Hi, and welcome to the Neil Ashton
podcast. In each episode, we explain
some of the fascinating ways that
science and engineering are changing the
world around us.
We talk to leading engineers from elite
level sports like cycling and Formula 1
to some of the world's top academics to
understand how fluid dynamics, machine
learning, supercomputing are bringing in
a new era of discovery.
We also hear some of their life stories,
their career advice,
and lessons they've learned on the way
that I hope will be helpful to you, too.
So, sit back and enjoy this episode.
Welcome back to the Neil Ashton podcast.
Today's episode is with Chris Rumsey,
who is a research scientist at NASA
Langley Research Center.
This is another one of these episodes
that is going to get quite focused into
computational fluid dynamics and
turbulence modelling. So, um
I'm hoping if you're interested in that,
this is, you know, great for you and
diving into a topic and speaking to
somebody who I think is,
you know, has played a key role in this
industry and this community. Um but, uh
just just a caveat that if you are maybe
outside of CFD and you've been watching
or listening to some of the episodes on
Formula 1 or cycling,
uh just be prepared this does get quite
deep. So, I just put that as a warning
out there. Um
a quick thing as well I should say if
you're listening to this podcast, there
is also a YouTube version where we have
the full video.
Just type Neil Ashton podcast. Uh but,
equally if you're watching this on
YouTube right now, and actually
sometimes you prefer just to listen to a
podcast, it's also available on uh
Spotify and Apple. And uh
thanks to those who have already
listening. I I can't believe there's
over now 15,000 subscribers on YouTube,
which
yeah, I thought it'd be, you know, five
people and a few of their mates. So, I'm
pleased that at least some people are
finding this interesting, but yeah, so
if you do like this, I hate saying this
thing, but I'm told I should say it, the
whole like and subscribe, just because
then if a new episode comes out, you
you know about it.
So,
today's thing is, like I said, about
Chris Rumsey from from NASA. Um
I have to say that
I have a personal
I I just I really like Chris as an
individual, and I hope many other people
who know him will agree with me, and and
after watching this, you'll also agree
if you didn't know him before, because
he's just such a nice guy. He's just
such a pleasure
to talk to. He has
no ego, he's very modest, he he
but he has so much awareness, and and we
discussed many of the things that he's
he's done.
He's created and shared so much like his
his role of sort of public
dissemination, the papers he's written,
the websites he looked after, the
workshop he's done, so many of these
things that I genuinely think have had a
huge impact on every person out there
who develops a method, a code. And this
is particularly true in the aeronautical
sphere and things like drag prediction
or high-lift prediction
of aircraft, Chris has really played a
huge role.
But what the one of the some of the
things that I found interesting and that
we get into is um
I didn't know Chris was an artist. So,
we talk a little bit about some of his
early passions for that, how that he he
actually interviewed for the CIA. Again,
I had no clue about that. But I guess
the conversation is split into two.
The first half is a little bit about his
career, how he got into CFD,
some of the early days at Langley.
Talking a little bit about the
turbulence modelling website that he was
one of the the founders for. And if you
look in the um the caption notes or the
the show notes, there's a link to that
website if you're not aware. It
basically lists almost every single
turbulence model
that CFD has so that you can implement
it and you can verify that you've
implemented it correctly and then you
can validate it against some test cases.
This is
widely used as he said as you know more
than 40,000 visitors a month because it
is one of the central places um for for
these models. And that's not just
low-speed aerodynamics. Almost anybody
who's doing any turbulence modelling um
quite often goes to this website to find
the formulation and the papers.
Um and if you're writing a new code, uh
I mean hundreds or thousands of papers
reference this site to verify that
they've implemented the model correctly.
So we talk about some of the origins of
that. Um we talk about the high-lift
prediction workshop which um is a
workshop that's trying to advance the
state of the art for high-lift
aerodynamics. Uh again, I'll put a link
in the in the chat. Um and we talk about
how how some of those came about, some
of his thoughts on the role of machine
learning for CFD and for turbulence
modelling. Again, something that he's
been part of uh arranging with other
symposiums and and workshops to try and
explore this place. Um we also um get
into some discussion of certification by
analysis uh and some of the challenges
of machine learning applied to that, uh
how reproducibility, etc.
Um and then we shift a little bit
because one of the things I
wanted people listening to this to get a
feel of is
uh what is it what is it like to work at
NASA? What was that journey like? Um
I'm always jealous of people who work
for NASA. It was a dream of mine to to
work for NASA. And even though I did
spend a little bit of time there, I was
you know, never a civil servant like
like Chris is. So we dive a little bit
into how he got in, how other people,
the differences in the roles,
and then maybe a few more light-hearted
things around
some of the conferences that he the most
memorable times he's had at conferences
and events that he's done. And then a
little bit of discussion at the end
around
whether it's best to pursue your hobby
as a job or do your job and have a hobby
afterwards. So, hopefully an interesting
explanation or exploration of someone
who I think has made it a really key
role for CFD. And and just someone, like
I said, it's just such a a lovely and
nice individual that I hope um
if you haven't met him, that you have
the opportunity to meet him because he's
just a pleasure to speak to. So, yeah,
please sit back and enjoy this episode
with Chris Rumsey.
