Shadowing-Übung: Real-Time Digital Twins Using CFD, Sensors & AI - Haris Kokkinos | Podcast #165 - Englisch Sprechen Lernen mit Video

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And I would say that this is the one million dollar question.

This is the biggest problem, especially at the beginning of a project when still nothing has been manufactured.

The loading scenarios are not finite, they are infinite.

This is a technology that is not still very mature.

It's not easy to have the same engineer who has a safety background or a structural background for books to read.

Now you can ask ZZPT.

Ladies and gentlemen, welcome to another podcast episode here at Realize Life in Amsterdam 2025 today with Radis from FEAK.

Welcome to the show.

Thanks for having me.

Can you give us a short introduction of what you are doing at FEAG

and what are you guys working on on a day -to -day basis?

Right.

So FEAC is a Greek -based company and our expertise is numerical simulations,

digital twins, and in general digitalization technologies.

So, most of the time we are working with, for example, with the Siemens portfolio,

with all the software, providing either support Okay,

let's just delve straight into it.

What are digital twins in the first place and how do you define it?

Because the thing is, a lot of people define it differently, right?

Absolutely.

And I would say that this is the $1 million question, because we've been in many discussions,

and you can see that everyone has a different understanding of the term digital twin.

So, there are some people who define digital twins as collecting data from sensors.

There are other people who believe that a virtual reality, augmented reality is a digital twin.

And others who believe that by performing AI in databases,

this is also a digital twin.

all of them are right.

So this is the interesting thing.

In our case, the way we approach this term is that we We base ourselves on simulations,

physics -based simulations, and then we consider as a real digital twin when we combine the simulations with sensor data and AI.

How much overall does physical testing play in this whole concept of digital twins?

It's important because at some point you need to validate your simulations.

So this is the biggest problem and especially at the beginning of a project when still nothing has been manufactured.

So there you rely on synthetic data and you just develop the technology but at some point you really

the audience probably also wonders.

We always talk about digital twins and in every podcast I had so far, people compress it into one sentence, you just build a digital twin.

But obviously, there's more steps involved to it, right?

So how do you actually build a digital twin?

How do you get started?

And one of the different steps to actually build a fully like a digitalized twin of a component, for example.

I have a nice example because we just finished a project for the defense sector,

for a naval vessel and our role was there to build the digital twin of the vessel.

Okay, let's strip it down and make it more precise.

So, The goal was to build, let's call it the next era,

health monitoring, system of the hull of the vessel.

So we started with simulations, basically CFD simulations and structural simulations.

Because it's a vessel, so you have the waves coming in, you have pressure on the hull, and then this creates stresses.

So, this is the workflow.

So, we did the simulation part and then it was important for us to exchange data with the sensors.

So, we performed a sensor placement analysis, we received data from strain gauges and accelerometers.

And then we applied AI algorithms some of them call it reduced order models,

in order to make the time demanding and computer demanding simulations being able to be solved in real time.

So, I would say that the workflow is simulations,

in combination with sensor data and then on top of that reduced order models

to end up with maybe what we call real -time digital twin or executable digital twin.

What would you say, Chars, is the most critical bit when building a digital twin?

Because I can imagine some things add friction to the system, in the sense of that things are a little bit more complicated than you anticipated before.

The biggest problem is training.

is the training of the AI part.

Because in the real world,

you have the scenarios, the loading scenarios are not finite, they are infinite.

So this is the biggest problem.

So how can you create a digital twin that can predict real -world scenarios only by testing it as much as possible.

So, I would say that in my opinion

and as far as we have seen that this is a technology that is not still very mature, we are working on it,

on the technologies and on all the Technobricks, but it still needs some time.

So you've mentioned some Siemens products before, Simcenter products.

What is your favorite product that you use on a daily basis?

If you use it on a daily basis?

Personally, it's the Enix Simcenter 3D platform.

But this is because myself, I'm a structural engineer.

But my colleagues, some of them use StarCCM a lot.

Some of them use Amesim, some of them use Mendix.

Team center.

So in the company at FEARC we are working really with many tools from the Siemens portfolio.

Why Siemens products?

That's a good question.

We didn't start with Siemens products back in 2014,

but then it was time for us to switch because we sensed

that the workflow that we needed to build for our work we needed tools that are integrated,

that can be combined with easy exchange of information.

And this is why we chose Siemens.

