We are starting a small simulation special. Last week it was all about batteries. This week the focus is on gas turbines by Siemens Energy. Behnam Nouri is the AI expert at Siemens Energy. He talks to Peter Seeberg about the simulation of gas turbines and explains how he and his team were able to reduce the calculation time for simulations using machine learning. Peter is traveling with an unreliable Internet connection. So the news part is a bit shorter. Robert Weber talks about Sora and what the new genAI model means for the engineering process.
Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.
We thank our partner **HANNOVER MESSE**
https://www.hannovermesse.de/de/
Job offering: Heilbronn is looking for a professor
https://cdn.hs-heilbronn.de/ce3c0933328f570c/d9e7413e6a15/232-P-IT.pdf
Sora Video https://www.youtube.com/watch?v=fG3IE9dkyKY
Stefan Suwelacks Posts https://www.linkedin.com/feed/update/urn:li:activity:7164215963301703680?utm_source=share&utm_medium=member_desktop
Benedict Evans https://www.ben-evans.com/presentations
Behnam Nouri https://www.linkedin.com/in/behnam-nouri-33381063/
This podcast is presented by Hanover Messa your leading event for industrial AI hello everybody and welcome to a new episode of our industrial AI podcast my name is Robert Viber and we have a very weird situation because normally Peter and myself record this episode but Peter is traveling with an unreliable internet connection in Asia so now we could have
Gone and clone his voice instead Peter asked me to share some topics with you first a reminder from last week’s podcast from chiao Pang Lee he suggested that while the US and China have been leading in geni development by building Foundation models the opportunity for European companies maybe in creating
Value back by applying foundation models and we know since the weekend we have a new Foundation model it’s called sora and Sora and Industrial use cases how do they go together very very fine and Stefan Zak has explained it wonderfully and I have a quote for you so Stefan
Wrote Sora is a glimpse into the future of engineering okay the result of the text to video tool from open AI are not only amazing to look at but they reveal an incredible understanding of physics and that is important I don’t know if you know the video by Sora with this
Ships in the coffee cup and what is fascinating for me and for Peter and for Stefan maybe also the flags are waving in the wind and the ships moving along with the waves light bouncing of the water surface very very rich realistic in order to render the scene the model
Has to First implicitly construct 3D models from the text input whoa compute the physics of the scene render the scene and now what comes together these are threee core task in most engineering workflows free core task and Stefan is absolutely right geni is not only for marketing it’s also for industrial
Engineering workflows wow and one comment by myself if you want to know more about the technology of Sora I link to a YouTube video in the show notes so Peter is in Asia I was in Austria to plan our event AI in the Alps thanks to hanoa Messa for being our event partner
And our guests this year are Professor Dr Yannis branter together with Dr Sebastian Lena they will hold a workshop on simulation and AI our guests are trustifi they will talk about certification and our guest is Professor Dr Marco Huber from frown hover who will discuss AI use cases and
One spoiler we will have an episode in simulation and AI with Johannes in the next weeks my news for today I have a reading suggestion a very interesting article for all the decision makers in the industrial sector by Benedict Evans It’s called Ai and everything else more
In the show notes so that was it I hope I got all of Peter’s topics correctly we will now move on the main part and hopefully Peter will have an internet connection by the next time so Peter all the best to Asia enjoy your holidays and see you hear you soon bye-bye
Hi there welcome to a new episode of the industrial AI podcast my name is Peter seberg and I’m your host and my guest today is benam Nuri he’s the AIML tools expert for knowledge-based engineering and platform design with seens energy benam and I are going to talk about design process acceleration by
Means of integrating machine learning hello benam hello Peter thank thank you for inviting me you’re welcome looking forward to what it is you have to share with us let’s start with you introducing yourself to our industrial AI podcast listeners please hello my name is bami I’m working now since 15 years on
Turbine optimization I started to work with Ros Royce on a project called virtual turbine where we virtualized the full turbine the first time and then I switched 10 years ago to zens energy where I was responsible to building up big process chains big multidisciplinary optimization process chains of high
Effici Gast tobine blades and veins and since five years I’m more and more interested in integrating machine learning into our process chain because we saw there’s a limit of computational time and what we really require to get a robot design so we integrated piece by piece machine
Learning tools into our process chain to get the designs faster and more robust and here we collaborated to Zan energy with Zan’s digital industry together to create some products which I will talk to okay good yeah you and I we met already at the first AI INX conference