I I think most people have a dream.
At least I did.
Of working for NASA. It's sort of like
when you're growing up, there's this
certainly doesn't almost matter what
country you grow up in. You know, you
you think about NASA. You actually do
work for NASA.
So, did you always actually want to work
for NASA? Was that sort of a childhood
dream?
No, not not really.
When I was in
in college at
Rensselaer Polytechnic Institute, RPI,
they just had interviews and I actually
interviewed for the CIA. And then I
interviewed
interviewed for NASA.
And it just it just struck me at the
time that it would be
a good place to work and it sounded cool
then, but I hadn't thought of it before
before that time. Just that they had an
opening and
and it sounded really interesting.
Okay, I need to back up there cuz
I wasn't expecting that answer.
Is that because of where you
were living? I mean, was this
When you said the CIA or NASA, they're
quite different things. Were these
graduate programs essentially or
No, it was just at the time they just
had
the school had job fairs and
it was just random companies would come
in, you know, all from all over and and
you just signed up for the ones that you
were potentially interested in and just
had
It was almost like practice interviews
where you could could talk and not you
know, not worry so much about it because
you were just kind of trying to get a
feel for what was out there and
and I just remember those two.
Did you ever think what would happen if
you'd gone down the other route, I
guess?
I'd be a spy, I think. Well, we wouldn't
be having this conversation, that's for
sure. That's right, yeah.
Um but hold on, so you What did you
study then in university? What? I went
into aeronautical engineering. So I
started I I went directly from
directly into the aero aeronautical
engineering program.
And so I was always interested in in
aeronautics and aeronautical engineering
and
mathematics, that type of thing. But the
the aero
appealed to me I guess because of uh
airplanes and space, I've always
just found them fascinating that they
even work. So, you know, when I was a
kid it was like, "Wow, look at look at
that thing. How's that even working?"
So, just always always fascinated me and
and interested me. So, just wanted to
get into that field.
And did you Were you the sort of child
who was very
like obsessed with planes and fighter
jets and all that or were you more just
yeah? Yeah, not not really. Actually, I
was more into um
art and artistic things. I was In fact,
I
almost went that direction instead of
engineering
because I was
good at art, good at drawing and
painting and things like that.
But um, teachers convinced me that it
would be better to go into something
more lucrative
and
that where you have a definite type of
income and then
keep the art for a hobby or something to
do on the side
uh or or to do when you retire type of
thing. But, that's also partly why I was
interested in in aeronautics because of
the sleekness of the airplanes and how
they were shaped and
the design aspect of it was was
fascinating.
interesting. So, Oh, so I see behind
you. Are these some of your works that
you Oh, yeah. have that that one
uh
uh went to yeah, those are mine. Yeah.
Mhm.
But, see, you said before we started,
"Oh, I've got nothing interesting." I'm
already this is something I did not
know at all. Okay, that's interesting.
So, you were always Did Were you tempted
to go into architecture then at all that
side or was it more the drawing like uh
proper art like Yeah, maybe a little bit
um
the architecture I
um
Yeah, it doesn't move though. So, maybe
it wasn't quite as interesting me to be
from that perspective. A- And actually,
when when I was when I was at RPI, they
had an architecture program and some of
my roommate one of my roommates was a in
that. And it sa- he was just miserable.
It was awful because
the mathematics part of it, you know,
was always very definite. You're either
right or you're wrong. But, the art part
of it is very subjective. And if you
happen to have a teacher who is just
doesn't like you for some reason, he can
just say, "I don't like that because
it's not good." And it And it's very
subjective. So, he he was driven crazy
by that.
So, you did
your um undergraduate, but then you went
on to do a PhD. Is that correct? W-
Yeah, at RPI, I stayed at RPI for the
undergraduate and a master's. Did those
kind of together. Okay.
Yeah. So, it went fairly quickly and
then came to uh NASA.
And then I got my PhD through NASA. NASA
has a has a nice program. Well, they
they will
pay for you to get your degree,
you know, because it benefits them to
have someone who's
smarter working for them.
Okay. So, what did you do your PhD in
then? What was the um It It was in uh
aeronautical or aerospace engineering.
It It was at the University of Michigan,
and it was numerical methods
uh under Bram van Leer.
And that was uh he was my advisor.
Okay. And that connection came about
because of NASA, because
at NASA we had the ICASE program. I
don't know if you've heard of that.
But it's it's a it's a it was a a really
nice institute, kind of like the uh
there are other institutes like this
elsewhere. Um
ICASE, what does it stand for? I don't
remember what it even stands for. It's
not around anymore.
But it was a a small institute that was
independently run within NASA, and it
was actually housed in the building
where my branch was located.
And it would bring in mostly in the
summers bring in all these professors
from all over the world, Bram van Leer,
Phil Roe, I mean, all these all these
really uh great CFD guys. Mhm. And
turbulence folks as well. Uh to to come
and work um
uh for the summer or or other times. And
they would also bring in young
undergraduates and uh
build up
the science that that way.
And so, being housed with them, you
know, we got to know all these folks.
And it was really a a great
relationship. And because of that is
what why
then work for Bram for the PhD.
Ah, okay. But did you Did you always
want to go into the more
uh CFD side rather than wind tunnel
testing? Was Was there a reason for
going in, or was it just that was the
choice of PhD that was available?
