Being an Engineer

S5E19 Krishna Raichur | The Ins & Outs of Engineering Simulation (FEA, CFD)

May 10, 2024 Krishna Raichur Season 5 Episode 19
S5E19 Krishna Raichur | The Ins & Outs of Engineering Simulation (FEA, CFD)
Being an Engineer
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Being an Engineer
S5E19 Krishna Raichur | The Ins & Outs of Engineering Simulation (FEA, CFD)
May 10, 2024 Season 5 Episode 19
Krishna Raichur

Aaron Moncur interviews Krishna Raichur about his journey, Krishna shares his insights into simulation technologies, the evolution of engineering tools, and the impact of digital twins and AI/ML algorithms on the industry. 

Main Topics:

  • Krishna's background and journey to simulation engineering
  • Benefits and challenges of simulation implementation  
  • Case study on simulating blood flow in an artificial heart design
  • Static vs dynamic simulation, material properties and limitations
  • Costs of simulation software, ROI considerations and future developments

About the guest: Krishna Raichur is a Principal Engineer at SimuTech Group and an Ansys Certified Elite Channel Partner. With a robust career spanning over three decades, Krishna has made significant contributions to the field of engineering simulation, technical support, and techno-marketing. His expertise in ANSYS products and his passion for educating others make him a distinguished figure in the engineering community.

Links:
Krishna Raichur - LinkedIn
SimuTech Group Website



About Being An Engineer

The Being An Engineer podcast is a repository for industry knowledge and a tool through which engineers learn about and connect with relevant companies, technologies, people resources, and opportunities. We feature successful mechanical engineers and interview engineers who are passionate about their work and who made a great impact on the engineering community.

The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment such as cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us on the web at www.teampipeline.us

Show Notes Transcript Chapter Markers

Aaron Moncur interviews Krishna Raichur about his journey, Krishna shares his insights into simulation technologies, the evolution of engineering tools, and the impact of digital twins and AI/ML algorithms on the industry. 

Main Topics:

  • Krishna's background and journey to simulation engineering
  • Benefits and challenges of simulation implementation  
  • Case study on simulating blood flow in an artificial heart design
  • Static vs dynamic simulation, material properties and limitations
  • Costs of simulation software, ROI considerations and future developments

About the guest: Krishna Raichur is a Principal Engineer at SimuTech Group and an Ansys Certified Elite Channel Partner. With a robust career spanning over three decades, Krishna has made significant contributions to the field of engineering simulation, technical support, and techno-marketing. His expertise in ANSYS products and his passion for educating others make him a distinguished figure in the engineering community.

Links:
Krishna Raichur - LinkedIn
SimuTech Group Website



About Being An Engineer

The Being An Engineer podcast is a repository for industry knowledge and a tool through which engineers learn about and connect with relevant companies, technologies, people resources, and opportunities. We feature successful mechanical engineers and interview engineers who are passionate about their work and who made a great impact on the engineering community.

The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment such as cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us on the web at www.teampipeline.us

Aaron Moncur:

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Krishna Raichur:

Thank you for having me, Aaron.

Aaron Moncur:

What made you decide to become an engineer?

Krishna Raichur:

Good question. Well, you know, growing up in India, in the 70s, of course, I'm dating myself right here, you know, back then, you know, we were kind of groomed to become either a doctor or an engineer, or you're considered a bum kind of, you know, of course, that's, that's kind of tongue in cheek there. But sure, you know, what I mean, it was really doctor or engineer or what are the main things. And, you know, I was always kind of fiddling around with things we used to have what is known as what we used to call a Meccano set, think of it like a Lego set, where it's more links and bolts and nuts, and you could put them all together to make a car or a plane or, and things like that, and me and my cousins, we would play with the mechanic all the time. We're also also interested in dismantling, and then assembling things, you know, I had a bicycle. And for no reason at all, I would just dismantle the whole thing and start reassembling it just just for the heck of it. So I guess I was, you know, kind of an engineer to begin with in those in that way and eventually started said mechanical engineering is the way to go.

Aaron Moncur:

Yeah, it sounds like you were destined to become an engineer. There was no way around it. Right? It's interesting hearing about in India, where you are groomed. I know it was kind of tongue in cheek, but either to become a doctor or an engineer. I feel like here in the US, it's often a doctor or a lawyer, an engineer isn't discussed quite as much. Why do you think that? That was I mean, maybe a little bit of speculation on your part.