That Robert and I have started it was actually the AI in what we call the monastery yeah that was in word spoke right and I do recall also I kind of from the very beginning that we met kind of linked you always with turbines and that’s what I’m going to ask you maybe
To give our listeners a small introduction into turbines you just mentioned rolls Wars the first so the first thing I think oh turbine that’s has probably has something to do with with these can I call them engines I don’t know but you will tell us that you
Know make sure that the plane will fly I think later on we’re going to talk about gas turbines so I think there’s different type of turbines what is a turbine and why will we later on be talking about them yeah a turbine is in short like a machine which converts
Mechanical energy into some sort of rotating energy and then you can generate electricity which we do at cement energy or Ros we just generate trust and push the airplane forward and V turbine it’s working the same so we use the nature the wind to rotate it big shaft and the generator and generate
Electricity and the same applies for coal power plants so there we have steam turbines right we’re not going to go into the details of the politics behind it but you already mention so I think we concentrate maybe later on on gas but the same could be done you say and maybe
You’re going to tell us a little bit more about your company as well and then we’ll hear that wind or coal and there was one that I heard about and I just want to share it because I think it’s rather interesting you should confirm if that is actually correct understanding
Of me but there is what I would have called 100 year old project I would have called it a water turbine but I think the official English name would be more like Hydro electric power station called valan that’s south of Munich uh and that started exactly January 26
1924 so what you then call that and I do recall that I visited there maybe 10 years ago and what I was seeing there so when where the water falls down that would be a turbine as well right yeah in the end that’s a water turbine so you
Use the force of water and height difference to rotate a shift and power generator in the end the the first power generation in the US and Germany were all driven by water turbines in the beginning and then they started to use cold okay yeah and now we we want to try
To move away from coal I guess towards wind and probably also still gas is what we’re talking about okay very good yeah I recall that 100 years ago and the guy who who proposed it the engineer and then the the responsible people or I think what they said is like in town in
The city of Munich not all the streets were having lights people were not having electricity necessarily so they said why are you going to build this why are you going to build this and now 100 years later they’re very happy to have already Alternative Energy turbines so
Tell us a little bit more about seens energy I think it’s separate from seens but still connected related and maybe you’re going to share with us what parts of the energy Market that you play in yeah so zens and zens energy we were one company so I was hired in zans a 10
Years ago and in 2020 p decided to divide this into two companies so zens I they still hold like the trains and the digital industry where we collaborate closely together and zement energy we have all the transmission business so smart City business uh we have Electro resers to convert electricity and water to
Hydrogen and we have gas turbines and wind turbines which our our main business yeah and we we really care about also the energy transition and here gines play also a big role because they are reacting very quickly so with this renewable energy Revolution and like countries where you have a lot of
Sun and H the people are going home the sun goes down and suddenly you need a lot of energy at evening to power up all the air conditioning and here gas turbines play a vital role so in all countries so even Saudi Arabia Texas or California they only need gas even if
They are blessed with a lot of sun and wind and yeah also Germany the Gast toine demand needs to be high because we know we have periods of up to two weeks where there’s virtually no wind no sun and then you need a resource which is
Reliable and you can power gines in two different ways so one is the combin cycle where where you add the steam turbine at the end there you can reach up to 65% efficiency with our best 9,000 HL turbines and a single cycle where you just power a gas turbine and you can
React very fast can just turn it on for one two hour and then turn it off again okay thank you very much for that just that you know some of you listeners will have known that but some and including myself maybe have not been that aware I
Think I’m going to ask you one final question on what should I I mean these we’re talk if we’re talking a gas turbine it’s a huge thing what what is the size what should I think of when I see pictures yeah so the size of a gasan
Could be like a Berlin double de bus it’s 12 13 M long a diameter I think of up to 3 4 M and the nice thing about gastan is they have a lot of power in in a very concentrated space so one gas turbine can generate 300 400 megawatt of
Electricity and this you can build in one building compare this with wind turbines where the biggest onow wind turbines have 6 megawatt and have a length of 200 M so you need a lot of them with a capacity factor of 20% that’s a typical capacity Factor here in
Germany okay that’s like a factor I don’t know Factor 80 or something like that yeah so so if you want to replace like 300 Mega gine fully with winin yeah you have to multiply it by five and then divide it by six and then you know the