Well, the branch that I joined was a it
was a This was the time when CFD was
kind of getting
started. So, it was a new branch that
had just uh gotten underway maybe a year
or two previous.
And I was
hired into that branch uh
despite knowing very little about
numerical methods at the you know, from
my uh
from my
uh
undergraduate and and master's work. I
didn't do that much with with com- with
computer science. But, the branch was
all numerical methods. In fact, when I
first got there, one of the first things
they do is they sit you down
and and they Each person in the branch
who was already there would talk to you
about what they did. And I just remember
sitting there going,
"What the heck? I didn't even know I
didn't know what a vortex was." I mean,
they they were just talking like
gibberish. So, I was
brought in and really trained on the job
to to do the the numerical methods.
Wow. So, what was the state that's This
is NASA Langley. It was NASA Langley,
yeah.
I And so, what was the
um
What was it like at that stage? Was
there any major projects going on at
that time? I mean, the space shuttle Am
I getting the dates wrong now? When
would that have been? I'm sure that that
was going on, but we weren't at least to
my knowledge that involved in that. It
was more uh fundamental development of
the of CFD codes for doing um
analyzing wings, you know, starting out
with the Euler and then moving fairly
quickly after I got there into the
Navier–Stokes and and started up these
codes. I don't know if you remember any
of the names of the codes, you know,
CFL3D and TLNS3D. They were very old
codes that were
uh and and worked with uh you know, Tony
Jameson was someone involved too, or he
was in in the same cadre of
of folks that that
that came to ICASE. And so, everyone was
working on the same um
uh in the same area trying to build up
the the CFD methods. When did you start
to transition more into turbulence
modelling compared to pure numerical
methods? Was there a sort of turning
point when that happened or particular
project? Yeah, it was kind of a gradual
shift toward that. Uh, part of it was
that we had, um,
some
to turbulence, you know, the turbulence
work was going on at ICASE and then, uh,
we had some folks in the branch who who
were more turbulence modelers and I just
started to work with them
uh,
a little over time and and of course our
codes, starting out with just
laminar Navier–Stokes, but then we
needed turbulence models, so we put in
this the Cebeci-Smith and the
Baldwin-Lomax. And then, of course, in
the early '90s,
uh, Philippe Spalart and, uh, Florian
Menter, who who you've interviewed
already here, their models came out and
and we worked with them to put them in
our codes as well. And then that it just
kind of built from there.
Yeah, what was I was trying to when I
was asking Florian about that, what was
it like that time? Because looking back,
it seems like that must have been this
really,
you know, fast-paced development all
these models. Did it feel like that or
at the time did it
did it not? Do you know what I mean? Did
it feel like you were onto something
momentous with these new models coming
in that gave better accuracy than before
or was it Did it just feel like a normal
No, I don't think it felt normal. It was
It was I think we we we saw this as as
major developments. I mean, Mhm. uh,
because with the ones we were working
with before were just, um,
uh,
I don't know. So, so, um, touchy and,
um, I don't know what the word is, but,
ad hoc ad hoc, yeah. And then to have
these, uh,
well actually the even before Philippe
Spalart's,
uh, model, there was the Johnson-King
model, you know, and that that one I
spent a lot of time working with Dennis
Johnson and NASA Ames to put that in our
code.
And and that was a very difficult model
to to implement.
I'd say
in fact if we had to do verification on
that model now, I think most codes would
all be giving different answers cuz it
was
you know, searching along lines to find
thing I don't know. It's just
not very easy.
Mhm. And in fact it was kind of a funny
story with that. We had two codes in in
our branch, CFL3D and FUN3D and and we
started comparing against each other.
And at one point we were getting
different answers with that model.
And
the TLNS3D answers were better compared
to certain experiments
when we were comparing. But it turned
out that that there was a a factor of
two error in in their coding. So putting
in that factor of two
just happened to give a better answer.
This is a this is a story of turbulence
modelling, right? Yeah.
There's always a two missing or a half
missing. Yeah, yeah.
Ah, okay. Just so for people to
understand, so CFL3D, am I correct that
was the structured focused code, is that
right? Well, both TLNS3D and CFL3D are
both structured codes. Yeah, they just
kind of developed side by side. Um
Okay.
within the same same branch. And then
FUN3D came along. This is also within
our branch, came along the unstructured
uh
code came along around that time,
started to build up.
Okay, so were you quite involved with
FUN3D as well or was it more CFL
focused?
Yeah, for me it was more CFL3D. Uh the
FUN3D I wasn't too involved in until
later.
Yeah, maybe in the 2000s I started to
get more involved on that team.
Now, one of the thing that I was really
interested to know
the turbulence modelling website Mhm. has
become a
I mean, almost everybody who's
developing turbulence models or
or not even developing them, testing
them, verifying them, validating them.
So, if you go to that website now, but
how did it all begin? What How did that
even start?
Yeah, that um
during the early 2000s, I guess. Uh
uh
Brian Smith from Lockheed Martin and
George Wang from um
Where is he now? Ohio
State One of the One of the universities
in
up there.