Krishna Raichur:

Yeah, it's gonna be speculation, but really, you know, back in the of course, things have changed now big time in India. I think Doctor engineer may actually be looked down upon almost, I don't know for sure. Because I haven't been in that society for a while now. But, you know, you know, back then really, law was, you know, considered not the very glamorous, shall we say, you know, and whereas, you know, doctor, always, there's always glamour as a doctor, you know, I don't know if glamour is the right word or prestige, maybe the right word or Yeah. You know, and engineers, same thing, you know, they were looked up to, you know, engineers and so on. And, and, you know, so that's how that's how it was. I don't know why that was the case. You know, surely in this country lawyers and doctors make a lot of money compared to engineers, but, but I think back then India, you know, doctors and engineers, were really the ones making the money.

Aaron Moncur:

Very interesting. Okay. Well, great. Thank you for sharing that. So you are an expert when it comes to simulation and simulation related technologies. Can you share a little bit about how that came to be, you know, what was it that influenced you to move your career in that direction?

Krishna Raichur:

It's interesting when I, you know, I was not really exposed to finite elements, until I came to the US to do my master's degree in mechanical engineering. I went to Villanova University. And one in the very first semester, there was a finite element course. And funny story there. The the, you know, here I am brand new to the country still trying to understand accents and the American accent and everything. And the professor was a great guy, great professor, for fun and element. He had this southern drawl. And for the life of me, I could not understand a word he was saying, when he wrote on the on the blackboard, I could not really follow this guy, had the southern drawl. And I was almost sure I would flunk out of this class, I was really scared. But to my surprise, I did very well in that class, you know, at the end of the semester, and then, and then this. Professor, he actually introduced me to introduce to us two answers at that time. And of course, this is way back in the mid 80s. And ANSYS was still at a very, you know, young age, if you will, all command based and things like that. He introduced us to answers. And I kind of picked up on it. And I got good enough with all the commands that he asked me to teach newer students, hey, wouldn't you get them up to speed with answers kind of thing. And in my final semester, you know, I decided to do what is called an independent study, which is a little bit less of a commitment than a thesis, write a full fledged thesis. If I had to do an independent study with this professor, he had a particular problem that he was looking to solve. He said, Well, if you're up to it, let's go. And so that's when I started using more of answers. And interestingly, you know, around the time, of course, I'm looking for jobs. And there was a, an announcement, or or in the bulletin board, I saw that there was a technical support position open at Swanson analysis systems, that what ANSYS was called before, it's before it was called ANSYS, Incorporated. Now, back then, it's called Swanson analysis systems. And I saw this, this ad, and I applied for it. And thankfully, I got a job. And the rest, as they say, is history itself.

Aaron Moncur:

Wonderful, wonderful. Well, what an exciting moment, that must have been as a young engineer. So you've you've kind of seen a lot in your career when it comes to simulation and how engineers work with and implement simulation in their their jobs. What are some of the challenges that you've seen engineering teams teams facing when when trying to implement these simulation technologies into workflows that maybe previously, you know, were more like hand calculation or maybe just engineers? gut instinct?

Krishna Raichur:

Correct? Actually, you know, the biggest challenge, I think, is resistance to change. You know, kind of the way we've always done it this way, type of thinking, right? And there was a time when the challenge was to make customers aware, I'm talking, you know, 90s, and maybe even the early 2000s. You know, the challenge was to make customers aware of the benefits of simulation. Okay, I want to say maybe 80s and 90s. They were so used to physical testing and like you said, hand calculations and so on, and they talk they couldn't trust results from a computer simulation. Of course, we will come a long way from then. Now most customers understand the importance and benefits of simulation. Now the challenge is to make them have simulation be more of an integral part of their design process rather than an afterthought type thing. And also moving simulation from the component level to the system level, you know, this component may behave very well, or show itself to behave very well on my, you know, in isolation, but when it's part of a system with with other devices, you know, working hand in hand with this component, how is it going to behave? So moving up to the system level is now more of the challenge? And answer says, really products that can help with that, and we are trying to help our customers do that. So really, the way to overcome really, I think it boils down to education, you know, showing case studies teach customers to do their own case study. And a lot of times it's, it's not just the technical part, we also need to educate the finance team and show them the ROI, if you will, of incorporating these technologies. Yeah,