Numbers of wind turbines you need good thank you for that today we’re talking about uh the acceleration of the design process I assume that we’re probably going to use the gas turbine as an example and your claim that I’ve read is that integrating machine learning into the design process has the potential to
To decrease the Computing time from Days down to minutes maybe let’s start that you explain us how today or how yesterday your design process worked and then maybe you’re going to tell us how your changing it yes so we introduced Gest Prov with an efficiency of 60% in
The year of 2000 and since then we increase efficiency up to 5% and every time you increase efficiency by 1% you have to get 100° hotter turbine entry temperature the thing is we haven’t found any metal our competitor also not any metal which just can hold 500°
C without melting so we need a lot of very complex geometry and cooling Technologies coating and then you need a lot of simulations to verify that all the disciplines you need to calculate to verify the turbine blade fulfills it is increasing exponentially like in the year 2000 with it’s hundreds of
Calculations and now we have to do 100 thousands of calculation in all disciplines like CFT FAA conjugate transfer tell us tell us tell us one or two of these acronyms what they stand for FEA for example yeah FEA is finity element analysis which is used for structural mechanics that we have very
Time consuming elastoplastic calculation which takes on our best computer cluster up to 70 hours and CHD is a conjugate heat transfer where you use cftd to solve the energy equation with the fluid and the solid to get very accurate temperatures and here you have to be very very accurate so you
Need a very fine mesh and which is very time consuming and CPU intensive okay so what is the tools you you use for exactly accelerating you say you know where maybe in the past you would needed days you can do to days in minutes are they internal tools is that products you
Only use yourself are they tools that are designed by maybe semens themselves and they are available on the market or what are they exactly yeah so so every guine develop we have a combination of commercial tools and in-house tools so you have in-host tools which built the
Geometry of the turbine blade and then um commercial tools like ANM hour course they just help us to do the simulation so they are very computational intensive and then in the last 10 years we introduced process chain so before it was always one designer is doing the ER
Dyamics the second is doing the thermodynamics the third the structural Integrity test but but now we we combine this more into a process chain so we can calculate all disciplines at once which also takes a lot of time yeah and then here we introduced our first machine learning models to surrogate some of
This disciplines piece by piece okay what type of machine learning are we talking about can you not go too detail but at least at the level of are we talking yeah what kind of machine learning is it reinforcement learning are we talking generative what what is it that you’re doing that you introduce
That will you I mean simulation I think you talk about the simulation process simulation has been done long time even when I started computer design 40 years ago but what is it that machine learning is uh is changing so to say yeah so even 20 years ago there were some simple surate mods
Very basic linear polinomial Sur Gates which you can replace basic scolar relationships piece by piece but now we we explored so we had a we had a competition here at zens was called Innovation F and then we we applied for it we pitched in front of the CEO and
Then we won small budget to to explore a lot of Technologies and there we tried all kinds of machine learning models which were suitable like random Forest CNN BNN so basan neuron networks and and we just tested which of them gives us the best prediction with the least
Amount of training STA and can you share with us which one was the winner or is that internal information you would rather want to keep internal yeah published some papers about this oh you did okay yeah yeah I mean lot of good models where basan and the Goan process
They were both quite good and then what was also very important is that these machine learning models could predict their own certainties so you could ask the model what efficiency will this turbine blade have and I can say Okay 90% but I am very confident or I am not confident and
If it’s not confident we say okay then we need more trainings M so we need more simulations to make the moreel more confident and nice thing is about this model is say yeah you need to trade data create simulation data for weeks train for minutes and then the predictions is
In seconds milliseconds and suddenly you could go from thousand members to 100,000 members and then you can you can upgrade your the probability or the like yeah the probability of the model that it says itself so and then at a certain point you’re going to be happy with I
Don’t know at 90% or where where’s that’s it’s a it’s a very general question it’s always asking like yeah yes as you say we’re we’re working with with with probability machine learning probability basing models go models at what point is then there the cut off where you say okay now now we’re not