Uh
we got together and we were talking
about this um
how it would be nice to have
uh
a website or a a place where people
could go to um
see
how models behave for basic problems,
right? That was the the first idea was
to say, "Okay, well, if I want to run a
case that's really dominated by, you
know, shear or or
a jet an axisymmetric
you know, different type If I have this
this dominant
basic problem, what is the best model
for it?" And to have a place where
people could just go and look it up and
say like a little library. Say, "Oh, you
know, for this type of
flow, this is the best model."
Uh or or whatever. Or or this is the
the flaws of the model here. And uh so,
we created this team
uh the turbulence model benchmark
working group, which still exists today
and still meets fairly regularly. Um
to to talk about to get together and
talk about these things and maybe start
to formulate uh plans for that.
So, that's really the where it started.
And there were some special sessions at
early AIAA meetings where we uh brought
up the ideas and talked to people about
it.
But then, the part of the But the part
of the website
the verification part, which which I
think is maybe had a little bit more
impact now. That was actually something
that I came up with as a total fluke
result of what what I would call
serendipity.
You know, I just something that just
happened at a workshop I went to and it
was it was something in um
it was a workshop in Europe. They used
to have these
uh I can't remember the names of the
workshops, but they were
they would get together kind of like
they do now and they everyone would run
their codes with
certain models certain small ones.
um a cough tech maybe?
It was in a cough tech. It was in a
cough tech. Yeah, sponsored event.
And then they would compare results. You
know, and usually it was something with
separation because no one could do that.
Mhm. And then they would um draw
conclusions from them or not which is
maybe more often what happened. And and
at at this at this workshop at one point
there was um
they were showing results and I was I
was using CFL3D with a Spalart–Allmaras
model.
And it turns out that another
group, I think it was ONERA
was using
I believe it was ELSA, but one of their
codes using the same model
and there were all these results, but
these two results right dead on top of
each other. And it was it was these two
code results and I realized then that
said, you know, this is a good way to
determine whether
you're um
you've coded it correctly. If two people
because it's so much more likely
that if you get the same answer what's
the what are the chances of
of of a coding error
if two are absolutely dead on top of
each other like that.
Mhm. And so it just struck me that that
was a way of
um
doing a kind of cheap verification, you
know, without
you know, the formal
method of manufactured solutions or or
other methodology, but just just
you know, the the on the more codes you
compare and the more the degree the more
confidence you have in that this is
right.
And um do you have any stats on that? Do
you know how many people actually go to
that website?
Do you have a ton of any Yeah, I mean
I've it's that's hard to say because
they have these, you know, Google um
has a
a way of uh tracking these things, but I
don't know what these numbers actually
mean because it's is I don't know
whether it's actually people going to it
or or robots going to it. I I don't know
what it is.
But uh yeah, it's it's it's quite a bit.
I can't remember maybe 40,000 a month or
something like that at the most, yeah.
It's a lot. I'm sure it is cuz everybody
seems to go there.
It's um I mean, it is a credit that you
know, Brian himself and the rest still
keep this going because uh
I I used it in my PhD. I think most
people do it. It's it's such a But I
always feel bad for you cuz it seems
like I your website coding skills quite
good now because you always seem to be
updating websites. Oh, yeah.
That that's happened. And then one
unintended consequence of the website,
too, was just I didn't realize this at
the time, but having one place just to
go for information like that is really
it's I mean, I do it all the time
myself. Like instead of
having to say, "Okay, well, I got to
look up this thing in in the Broughton's
model." So, you have to find the paper
and you have to remember where the paper
was. You have to remember which year it
was published. And this way it's just
one it takes you 10 seconds and you're
and you're there. One of the things that
stood out in that 2030 report was the
lack of machine learning.
It it's kind of incredible in a way. If
I'm not mistaken, there was very little
mention if if at all Yeah.
about machine learning and fast forward
to now and it's So, is that What Where
do you
I mean, this is a big topic, but so
let's say first of all, the turbulence
modelling, where do you see
the role of machine learning? Are Are
you positive about? Are you thinking
there's over hype in this area around
turbulence modelling to begin with?
Yeah, I think there's a little bit of
over hype and it's coming back down to
ground now a little bit because I think
at the time well first of all I think
when the when the 2014 report came out
there wasn't much machine learning out
there. I mean there it was there but not
so much in our field. So I it wasn't
even in the the authors
brains at that point. But um
I think then that that the thought there
was a lot of thought that oh well this
is going to answer everything within a
few years we'll have all the answers and
and that
did not pan out and now I think maybe
the thinking more is that machine
learning
in conjunction with someone who
understands turbulence like a turbulence
model developer
somehow working hand in hand and maybe
it like another tool that could be used
to help the turbulence modeler develop
something but that the machine learning
itself
it is it's it's not going to do it on
its own.
So yeah we were talking um before about
the high-lift. So where did um
I guess similar to the turbulence
modelling
website high-lift workshop among some of
the other workshop as
I would say been responsible for pushing
a lot of the innovation particularly
around scale resolving methods. I think
the past few years
um there's been a lot of advances on
that but what where did that actually
start
the the high-lift workshop? Do you
remember the origins?