Aaron Moncur:

so speaking of, of case studies, can you think of a project where the application of ANSYS tools directly contributed to, you know, a breakthrough or some significant improvement in the design or performance of a product? Oh,

Krishna Raichur:

yeah, there. There are many stories along those lines. But one that comes to mind is one of my colleagues, I was talking to him recently, he worked on a CFD simulation of an artificial heart. I mean, this is like a total artificial heart, not just one portion of the heart, or like just the left ventricle, or just one valve or something this the entire or total artificial heart, they call it, it works without any valves. And it stays suspended with forces from magnetic forces and hydrodynamic forces balancing each other out. And basically like our real heart, it has a left impeller and the right impeller in place of the left ventricle and the right ventricle. Now, when they tested this initial device in animals, they found blood clots on the right impeller after about a month. And there are no clocks on the left impeller. So that's where CFD simulations came in. And my colleague and his team got together, they figured out that the lower shear stress levels on the right impeller was corresponded to blood clot locations. So meaning if there's if the blood is just sitting there, not moving much, you know, tended to form plots, and they found these more more and more on the right impeller, none on the left impeller. So apparently, there's a sweet spot for these shear stress levels. Or if it's too low, you will get blood clots if it's too high, we will destroy the red blood cells and platelets. So it has to be somewhere in between. So to get that sweet spot, they used you know CFD simulations, computational fluid dynamics simulations, to try different configurations of the, you know, positioning of the impeller blades, the angle of those blades and so on, and came up with improved design. And now they have conducted some preliminary testing of this new pump design. The good news is that new pump has a more efficient, right impeller design, it can operate at lower rotational speeds, meaning it will reduce the likelihood of damage to the blood, red blood cells and so on. They've not yet conducted enough animal experiments to determine the impact on the blood clots and so on, but it does look promising. So I thought that is something fantastic, you know, with the heart disease and and all this and artificial hearts. It's something great.

Aaron Moncur:

That is phenomenal. What a great case study. I guess I didn't realize the level of I don't know what the right word to you is not quite fidelity, but that you could simulate tissue that accurately to the point where the software can determine or at least make indications of whether blood clots are going to form or not. That that is fascinating. Yes,

Krishna Raichur:

that's quite a challenge actually getting the right material properties for the tissue and so on. Thankfully, in this particular case, this was a man made artificial heart with with a mortar and all the stuff so They didn't have to worry so much about tissue. But still you're right. You know, right, once it's incorporated in the in the human body or in the animal body, you have to model the surrounding tissue as well.

Unknown:

Yeah. Okay. Let's use that and back up just a little bit. Can you explain not getting super technical here, but in as basic terms as possible? What what is simulation? You know, for those who just have not been exposed to it at all, use the term CFD, computational fluid dynamics, there's, there's FTA, what what are these things? Well, what is simulation mean? And how does it work?

Krishna Raichur:

Well, it to put it in very basic terms, I think of simulation as virtual testing. So, you know, let's say you have a design that you come up with, the traditional way would be to make prototypes of this design, take it into the lab and subject it to whatever loadings you expect it to see in the real world, and then seeing if it's going to withstand those loads, and that has always been there, and it's still going to be there. What simulation does is take that testing part on to the computer, so what we do is we create a mathematical model of that, whatever device it is, maybe it's a hammer, or it's a, it's a wrench, or it's an artificial heart, whatever it is, that you're designing, you will make a mathematical model of it. And you would apply those loads mathematically on the computer. And there is, there is techniques, numerical techniques that will allow the computer to calculate how this device responds to those nodes. And then you can look at it on your computer and say, Well, you know, in this particular location, looks like this thing is deflecting too much, or the stresses are too high, or the, or the blood is flowing too fast here in this location, or there's not enough blood flow here. Whatever, you can visualize all that on the computer. And since you're on the computer, you're free to make whatever changes you want without having to build new things. Go back to the lab, right? So you can try different designs on the computer, well, let me try repositioning this vein, on this impeller this way and see if how that affects the blood flow, whatever it is, right? So you can do those things much faster on the computer, and try different designs much faster. And then you can come to a final design. And you can say, well, this is looking really good. Now let me build a prototype of this one thing, not the 20 other things that I tried. I'll try this one thing, and I'll build it and I'll test it. And maybe instead of running 20 tests, you you just run one or two tests. That's really what simulation is all about.