Going to put more effort into because we’re happy with x%c yeah this this is case dependent this is really case dependent sometimes we are happy this 98% sometimes a very intensive calculation we say are happy with 90% confidence it’s really case dependent and then really also what is the optimization criteria now interesting
That you mentioned Bose the basian uh I recall Bas is it the what what was his name far well I forget the ter now the um is was a kind of priest where Bas or you know G you know they are they are all I guess we call them part of the
Machine learning but they approaches that are not you know not from the last couple of years right I mean we’ve been doing day for many years now but and that is that is the question on one hand you call them out specifically and I understand you are
They help you so they help you move forward then now the other I’m going to combine this the question with what about you know newer approaches like a generative design whatever the base of those approaches is is that something that you’re looking at as well I mean
Does generative have a role to play in again accelerating the simulation design process yeah exactly so these are our newest research projects where we look into generative design or also on 3D surrogates so surrogate model where you can just give any geometry and then it can give you all the predictions you
Need but this is really the newest type of technology and then here we see also all the small startups which are building this S Gates so they all have different models they all have different approaches so so nobody has found now this is the right way so this is still
In a think explorative phase so where where every company is testing what is best also we are testing it with some PhD students they just use different models which have been used for complete different applications like image recognitions or some biological like medicine applications and then we used
It on simulation data sometimes they were very successful sometimes not but this is something we now have to explore and I really believe in 3 4 years we will be in a stage where with some small piece of training data you can build like a generative AI model which can can
Do all the predictions which takes weeks in seconds minutes now when you talk about predictions I’m going to ask you a question that turns it almost the other way around it’s like you just mentioned I think healthcare so I’m not sure if we’re then talking reinforcement
Learning I think we talk it we call it still generative but it’s like so designing the new proteins right and I know exactly I know what the outcome needs to be so I have a number of variables uh whatever that is in healthcare I have no clue but you know
There’s a b c and d and they need to have specific values because that’s the new medicine I’m looking for now my question of course then is going to be you know we heard from you what it is that you need to do is improve efficiency from 60 to 65 so you still
Have 35% ahead of you for the next couple of years yeah that is a car no limits per so there’s a thermodynamic limit yeah I understand that but nevertheless so the question then is do approaches exist and because that would in my understanding typically be generative to say you know I need an
Efficiency of let’s say let’s be realistic you know 68% and you tell me how the design looks like is so it feels like the other way around does that exist does that make sense yeah yeah so we have optimization criteria yeah so you could say okay
Let’s push it to I don’t know 67 68% efficiency and then see what is the condition into the terb blade so you can push the temperature quite easily the simulation up and then you have to verify that all this the other disciplines hold and this is the the
Difficult part so you have your metal geometry you have your coings your coolings and then you have to do all the simulations and then verify all the dis IPL so you have aerom mechanical discipline plop plastic discipline and all the disciplines needs to be into our review criteria because our customer
Want to run the turbine for years not replace it after every year run and you want don’t want to have it to be the size of two buses it should still be the size of one bus or or smaller I guess yeah even even more compact would be
Nice yeah okay maybe if you can you can walk us through a number of ation so I believe it’s one step more detailed tell us a little bit about you know what exactly it is that you do in the different approaches maybe how you do it we just talked about different
Approaches is that something you can do yeah so so now we we have this collaboration zens energy and zement Di there’s a tool called Heats and heats you you build workflows so you can couple your geometry building tool with your CF tool with your FAA tool and do simulations and here we developed
Together a tool called Heats AI accelerator and heats AI accelerator Works in this way that you run your process chain on the classical way so you do simulations which takes a lot of time and after 100 member uh the tool automatically checks builds a surrogate model and says can I replace the
Simulation with a given certainty so it always checks so it’s doing a decider so inside the process chain so maybe the member 1001 is not good enough but after a certain time member 200 300 400 you see that the AI replaces more and more simulation so maybe in the beginning 10%
And later 30 50 80% and then you get a very good boost and you accelerate your optimization by a big factor this is one tool where we put it in yeah let’s let’s stay there a bit because it sounds magic that’s why I’m quiet it sounds a bit