Well I mean I guess the drag prediction
workshop came first and became the the
source for a lot of ideas for for
workshop subsequently but um
yeah the high-lift workshop was uh I
guess it was a the brainchild of uh the
NASA collaboration with folks at uh
Boeing and um another
uh
of the commercial companies that where
high-lift was the the solution or the
need to solve high-lift accurately with
CFD it was seen as a major stumbling
block and and it's you know still is
today, you know, that's not not really
known exactly how to confidently do
that. And so the workshops were they saw
the drag prediction workshops and said,
"Well, we should do this for high-lift
and really bring the community together
to to try to
put our brains in one place and get get
something working better."
And where do you see it from then
to now?
How how have you seen the progression
being in terms of accuracy, in terms of
capability?
Well, I guess the first three workshops
were almost all Reynolds-averaged
Navier–Stokes.
And one of our big conclusions so far
has been that Reynolds-averaged
Navier–Stokes cannot
consistently and accurately predict
separated flow. So, it consist it cannot
consistently and accurately predict high
lift
uh the high-lift flows.
So, for a lot of it, it was uh
not much progression other than just
confirmation every time that, you know,
we can't do it or we have this issue.
And then very slowly over the last
couple of workshops, we've had more of
the scale resolving type of simulations
enter into the
picture.
And we're seeing now the the push in
that direction and seeing how
it it's still not perfect, but those and
those methods still have a lot of
There's so many out there, so many
different ways of
approaching it.
But we see that the the flow physics is
better captured by those methods. We see
the
um
uh the separation patterns and the
the reasons for the stall that happened
near near maximum lift
are being captured by those methods. But
do you think therefore we're close to
solving this problem? Where do you see
as the
Well, I think it's it's certainly
closer, you know,
in projects we often have, you know,
milestones and uh
and points where we declare success.
And it's it's it it's maybe almost too
easy to say, "Yeah, we've solved it."
When really there's
it just made a step in the right
direction and it's it's certainly
getting it better, but there's
still a ways to go. I saw you just
recently did um
a paper which which I guess all of this
is sort of leading to, isn't it? Which
is this certification by analysis.
Um
where do you see that being and maybe
you could explain just this terminology.
I guess some people call it digital
certification, some people call it
certification.
How would you explain it and why is it
an important thing for for NASA and and
others?
Yeah, well well it's important thing
especially for the aircraft industry. I
guess NASA is hoping to support that
that goal. But it's um the idea that
right now when they design airplanes,
there's a lot of uh wind tunnel and
flight tests that are very expensive
that take place during the certification
process.
And the hope is that some aspects of the
certification testing could be done by
CFD
in combination with
uh with the others.
That would lessen the the need for all
of the expensive
uh especially the flight flight testing,
but all to reduce the expense of of the
testing.
So to get to that point though, there
has to be a extremely high trust in the
CFD that that what it's doing is
as good
as the um
the other type of testing.
And so that's the hope.
At this point
I think it's not there, but uh that's
that's the idea of
push toward that goal.
I I think that's the one where I find
particularly interesting around machine
learning because
I guess if you're going to do
the way I see it, certification by
analysis makes sense. It takes a you
know, billions of dollars, years to do a
lot of the wind tunnel and flight tests
that
will still need to be done, but it I
guess it's just reducing the number of
them
um such that the ones that they do do
could maybe be more high fidelity or you
know,
more detailed.
Um and it seems like scale resolving
methods are
the sort of methods that could help in
that. But then this is where the
interest in machine learning comes in
cuz there's a lot of a lot of talk of of
machine learning, but
the issue then is the reproducibility
and can you explain it? I guess if you
take a scale resolving, you can say it's
solving these equations with this
numerical scheme. You can sort of
there's a theory
how you get to the answer, isn't there?
Where
most people cannot exactly explain how a
machine learning model gets to the
answer. You know what I mean? Like how
The computer can't even tell you how it
gets the Yeah, so I do kind of wonder
whether there's a bit of an opposing
challenge here that machine learning may
offer the challenge to do things faster
and cheaper,
but does it meet the test of being fully
explainable and reproducible
such that
you can use it to do Right. Um cuz
there's a lot of
I think the reason I'm saying this is
you know how it is, there's always when
it comes to funding
there's buzzwords, isn't there? There's
there's there's things that attract
funding. So for a while RANS was it. I
think now it's almost the opposite where
if you put RANS in your funding proposal
This is what a lot of academics tell me.
They say that is a guaranteed way for it
not to be funded because they say it's
all been done.
Yeah.
And then it was all about scale
resolving. And that was the way to get
things funded, but it feels like now
if you just say you're doing scale
resolving, that's not considered novel
enough. That's what everyone does it
now. Yeah. You see, you have to do
machine learning. Mhm. And uh the sort
of concern is if all the money is put
into the machine learning
for partially good reason,
does it actually take money away
from perhaps some of the methods
that ultimately may
need to be used because of this
reproduce You know what
Do you see that? Do you see many of the
NASA project or not just NASA, but just
projects you're involved in?
Well, in general, yeah, I think so. It's
like It's like buzzword funding. Yeah,
you you
you almost have to have to do that to to
get funding. And this is one of the
concerns about the turbulence model
benchmarking working group. It's a big
mouthful to say. They um
you know, that group in general feels
that that
RANS, you know, because
Reynolds-averaged Navier–Stokes will be around
for a long time. It's It's a very useful
and usable method. We shouldn't
just
fix it in time and stagnate it. There
should be continued development. And so,
there's there
It would be nice to have continued
funding to some degree to some level for
that effort.