Unknown:

And ones and zeros are a whole lot less expensive, generally speaking, then custom stainless steel parts, right? Absolutely. Absolutely. Yes. Okay, what, what are some common misconceptions about simulation that you encounter? And how do you go about addressing those?

Krishna Raichur:

The most, I guess the most common misconception is that simulation is for PhDs, you know, and that it's difficult to learn, I need to be a real engineering guru, you know, a PhD to take advantage of it. And maybe back in the day, in the winter, when the simulation or finite element techniques first came about, maybe that was the case, but not anymore. So it's not as difficult to learn because of improvements in technology. The other misconception people typically have is that it takes too long, you know, to not only to learn, but also just to implement simulation, and so on. And again, with the advances in computer technology and so on, that's no longer an issue. And today, the most common misconception we run into is, well, I want the simulation to replicate exactly what I'm seeing in the lab, the test results and you know, because in the lab there are so many possibilities, right? There's the environmental conditions and stuff like that, and all those things are very difficult to duplicate in a computer model. So, so it So trying to get engineers to understand that, hey, you know, you won't be exactly replicating your physical test, if you do want to do it, it will take quite a bit of a model to do it to, to, I guess, duplicate the exact conditions that you're testing it in. So why don't you just, you know, not worry about the test results so much, but make sure your model is representing your true physical conditions as much as possible. So I don't know if that answers the questions, but those are the typical things you run into. Okay,

Aaron Moncur:

great.

Unknown:

I don't know how easy it will be to answer this next question. Because the answer I'm sure is, it depends. But what kind of performance like processing power? Does one typically need on the computer? The, the system they're using for simulation? Can you get by with, you know, a nice laptop these days? Or? Or do you need to have like a server farm available to you turn to run all of that those calculations?

Krishna Raichur:

It's, it's a good question. It really can be anywhere in between anything from a laptop to a server farm, and anything in between. Because a lot of times, what happens is, you want to start your analysis with simple small models, just to prove out the fact that my model is behaving as it's supposed to. And so for that, you don't need a server farm your laptop, we'll do with some, you know, not your maybe not a home laptop, maybe, but a little bit more of a, you know, more of a power in your laptop, in terms of memory, and so on. But a laptop should do for a lot of the basic, simple analysis. But But today, what's happening with this trend today is to model not just a single component, but an entire assembly, an entire machine, for example, not just one component of a machine. So if you want to do that, now, of course, you need more horsepower to do that. And that's where we get into server farms and so on. And the other thing is, it also depends on the type of physics you're solving. If you're solving a computational fluid dynamics problem like that blood flow we were talking about earlier, you'll probably need more horsepower than if you're modeling, you know, maybe a wrench or a hammer or something like that. It may take lesser compute computational power. So it depends on the physics that you're solving as well.

Unknown:

Okay, fair enough. Yeah. Well, let me take a short break here and share with the listeners that our company pipeline design and engineering, we develop new and innovative manufacturing processes for complex products, and then implement them into manual fixtures or fully automated machines to dramatically reduced production costs and improved production yields for OEMs. Today, we have the privilege of speaking with Krishna Raichur. Krishna, can you explain the concept of digital twins and how they're used in engineering these days? Yeah,

Krishna Raichur:

you know, different people have different definitions of a digital twin. Some people say my final model is a digital twin. But you know, where the way answers defines a digital twin is, it's a combination of a physical device or asset, with a simulation model. So for example, think of a critical component, like an engine or a pump, what we do is we put some sensors on it that can measure information that's happening in real time, like maybe the temperature or humidity, or the movement, or whatever it is, you want to measure acceleration, whatever it is, you want to measure, you put some sensors on it. And then of course, you build a computer model, a finite element model of that same device on your computer. Right now, what the digital twin concept is, in the computer model is getting real time data from these sensors. And, you know, churning out results very, very quickly. These are models that are designed to run in seconds. Okay, we call them reduced order models, and they're designed to run in seconds that get the information from the sensors What the current temperature is, what the humidity level is, whatever it is information that your model needs, it gets that spits out the performance parameters of your device in real time, and then the engineer can decide based on the results, hey, you know, is this overheating, do I need to maybe stop this pump or this engine, you know, in a short while, so I can do some maintenance work on it, rather than waiting for things to fail, and then you get into expensive repairs and shutdowns and all this stuff. So it helps with that real time. You know, simulation based on real time data, okay.