But there's also another factor, and this is that Siemens,

to my knowledge, is the only company that combines simulation with hardware like PLCs.

And you have you have edge computers, so you have the whole palette.

I'm wondering also from an AI perspective, right?

You mentioned AI before.

AI is a very broad term.

So how do you actually use AI?

I mean, in the context of digital trends, right?

But what do you think are some of the misconceptions?

How do you use it personally for projects?

And where do you think AI is heading towards in the next five to 10 years?

Yes.

So the way we are using AI is...

a bit on databases for what we call this data -driven process.

So we use AI to easily collect valuable data out from a very big pool.

But I would say

that most of our efforts are focusing on the reduced order models where we have to apply deep learning

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and we recently also work with what is called physics in four neural networks and so on.

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So, these are the two fields that we are mostly active.

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Can you go a little bit deeper into what are actually ROMs and Yes,

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so myself I'm not an AI expert but as a technical manager I need to have the overview.

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So reduced order models I will try to explain it as simple as possible.

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For example a CFD calculation for a vessel, for a ship.

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turning propeller, a high -fidelity model,

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this might even take some days to be solved for a transient analysis of several seconds.

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The reduced order model is that you solve many of these high fidelity models

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and then you apply AI so as to make the extraction of this information almost again,

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like extracting information out of a database, but in this case the database is the simulation results.

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So this is my explanation that I would give also to my mother.

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What about pins then?

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How do you use pins?

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PINs, we started trying them several months ago

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because we saw from the community that it's a possible new way

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or technology to increase the accuracy of the reduced order models.

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In this case, the pins, again to make it simple, is what we said before,

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the reduced order models, but on top of that you have to apply some boundary conditions which are based on physics.

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So, this should improve the accuracy.

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We are working on that, but I have to say that in general, reduced order models, there are some applications where they

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can be applied more easily like for example structural analysis

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or thermal but in CFD with turbulent modeling and so on it's still not that precise or I would say difficult.

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You founded the company around 11 years ago?

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Yes, 2014.

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Yeah, 11 years ago, right.

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I'm wondering how do you think the job role of the engineer changed in the last 11 years

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that you've worked in FEA or working with FEA since six if I'm not wrong.

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And back then I was working previously at CERN in Switzerland

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and I remember 2010 it was still a topic of discussion

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how to rely on a CAD centric model to build a the simulation.

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So that was already something really important back then.

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So things have evolved, of course we now have, if you have a look at the ZMES platform,

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it's a vast platform, so we have 3D simulations which is at least what we knew back then.

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But you have nowadays a machine, one -day simulation.

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It's an amazing tool that requires several seconds for the model to be solved.

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And you're not losing at the end of the day that much in terms of accuracy.

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So I believe that...

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They...

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biggest development or advantages nowadays is that you have tools that are interconnected and

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you have easy exchange of information and this new hype.

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But it's not always a hype.

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It also provides interesting results with AI.

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But going back to the job role of the engineer, do you feel engineers have to upskill themselves when it comes to AI?

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How do you do it yourself?

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Oh yes, I believe that it's a different persona.

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It's not easy to have the same engineer who has, good in AI.

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In my opinion, and at least as I see the degrees, the university degrees, and how students coming from universities to us,

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is that yes, they have the engineering background

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and then they need to do some kind of a master thesis or a PhD on AI,

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because it's a different kind of expertise that is needed.

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So it might be the case that the same persona has a strong background in CFD and AI.

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It's not very common.

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So it's good for some people to focus on specific things and for some others to focus on other things.

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But it's funny because in our case we have The main, my colleague who is, Yanis,

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who is working on AI, he's a physicist, so he's not from an engineering background, so more theoretical if you want background,

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but he's our AI expert.

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I see.

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You mentioned you have a background in structural mechanics, right?

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A lot of people who are watching this podcast, they want to know how to delve the best into the topic of FEA.

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How did you do it?

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Or how would you advise people to do it if you could go back in time?

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Okay, back then it was even quite difficult to solve 3D models.

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It required a lot of effort.

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But I was quite lucky because I was at CERN and I had the possibility to combine simulations with

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tests from the laboratory.

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So, I was lucky because I could validate my results.

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So, I don't think that this has changed even throughout the years.

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This is still a must.

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I know that it's not easy and it doesn't make you a better engineer, but it just gives you more confidence in what you are preparing.

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So, what has changed significantly is the exposure to information nowadays to engineers.