Like and it’s probably not but even if it’s not you can then correct me it sounds a bit like you know I started looking into chess and I’ve probably mentioned that when what was that Alpha go and then Alpha zero I believe but that was more about the algorithm only
Knowing the rules so of six so the rules would be you know the rules of whatever thermal uh design uh the details of your engineering but then it it runs Millions it does millions of games and at the end it’s it feels a it fell to me a little
Bit like you know it becomes more certain and certain and then at some point in time it starts winning and then it’s um You Can’t Win anymore from it maybe that’s a different approach from what you with it’s a different approach yeah it’s more like you have a a surface
And all this surface you have pressure temperature and other like results and then you you train the artificial intelligent what happens if the turbo plate is a little bit lean pressure such side or it’s sicker sinner yeah it changes the shape so we we are changing
The shape and we giving the AI that happens if we do the time I’m blade sicker remember that and then piece by piece it learns from it and then after a while it says okay now I’m sure I can predict all the geometric optimization you’re doing with the surate and that’s
The the thing and it’s not like chess you you can just train one surrogate and it it builds the turbine blade for any engine you have to retrain every time so every every engine type 50 htz 60 htz bigger smaller and then also the different turbine components you have to
Retrain model very good you are going to share a a second third application with us yeah so the second application which is really important for us is also the the Robos design aspect so the the thing is with optimization you can get a very good nominal design so you can design it
On Peak Performance Peak lifing and so on and then it could happen that you are on the response surface on the tip of the Eiffel to so any shaking any variation on conditions so you run the engine in cator in Iceland is a different condition or from casting you
Always have some variations in w s and so on so you have to test your your best designs on all the variations and there are a lot of variations so you have boundary conditions geometric so you have to test millions of millions of variations and again here you could do
It with simulations but this could take years of calculating or or trainer surrogate and then the surrogate can give you some sort of uh robust design response okay and this is one of the examples where you say this thing can happen you know within a I don’t know
Minutes but a very big factor faster than you could do it in the past exactly yeah several orders of magnitude faster if you yeah trained the model with the right data then you can get this robust design answer okay I think there’s one one more on creep is that right that you
Can talk about yeah so so creep is a a l of plastic calculation it takes a long time because you have to recalculate every time step with FAA so you have to do the calculations thousands of times share with us what is creep because we we did this before there was a
Misunderstanding I think between what I call drift at that time and I thought we were talking about a running machine machine kind of slowly moving towards giving data that are different and the model then starts reacting but creep is something different right yeah so so if
You have a piece of metal and you eat it MH it gets larger elastically and then you cool it and then it goes down oh okay that’s the creep but if you heat it too much and pull on it with a huge Force which we doing on the G then you
Get it to the elastic plastic deformation all right yeah and then we we have to calate this inside our design limits okay that sounds almost like how you produce uh metal objects or not yeah when the steel is when this when the steel is fluid or not you can you can
Put it in the form that you want and then you cool it off I guess or t g is very very hot yeah right right right so creep is that is then something you can basically say you do not want to happen or can you say it will happen will
Happen but it to stay inside the limit so we can verify for customers okay and what do you do then to calculate creb now with the new machine learning based approaches yeah so we again the same thing so we do FAA calculations and we saved the data in a specific format and
Then now we are testing several model to predict this time intensive calculations and then we can do temperature variations geometry variations and then the the simulation which took several days on 500 CPUs can be done in yeah seconds that’s good yeah that’s really good yeah yeah so then other other
People can use the same CPUs uh in the time that you don’t need it yeah exactly yeah so there’s always a competition between all the sure disciplines compostion aerodynamic everyone wants CPU yeah access to yes on one hand you know we hear you know everybody wanting
To buy the what is it h100 Nvidia or whatever it doesn’t matter what processor and more and more and more we all need to design chips Etc but at the same time if you say you know we’ve been using specific approaches and we can now
I don’t know what the number is I just say is it 10% or 1% of the CPU time to do even better things then that’s that’s almost the development on the in Z direction right yeah the thing is in the past you need 10,000 CPU hours to design