Mhm. Yeah.
I guess this is always the challenge of
um
existing method versus new methods. You
know, you have to sort of keep things
going,
but if you lose the older methods, yeah,
it's it's tricky. Um
I I was keen to to take a slightly
different turn. Um Uh-oh.
No, in a in a positive way. Um
Okay.
What's the best conference you've ever
been to?
Okay, the best conference I've ever been
to.
That's uh not a question I was expecting
to
Well, I've been to so many AIAA
conferences, right? I mean, I've been to
Reno probably eight times.
Uh Uh
maybe maybe 50 AIAA conferences in my
life. Um so, it's hard to say if if if
they're I mean, I always enjoy them and
they're and they're very good. So, I
have to think of a novel conference
that's something out of the ordinary
that I've been to. Um
well, I would have to say probably one
of one of the workshop that one of the
first workshops I went to in Europe
where this idea for the
turbulence model
verification
came about. That I think that was a
I always think back fondly on that
workshop. First of all, because it was
in France. Okay.
And uh you know, I hadn't been there
before.
And they had wine for lunch. See, there
we go.
But I just think fondly on it just from
the fact that that was where
you know, the idea first came to me for
the for the turbulence modelling website.
Did the wine help the idea of I don't
think so, no.
Do you remember where in France that
was?
Uh that I think that was in Poitiers.
Okay. Yeah.
Okay.
Yeah. Where Joan of Arc was
tried or burned or something. I don't
know.
Okay. It was part of the tour there.
Yeah, I mean, I love the AIAA, but um
you know, there is something nice. I see
the value The AIAA
conferences are fantastic as a
networking event to see everybody in one
place. It sort of saves you
the you know everyone's going to be
there. That's right. To a certain point.
Um but
there is a value to sometimes these
smaller workshops that have a
a very targeted focus. Um and I must
admit I do kind of like
these ones. Um
I'm a bit of I'm obviously a bit biased
being in Europe myself, but there tends
to be a sort of
a large social aspect to it, which is
quite helpful to to sort of start
ideating ideas. Um
Yeah, and you meet new people because
it's a place you don't ordinarily go and
it's out of your normal wheelhouse. So,
that's always nice, too.
Okay, so
the um
maybe we can find the name. It was
probably in a cough tech thing. I think
I may
I think it was, yeah. That
That reminds me. I've seen some reports.
I can try and find I can maybe put in
the show comments. Okay, what was the
worst conference?
Uh The worst location for a conference.
Uh the worst conference
Well, maybe the uh the one that was held
here in uh in Norfolk. There was an AIAA
held in Norfolk, so it was so close that
we just had to drive to and from it.
And you couldn't even stay. You couldn't
justify Couldn't even justify staying.
You just had to drive from home, yeah.
What about Didn't they used to go um
This was before I think So, what's the
history of Reno? Was it always in Reno?
Was that the thing?
And someone told me that there was like
but it was there for many, many years. I
think AIAA had a nice deal there. Maybe
that was the reason, but it was also
very inexpensive place to stay. I
remember $55 a night was the uh
the cost of the the room for many, many
years.
Wow. Okay.
hard to get to. That was the hard part
because it was in in January.
And then, of course, in Reno depends on
the weather, you know, and it was often
fogged in.
It was one time we had to drive through
a snowstorm from San Francisco to get
there because the flights weren't
weren't going in.
Wow. That's crazy. Yeah,
I think one of my um
favorite conference I don't know if they
still have this philosophy. I think they
did, which was um
ETMM.
This is the European Turbulence
modelling and
I should know what the acronym is. And
something measurement, something like
that.
And someone told me that the if you
wanted to be an organizer, one of the
requirements they had is that ETMM must
always be by the a beach.
That's right. I think I heard that, too.
Yeah. And
but it did make it a very nice
event
to do. So that I think there was one in
I'm not sure if you were there.
Yeah, I went to I went to the one recent
I've I've never been to any of those,
but I went to one recently. I think that
was the one in
near Barcelona. Was that the one in
there was one in Marbella
that I I think I went to. Okay.
That was one. The only one that I'm
jealous of is every time the AIAA seems
to have something in Hawaii. Which is
not very often nowadays. I've never been
able to make but I do seem to remember
that about 10 years ago it was in Hawaii
and all the European people I knew were
trying to desperately put together
abstracts to justify
going. Yeah. Going to Hawaii.
There was a big flak when they first had
it in Hawaii. Some of the NASA managers
were saying this doesn't this isn't
right because it's like you're going to
a resort. It doesn't
you know,
it's not right for a government person
to go to a resort. We have to be
Yeah. Yeah, no, no, no. I especially
whenever there's times of sort of
economic crunch. I guess those uh
those get more challenging. Um
how about
one of the things that I've
I've always
keen to do and I guess part of the
reason for you been doing this podcast
was
to help people who are maybe a bit
earlier in their careers and they're not
so clear sort of you know, how to
progress in you know, throughout
throughout career. Mhm. What would be
your advice, and I don't know if you do
mentor people or help people in terms of
working for NASA? Do you What are the
routes? So, if you're, let's say, an
undergraduate or school um kid in the
US,
what routes are there to ultimately get
to to NASA?