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First of all, you have ChatGPT.

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You can ask not everything but almost and you can have instant replies.

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Back then, we were searching for books to read, now you can ask ChatGPT or even the internet if not ChatGPT.

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I don't think that I have a great advice for engineers.

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It's just to practice, learn a platform, for example, like Simcenter 3D, as good as possible.

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At the end of the day, all these simulation software, they work in a similar way.

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So it's not like you need to have expertise in a specific software,

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but it's really the theory and the applied knowledge on FEA.

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And then doing models and find your tips and tricks to overcome problems.

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So that probably would mean that you also have to fail a lot in the process.

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Absolutely.

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You think it's a necessity, right?

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Yes, it is a necessity and something that at least I did is that, for example, to deliver a project,

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I could have solved, let's say, 10 models, but I was solving 30 models.

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just to gain this confidence and to test what -if scenarios,

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just for me to be prepared if something goes wrong or if the client asks me, "Why is that?

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And how would you explain these results?" So, it takes, of course, failure is an option and you learn a lot.

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And also, in the very beginning, until you gain this confidence, it needs a little bit more effort.

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Walk me through, how does a typical day of Hades look like?

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Yes, so now I'm holding the position of the technical manager of the company,

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so I need to watch out for projects that are developing.

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Also on the Siemens partnering side, we support our clients, so we provide them support and training and so on.

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because we have also a consulting part and the Siemens partnership.

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Also I have to travel a lot because we participate a lot in consortia for projects like projects for the European Defence,

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agency or the European Space Agency, where we have multinational consortia.

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So it's a lot of traveling, it's a lot of supervising,

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a lot in trying to stay up to date with the technologies,

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because nowadays I'm not doing hands -on work with simulation models,

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but I have to stay up to date with all the developments.

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because At the end of the day, I need, with my colleagues of course, we need to discuss a better workflow, for example.

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So this is a typical day and it does not last eight hours.

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I see.

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Okay, interesting.

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Yeah, I can tell.

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I think what would also be very interesting, Jalis, is the audience always asks me on LinkedIn, well, how do I get started with FAA?

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We covered that a little bit, right?

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But how do you actually pick, because you have a company hiring engineers, by what do you actually look at engineers?

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Is it like the deep physical knowledge?

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Is it the expertise?

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Is it projects they've done in private?

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What do you actually look for in people?

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That's a very interesting question.

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And nowadays I have a completely different view than I thought that I had a decade ago.

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So, Typically, an engineer or an employee believes that I need to have the best degree from the university,

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I need to have a master's thesis, a PhD thesis, which is, of course, this is fine.

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This is very well respected, but there are also other aspects that we look into.

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First of all, you know, the mentality or the personality of the person.

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But we try to understand this as much as possible.

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It has been the case that we chose someone with less,

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for example, without a PhD or without a master's thesis, but because we believe that he fits better in the team.

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Also what I find very interesting is that I.. don't like that much my colleagues to have walls,

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meaning that, okay, this is my thing and I don't care about what problems my colleague is facing.

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And because we are a team and we are at the end of the day, we need to succeed.

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So each one of us helps each other in order to do so.

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So even a CFD engineer might say something that a structural engineer might find helpful.

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So this is important for me.

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So you see some different topics.

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It's not always about the degree and it's not always about prior experience, which is of course important.

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But there are many more aspects to look at.

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That was interesting.

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Did we miss anything, Rals?

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I don't think so.

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We've talked pretty much about everything.

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If people want to learn more about FEAK, where can they find you?

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Of course on our website,

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which is feac .com on social media and mostly linkedin and

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Probably due to the various awards that we have nowadays,

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you might see us in different interviews and websites for aerospace or defense, something like that.

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Interesting.

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Thank you so much.

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Thank you.

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That was another podcast episode here at Realize Life 2025 in Amsterdam.

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If you want to have more information about Siemens or Simcenter, make sure to follow all the links in the description.

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And if you want to follow and subscribe, make sure to do so and I'll see you in the next episode.

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Wortschatz und Sprechhinweise zu dieser Lektion

Diese Sprechübung auf Niveau C1 basiert auf dem Video „Real-Time Digital Twins Using CFD, Sensors & AI - Haris Kokkinos“. Diese Wörter kommen am häufigsten vor: simulation, twin, digital, model, engineer. Dieses Video enthält 244 Sätze und 2890 Wörter zum Nachsprechen. Der gesprochene Teil dauert 21:16. Der Sprecher spricht gleichmäßig mit etwa 136 Wörtern pro Minute – ein angenehmes Tempo fürs Shadowing. 83 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen. 37 der 244 Sätze sind Fragen – gut, um Frageintonation und kurze Antworten zu üben.