Unknown:

And it also serves as kind of a secondary check, right? I mean, if you have a physical part and you test it, you get some results. And then you test that same digital twin within a simulation environment, and you get different results. Now all of a sudden, you have a pause, right? A Hold on, something's not right here, whether it was a mistake in the test and the physical part, or maybe the boundary conditions in the simulation were set up incorrectly, but it gives you kind of a, like a backup test, just to make sure, especially on mission critical parts that the test data you're seeing is in fact, accurate and reliable test data. Is that correct? That is

Krishna Raichur:

That is correct. Absolutely correct. So So what they do with these, you know, I mentioned reduced order models that spit out answers in seconds. What they do is they tweak these models to make sure that it is as close to the physical asset as possible.

Unknown:

Nice, okay. I certainly am no expert when it comes to simulation. But I've dabbled with very, very basic, you know, static simulations. And I know some people who that's what they do for their job is simulation work. And so I've had a few conversations about this. And what I've what I've gleaned is that simulations with specifically FDA I'm talking about, I have, I don't know anything really about CFD. But with when it comes to FDA, simulating metals can be easier. And the results can be more straightforward than simulating plastics. Is that generally the case?

Krishna Raichur:

Yes, and no, if I may say so. And what I mean by that is, from an actual underlying technology standpoint, metal, metal finite element models will be the same as plastic phenolic models, the difference, the tough part is because there are so many varieties of plastics, right, getting the material properties of a plastic characterized, is a little bit more of a challenge compared to metal, because metals have been around for a long time, and people have, you know, years and years of test data that they can come back to and rely on saying, Well, I'm using stainless steel 300, whatever it is, and I know these are the material properties. With plastics, it can be, it can be so varied, you know. And so the it's what is the challenge is really the characterizing the material properties of a plastic. If you have, if you can characterize the material, well, then, you know, you're finding our model will will predict the correct answers.

Unknown:

Okay. Okay. How about can you explain the difference, again, within the context of FTA between a static simulation and a dynamic simulation?

Krishna Raichur:

All right. So a good way to think about it is think of a diver standing at the end of a diving board just standing still. Okay, obviously, the weight of the diver is going to, you know, bend that, that diving board somewhat, right. And so that to calculate the deflection and the stresses in those in that diving board, with this person standing still at the end of the diving board, that will be a static analysis. Now, let's say this diver is jumping. Before he takes the dive, he or she takes the dive, let's say this diver is jumping up and down on this diving board. Now you can imagine this diving board kind of vibrating up and down right? And then the person hits the diving board and then leaps into the water. Well, if you now want to calculate the How this whether this diving board can withstand that type of a load this person jumping up and down, a static analysis is not going to be sufficient. You need a dynamic analysis, because there's the inertia of this person going up and down, you know, that's going to be taken into account, which a static analysis does not. I guess that's the best way. simplest way I can explain it.

Unknown:

Perfect explanation. Thank you. Oh, I had a question in mind, and I just lost it, was it? It'll come back to me? Well, what's what's one piece of technical advice that you often give that you wish more engineers would implement into their daily work?

Krishna Raichur:

Well, the most common one that I give and my colleagues give to our customers is to start with simple models and add complexity as you go. I can't tell you how many times we get support calls where the engineer has built this enormous model with, you know, a gazillion elements in it. And then it's not working. And it's with a large model like that, it'll take a long time to complete the solution. And then it may not even complete after running for a long time because of some issue with the way you model it. So it you know, we always tell people, well, can you make a simpler model, just to make sure it's working right? And then slowly add complexity, like that diving board example, right? Start with a static analysis, and then incorporate the dynamic loading of this person jumping up and down, don't directly go into that dynamic analysis, because it will be hard to you know, debug your model, if you will. Got

Unknown:

it. Okay, great. How about I mean, no tool is perfect for every single situation? What what are some of the limitations on simulation that engineers should be aware of? And cautious of?