A 60% toine blade but now you need Millions so so the number of simulation that you need to create a high efficient product goes exponentially up yeah like the CPS all right so bottom line we still be more than yeah this Nvidia a100 V100 they get very important for model
Training so this this highly efficient gpus we we did benchmarks so all the C took us weeks to train and on the GPU hours so that’s why the Nvidia Shares are going up so much exactly you need this TPU power for mod training and of other companies trying to get into that
Market I’m again reading Patrick galinger from Intel but that’s another topic we’re not going to get into today we’re actually going to come to a close here maybe you can share with us what is the what is the status of you know the topic of today applying machine learning
To the industrial simulation design process if you look around the world you gave already as you are aware where your turbines as an example are going to be placed in around the world you talked about C Saudi Arabia just as as examples so where are you where is your competition
We don’t need to know names but how is applying machine learning to the design process what is your view around the world and how is it going to change what how do you see the machine learning into the design process change over the next you know the complete way of doing
Things over the next 5 to 10 years yeah so so in our industry I think it’s in proof of concept phase so we using it for some very challenging designs and putting it piece to piece to all designs and I think all our competitors are in
The same phase so it’s getting piece by piece integrated into our design process some Industries like the Formula One car design there a little bit further so what I heard is say completely optimize on the surrogate so simulation is just there to feed the surrogate model with more confidence and
Then the optimization is completely done on the surrogate and this is I think the way it will go either you make parts for cars for turbines for Aero engines or airplanes data will get more and more important so you need to save your simulation data and feed piece by piece
Your surrogate model until your surrogate model can build every piece of your product with a good confident and then you can design faster and better and that’s I think will be the the change so in the past it was always you do simulations to to design to verify
That your cut geometry is in the right shape and can be manufactured and I think in the in the future in 5 years and 10 years we will all feed a big generative a AI model and then um this generative AI model can be run by experts to do very very challenging
Designs much faster let’s stay as a final on on this very topic I want to understand so is this geni model then is that you know some kind of foundation model is that an uh industrial Foundation model that we have talked about in the past for the last year you
Know everybody’s talking about it I haven’t seen it yet or do you see that uh while it may be a foundation model as such that could then be generally available to everybody at the same time you’re going to fine-tune it as one option I don’t know with your specific
Seens energy and you said Formula One whoever and everybody’s going to use their isid then simulation or whatever engineering Knowledge from the last in your case 170 years or whatever is see is what what is the the approach there of both the foundation and the specific
Knowledge yeah I think it will be a combination of models it could be a big foundation Model A large language model which would help like a complete new engineer to guide him how to design so it really would answer questions and guide him what steps to make but I think
For all the predictions and so on it would be really a combination of several VAR variable Auto encoders GN MLP models which together do the best predictions with the data they have right so still you as Seaman energy would be able to to produce your gas turbine moving towards
I don’t know what your goal is we just heard 60 65 70% and your competition they will use their own engineering Knowledge from who as as long as and maybe some other ones will use new ways of doing things but it could be on a common Foundation model then the
Question of course is who’s going to produce such a foundation model who has the interest in doing that is that you know does it need to come from industry like you know from seens and together with competition as far as that is allowed or who would who would do that
Yeah that that’s a big question yeah okay will we have a lot of models from a lot of startups or will someone in invent a model which can understand all simulation and Engineering data let’s see who is going to come up with such a model it feels
Like the more interviews we do around generative AI that it always comes up let’s see who going to who’s going to be the first and and who’s going to be the second third if we’re going to have this one big or if we’re going to see in the
Course of the Year many different uh in this in our case interest specifically in industrial Foundation models bam thank you very much thank you Peter the listeners if you want to get into touch with benam you can do that best on
LinkedIn bam B eh h n a m NRI n o Ur R I will put the is LinkedIn profile in the podcast notes as well otherwise if you the listeners have any questions comments please send me a short email Peter ai. de uh I’m very happy that you
Stay with us so far looking forward to have you with us again and Benham thank you very much and have a nice day thank you Goodbye