Wichtiger Wortschatz in diesem Video

Die 15 anspruchsvollsten Wörter aus dem Video, mit Aussprache und Bedeutung:

Wort	Aussprache	Bedeutung
simulation Substantiv	/ˌsɪm.jəˈleɪ.ʃn̩/	Simulation
sensor Substantiv	/ˈsɛn.sɚ/	Sensor
colleague Substantiv	/ˈkɒliːɡ/	Kollege, Kollegin
workflow Substantiv	/ˈwɝkfloʊ/	Arbeitsablauf
thesis Substantiv	/ˈθisɪs/	These
expertise Substantiv	/ˌɛkspɜːˈtiːz/	Expertise, Fachkenntnis
delve Verb	/dɛlv/	graben, buddeln
validate Verb	/ˈvæl.ɪ.deɪt/	validieren
fidelity Substantiv	/fᵻˈdɛlədi/	Genauigkeit
consortium Substantiv	/kənˈsɔː.ti.əm/	Konsortium, Kooperation
persona Substantiv	/pɝˈsoʊnə/	Persona
finite Adjektiv	/ˈfaɪ.naɪt/	endlich
necessity Substantiv	/nɪˈsɛsəti/	Notwendigkeit, Nezessität
hype Substantiv	/haɪp/	Hype
hull Substantiv	/hʌl/	Hülse, Schale

Sätze zum Wiederholen

Kurze, vollständige Sätze aus dem Video, die du im Alltag wiederverwenden kannst:

How do you get started?
If you use it on a daily basis?
How do you use it personally for projects?
How do you use pins?
How do you do it yourself?

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	has been manufactured · we've been · have seen
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	has been manufactured · be solved · are integrated
Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache	people who define · people who believe · conditions which are

Aussprache, auf die du achten solltest

Der Sprecher verwendet 21 Kurzformen und abgeschwächte Formen, zum Beispiel I'm, don't, we've. Sprich sie in der kurzen Form, so wie du sie hörst.

Die „th“-Laute: thesis /ˈθisɪs/, synthetic /sɪnˈθɛtɪk/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, algorithm /ˈælɡəɹɪðm̩/, thermal /ˈθɜːməl/
Die Laute „sh“ und „zh“: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, digitalization /ˌdɪdʒɪtəlaɪˈzeɪʃən/, misconception /ˌmɪs.kənˈsɛp.ʃən/, transient /ˈtɹæn.ʃənt/, friction /ˈfɹɪkʃən/
Lange Wörter – auf die Betonung achten: consortium /kənˈsɔː.ti.əm/, necessity /nɪˈsɛsəti/, accelerometer /ækˌsɛl.əˈɹɑ.mə.tɚ/, digitalization /ˌdɪdʒɪtəlaɪˈzeɪʃən/, executable /ˈɛksəkjutəbl/

Laute, die Deutschsprachigen schwerfallen:

Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: colleague /ˈkɒliːɡ/, expertise /ˌɛkspɜːˈtiːz/, delve /dɛlv/, digitalize /ˈdi-jə-tə-ˌlīz/, supervise /ˈsuː.pə.vaɪz/
/æ/ — offener als „ä“: validate /ˈvæl.ɪ.deɪt/, accelerometer /ækˌsɛl.əˈɹɑ.mə.tɚ/, transient /ˈtɹæn.ʃənt/, anticipate /ænˈtɪs.ɪ.peɪt/, palette /ˈpælɪt/
/dʒ/ — stimmhaft, nicht „tsch“: digitalization /ˌdɪdʒɪtəlaɪˈzeɪʃən/, gauge /ˈɡeɪd͡ʒ/

So übst du mit diesem Video

Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
Sprich Satz für Satz in normaler Geschwindigkeit nach und wiederhole jeden, bis dein Rhythmus zum Sprecher passt.
Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie simulation, sensor, colleague.

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →

Shadowing-Übung: Real-Time Digital Twins Using CFD, Sensors & AI - Haris Kokkinos | Podcast #165 - Englisch Sprechen Lernen mit Video

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