Well, I think part of part of working
for NASA is is
being lucky because they don't hire very
often and and it's it's very competitive
to get in, but um
uh the job itself is great, you know,
because there's a certain amount of a
autonomy. It's probably similar to being
uh in in a university to some degree,
although maybe a little less
pressure from the point of view of
Having to get
get funding, yeah. Yeah. Yeah. But uh
yeah, I mean, I guess
I
the kind of advice I would give would be
I mean, to always say
at least when you're young, to always
say yes, you know, to anything and sign
up for things and and try new things
that are um
that are challenging that
uh
no one else wants to do because you
you'll always learn something from it.
And then also, I think hook on to things
that happen by chance and and
if if you see something that happens by
chance, then it's uh
a potential route to follow, you know,
follow it if you can.
Mhm. But, do you have Are you aware of
like even in Langley, do they do
internships? Do you You know, if you're
like an undergraduate, can you come over
to do Are there those sort of programs?
Yeah, they do have they do have
internships. Um
there's a it's an official site I mean,
it's changed over the years how they've
done it. Uh but but there's a a
uh internship program where they bring
in even high school students, college
students, various different levels.
Mhm.
And how does it I think I know the
answer to this, but maybe it's
interesting for others to know. So,
you join at NASA,
correct? And
am I right in that
when you first join, you either
you quite often join
as um
like a contractor almost? Would that be
right to say that that there's a sort of
Do you Do you know what I'm getting at?
And
Right. Right. Yeah. Yeah. Could you
explain maybe
have civil servants and they have
contractors, yeah. Now, the civil
servants are the are the difficult the
ones that don't open up so often. There
aren't often uh positions or the
positions are very few and far between.
Uh so often
uh
they'll have contractor positions. And I
think even now they've even changed the
positions uh
maybe not to not be a lifetime position.
They'll be like term hires.
As a way of um
trying to limit the
the the number of
civil servants.
civil servants, yeah. So, term hires or
which would be you know, it'll be a
5-year position, renewable depending on
your uh
Ah, okay.
how how well you do or you know, your
the needs for the pro- of the projects.
I don't I guess this must be very
difficult across NASA, but I you
mentioned that and I've always wondered
myself
like with a university. So, is it meant
Is it essentially that
as a
you're sort of treated as your own
specialist where you sort of have a a
freedom
to work on your own projects as well as
you're assigned projects to work on? Is
that kind of how it is? There's a bit of
a blend between the two. There's some
independent work and there's some
some project work?
Yeah, well, I think the way it it
optimally works is well, 50% of your
time is spent reading emails, right?
Like but nowadays.
But among the among the time when you
actually do work,
uh
so the projects, you know,
when you're first hired especially, you
know, you you're you're given a project
and you're you're expected to work on
the project. I think as you become more
and more senior, you you have more of a
say in contributing to what the projects
are doing, right? Cuz projects often get
their ideas from the
the the people that work there, you
know, the senior members will help
define what the project's
goals are.
Uh, but then every individual also is
given
I mean, again, this is in theory, given
like 15% of their time to do whatever
they want, you know, this is a white
space type of activity where they
encourage you to go try something that
that you saw that you
didn't you know, it's not necessarily a
project
goal, but something that you think might
turn out good and give give something.
So, the problem, of course, is finding
that 15% sometimes,
especially with the with the all the
emails.
Yeah, I know I can
I can imagine.
Did you ever
There's often a debate, academia,
industry, government. Mhm.
Were you ever tempted to go to an
industry position, you know, to to leave
NASA?
Yeah, I'd say you know,
throughout my career I've had several
times when I thought, well, I've done
this long enough, I should I should do
something different and thought about
what if I worked at Pixar, you know, I
mean
Oh, wow.
or or Tesla or something, you know.
Yeah. Yeah, but never got beyond the
point of just kind of thinking about it.
But, uh,
yeah, yeah, and then at one point so
some folks have left NASA and worked at
universities, um, and then sometimes
they come back and or they've
migrated from universities to NASA. It's
very different. I mean, we're we're
similar to university. I think we're
there's a lot
uh, it's kind of an academic feel to
working at NASA. Very very academic
environment in general.
But we do have very definitive project
goals and project thrusts.
So therefore there's milestones and and
needs you know NASA type
you know
thrust and goals that that that define
you where which where you may not have
that in in a in university environment
but then of course university
environment there's other pressures of
trying to get funding from places like
NASA.
Yeah.
of course industry there's always the
worry of
the ups and down swings in the in the in
the industry.
So what's kept you is there a certain
thing of
are you over the NASA thing or do you
have any moments like I don't know if
you ever work then wear the NASA t-shirt
when you go through are there any times
when you you sort of have that moment
you go oh actually this is pretty cool
that I work. Is there anything that
keeps you basically at NASA like that?
Yeah I think that there's that that
factor.
There there's been a lot of a lot of
times where you run into people and you
say you work at NASA and they all go woo
you know. And then you say well that's
the aeronautical side of NASA and they
say the what?
