
01:17:45,790 --> 01:17:48,779
And is that difference that it makes big enough

1650
01:17:49,189 --> 01:17:52,600
to make that investment in time and effort? Or should I,

1651
01:17:53,060 --> 01:17:53,500
you know,

1652
01:17:53,790 --> 01:17:55,979
look at something which has a bigger

1653
01:17:56,160 --> 01:17:57,509
multiplication factor,

1654
01:17:57,520 --> 01:17:59,990
something that has a bigger potential to get

1655
01:18:00,000 --> 01:18:02,950
yourself a technology or whatever you're interested in a

1656
01:18:03,299 --> 01:18:05,060
step forward. So

1657
01:18:05,310 --> 01:18:06,450
that's something.

1658
01:18:06,459 --> 01:18:09,700
Also, it's very easy to get dragged into something just because it's there.

1659
01:18:10,399 --> 01:18:10,649
Yeah,

1660
01:18:11,129 --> 01:18:14,620
and and then you're stuck with it and then it's hard to get out of it.

1661
01:18:14,629 --> 01:18:17,080
But it might not be the best usage of your time.

1662
01:18:17,770 --> 01:18:18,479
Mm,

1663
01:18:18,810 --> 01:18:19,220
yeah,

1664
01:18:19,390 --> 01:18:19,720
yeah.

1665
01:18:20,000 --> 01:18:21,529
No, no, I think that's very

1666
01:18:21,720 --> 01:18:24,000
wise. I think the first one is, uh

1667
01:18:24,930 --> 01:18:26,470
is very true.

1668
01:18:26,770 --> 01:18:27,339
I mean,

1669
01:18:28,779 --> 01:18:29,310
yeah, no,

1670
01:18:30,459 --> 01:18:35,029
not many people do. I guess it does seem like there's that golden period as well.

1671
01:18:35,049 --> 01:18:36,350
Sometimes people,

1672
01:18:37,049 --> 01:18:40,080
maybe when they don't have the stresses of life and like

1673
01:18:40,330 --> 01:18:41,439
mortgages

1674
01:18:41,600 --> 01:18:43,729
and all the rest There's probably like a time period,

1675
01:18:43,740 --> 01:18:45,509
which I guess you were at in those.

1676
01:18:45,520 --> 01:18:47,689
Obviously, you've gone on into more things. But

1677
01:18:48,020 --> 01:18:51,149
in that sort of SST time, I guess you were more.

1678
01:18:51,709 --> 01:18:55,029
You could just focus on it. Where, I guess later on,

1679
01:18:55,709 --> 01:18:58,970
you have more complexities of life to sort of

1680
01:18:59,169 --> 01:19:00,200
get in the way

1681
01:19:00,890 --> 01:19:01,979
invariably,

1682
01:19:02,149 --> 01:19:02,930
but still,

1683
01:19:03,970 --> 01:19:07,290
I mean, ideas are ideas and they come and go and you can't control that.

1684
01:19:07,299 --> 01:19:11,310
And sometimes they come at, you know, most unpleasant or

1685
01:19:11,689 --> 01:19:13,890
unsuitable times, but

1686
01:19:14,020 --> 01:19:17,750
then you still have to kind of listen. It's kind of interesting. What I found is,

1687
01:19:18,700 --> 01:19:20,479
if you are confronted with a problem

1688
01:19:22,209 --> 01:19:23,259
your brain

1689
01:19:23,479 --> 01:19:27,069
decides on its own, At least that's my experience.

1690
01:19:27,080 --> 01:19:28,669
Whether it can solve that problem.

1691
01:19:30,569 --> 01:19:31,080
And

1692
01:19:31,250 --> 01:19:35,200
if it decides that at least it has a fighting chance,

1693
01:19:35,540 --> 01:19:39,319
it it it keeps on working whether you want it or not.

1694
01:19:41,069 --> 01:19:43,979
And and but some other problems, which I think, well,

1695
01:19:43,990 --> 01:19:45,319
that would be very interesting.

1696
01:19:45,330 --> 01:19:49,439
My brain says, You know, I don't think you have the competence here to to solve that.

1697
01:19:49,450 --> 01:19:50,660
And and and it just

1698
01:19:50,810 --> 01:19:52,390
it just doesn't connect to it.

1699
01:19:53,020 --> 01:19:53,990
Yeah. Yeah,

1700
01:19:55,089 --> 01:19:58,740
Well, thank you so much for taking the time. II. I

1701
01:19:59,029 --> 01:20:01,689
learned a lot already on sort of the

1702
01:20:01,700 --> 01:20:04,169
interesting bits of the history behind the model.

1703
01:20:04,180 --> 01:20:06,600
And like like, I didn't even know the transition. Like

1704
01:20:06,959 --> 01:20:08,990
how you got that funded. Or

1705
01:20:09,160 --> 01:20:12,209
there are certain bits that now gonna make more sense to me.

1706
01:20:12,540 --> 01:20:15,750
Like why certain things happen And, uh, yeah,

1707
01:20:15,930 --> 01:20:18,220
thank you for taking the time. And it was lovely to chat.

1708
01:20:18,229 --> 01:20:20,120
And I hope we get to speak again more in the

1709
01:20:20,549 --> 01:20:22,649
in the future, in various workshops or things.

1710
01:20:23,279 --> 01:20:27,029
Yeah, thanks. Thanks, Neil, for having me. And good luck with your endeavour.

1711
01:20:27,040 --> 01:20:27,759
There on on your

1712
01:20:29,359 --> 01:20:29,399
cheers.

1713
01:20:29,990 --> 01:20:31,020
Thank you. Bye.