I was going to ask you about
Yeah everyone thinks of the the space
space part of it. But I I think I I've
really enjoyed working here. It's been a
you know
it's nothing's perfect and there's
always you know the grass is greener
thoughts.
But but overall I'd say it's been a a
really good place to work. You said
right at the beginning that you
attempted to to go into art. So have you
managed to
keep that balance? Do do you still draw?
Do you
have you managed to sort of keep that
hobby
alive?
Yes and no. It's it's gone in cycles
like anything. It it it's it's energy
right? I mean if if I have
Uh been a time in my life when I had a
lot of energy and every night I would do
um
some kind of art project or or something
just just for fun. Try to enter in
shows. At one point I was a a member of
uh
art alliance from the Hampton Roads
area. So I would be constantly going to
and from there. But then, you know,
you get tired of that and
the cycle cycles
cycle out of that and then everything
gets packed up and put on a shelf
somewhere and you don't do much.
Mhm. But
Do you think that advice is right though
about
art you know, a job that pays well and
not do art? Cuz I
I've heard this a lot that some people
are encouraged away from the arts
to go to a safe job like being a doctor
or being a thing. I mean, when you look,
do you think that was the right choice
or do you know friends and family who
have
you know, made a living out of
uh art?
My sister's made a living out of art.
She's she's um done very well. She's
actually a professor of art up at uh
Oh, wow. in in your your in a college.
Yeah, it's um
I don't know if that was the right
decision or not. I mean, the the the
professor that I I trusted the professor
who who told me that and I felt that
that was the right decision at the time.
I you know, obviously if I had if I had
made a different decision everything
would you know, wouldn't have the same
family and wouldn't be you know, So it's
right from that perspective that what I
did.
But yeah, I I don't know. It's it's
really hard to hard back in time and
Mhm. and say You know, you contributed a
lot. You've done a huge amount of
things. Is there anything that you still
feel is unfinished? Is there Is there
anything that you like I really I have
certain things I really would like to go
and do or achieve that I haven't yet
done?
No, I don't think so. I mean,
when I was younger, I would be um
uh of course there's always the dream to
become uh another uh Brown Van Lear or
you know
some
surname
Einstein or something.
Yeah. You know, but that that eventually
you say, "Okay, this this is what I'm
good at. I
I'm well organized. I can figure out how
to do do these certain things and and
you know, I I
I'll just enjoy what I'm able to do as
best I can do it, but
I'm not going to solve turbulence
modelling.
That was also a dream, too, at one
point. Like, "Oh, this model is going to
Everyone's going to use it. It'll be the
best thing to come along." But now, it's
it's um
So, just just
bow out gracefully at some point, say,
"Oh, well, this this is
Yeah, no, it's it's it's true, isn't it?
It's like almost
That's why I enjoy speaking to people
like
Florian or Philippe who
who are the rare individuals that you
could say have
done that thing of having a surname
after a model, but I guess the sad
reality is that for most people that is
never the case. And I I remember the
same thing during my PhD thing, "Oh,
yeah, well, if I sort of move this
equation around, if I can fit this line,
maybe this could be a new model." And
you know,
this could this could be it, but it's
actually much harder than you realize,
isn't it,
to do it?
Oh, yeah. Yeah, definitely.
And you know, it's respect to those
people, but I don't think whilst it's
amazing what they've done, I think
actually what you've done, which not to
uh put what you've done just in one
circle, but I think
where I genuinely think you've done a
lot of
help to the community is the curation of
knowledge.
You know, you can have the best models
in the world. You can do the best
research,
but I don't know if you've consciously
focused on this or not,
but you've seemed to have done a lot
about documenting it.
You know,
like when I look at what you've done
with the turbulence modelling website,
with the high-lift, with CGNS, with the
codes, like you seem to be very good at
documenting and curating that knowledge
and almost that public dissemination
knowledge. Is that a conscious
thing you do or you just sort of slipped
in to the to the
Probably probably just slipped slipped
in. Yeah. I mean, I saw the need for it
maybe and just did it because it it
seemed like the right thing to do. But,
I I I think kudos to you for that cuz
genuinely, if I look at all these
new methods, these new startups, new
methods, they almost all are showing
that they've validated their thing
against some basic turbulence modelling
website case.
They've almost all taken some high-lift
case and running it. And I know it's a
team effort, but a lot of that has been
basically put on a website, curated,
organized
by you and a few others. So, I would
argue whilst your surname may not be on
a particular turbulence model, I think
you've still actually had AI think you
can have a tap on the back for
for contributing. That's That's nice of
you to say that.
And I I think others would would agree
with me, too. So, I Yeah, I really um I
really appreciate you speaking and um I
have certainly learned something new. I
had no idea about the arc connection. I
didn't know that maybe you could have
been a spy. That's
Never too late.
Not a good one, though.
Um but, yeah, and uh I hope we can um
uh have a good chance to catch up at the
uh at the high-lift workshop.
Coming up.
a nice way to sort of I don't know about
you, but I'm after that, I feel like I
need to have a break. I think August
will be a a good time for Yeah, this
next month is not going to be fun for
Yeah.
collecting everything and
getting it ready.
Yeah.
Brilliant. Thanks Chris. Really
appreciate it.
Sure Neil.
Cheers.
Yeah.