Krishna Raichur:

That's a great question. Really, the limitations would be material characterization, meaning, if a basic requirement for your fronted elements simulation, for example, is, you know, how I characterize the material properly, for example, I have a steel part and rubber part, and maybe a plastic handle or something on this device that I'm modeling? Well, you know, have I modelled the, the material properly of this rubber? They're just like plastics, there are many, many different varieties of rubber. Have I characterize the rubber properly? Okay. So really, there will be one of the important things I would recommend people to look at is make sure that you have the right character and material characterization. And then the other. I don't know, I don't want to call it limitation. But the other important thing here to consider is, you know, we apply these loads mathematically on the model, do these loads? Are they representative of the real load that is being applied to this model? And going back to the diving board example of this person jumping up and down? How well can we characterize that up and down, jumping on this, on this diving board? If you can characterize that and you can, of course, characterize it quite well. And as long as you do that, well, you can rely on your results. So really, it is, it was not, does my model represent, you know, physical reality? Fully?

Aaron Moncur:

Yeah. Okay. You had mentioned before that one of the misconceptions about simulation is that it takes a long time to learn is really, really hard. And it's going to take forever to figure out how to use this tool. These days with the modern tools that we have and the training that's available, how long should an engineer expect to you know, to get to like a basic level of competency and not not like world famous guru or anything, but you know, good enough that he or she can run some simulations and be confident in the basic results. So

Krishna Raichur:

with a good engineering, understanding of engineering fundamentals, you know, I think that is important to understand the engineering fundamentals, what is stress, what is strain, you know, that kind of stuff, basic stuff, as long as you want Send the engineering fundamentals, picking up an engineering simulation, really, these days shouldn't take more than I want to, say, a day or two really getting familiar with the, with the software, you know, what buttons are where and, you know, we have what we call, you know, getting started quickly type of courses or Quickstart courses on our website, where, literally, you know, in a half a day, they would be running some basic models, you know, so, yeah, I would say about a half a day. Terrific.

Aaron Moncur:

Okay. You had also mentioned earlier about ROI, right? How is this actually going to save us money? Is there a story that that you can share or a case study experience? Where using simulation really reduced the cost of a development program? And if there isn't a specific story, then maybe you could speak to just generally like, how do you? How should engineers think about the ROI when considering simulation? Yeah,

Krishna Raichur:

see, the the biggest benefit of simulation is that you can run hundreds of test cases on the computer without ever, you know, building a single part. And so, right there, the ROI is huge, right? Instead of building 20 prototypes, each of which may cost, I don't know, hundreds of dollars, okay, you run those 20 prototypes on your computer, which costs, you know, other than the cost for the software itself, you're not spending any money building those parts, or those toolings and all that stuff. So right there is is a huge, huge savings. So, you know, there's really, it's almost a no brainer to consider simulation as to be an integral part of your design process.

Aaron Moncur:

Great. So regarding the cost of the software itself, not specific to CMU tech, as a channel partner, anything but I imagined the cost to get into simulation is roughly the equivalent no matter where you go to purchase that software, can you give the listeners a general sense of the costs involved with the investment to get involved with simulation?

Krishna Raichur:

So the, at the lower end of simulation, you can get basic simulations done for Ireland? You know, I want to say, three figure investment, right? You know, like, yeah, maybe under under $10,000, let's put it that way. Okay. And then, as you want to explore higher and higher levels of simulation, like that example, I gave you off. You know, the, the one where was the example? I'm just drawing a blank on but the heart the artificial heart? Yeah, the artificial heart example? Well, now you're looking at, you know, higher level of simulation, right, you have fluid flow happening, and all this stuff. So, so, so that one would, would take a lot more investment in the software, and so on. So it really can vary from three figures to six figures, okay. So depends on exactly what you want to do.

Aaron Moncur:

Okay. And like most things, of course, you could hire this out, have a group, maybe you're not, you're not using simulation regularly, but you have a project or even just a specific application within a project where you really do need it, you could hire it out. Or if you're doing a lot of simulation work, it might make sense to just purchase the software outright, and then your team can use it over and over and over. Is there like a break even point that teams should be aware of when they're thinking about just contracting that work out versus purchasing the software and training their staff internally? How should they think about that?

Krishna Raichur:

That's a great question. You know, it really depends on the philosophy of a particular customer, right? You know, how they think about it. Some customers are in the mindset of, well, if I'm going to do it, I'll do it myself. Even if it takes Need some time to learn it? And then on the other end of the spectrum, there will be customers who say, Well, I don't want to get into, you know, simulation business, we have experts who can do it and give us the answer. So I would rather do that. So, you know, it, it's both ends of the spectrum, really. And it, it really depends on what your intent is. And like you said, if you're, if it's like a one off, you know, simulation that you need to do to satisfy some government requirement or something like that, maybe it would make more sense to, you know, send it out as a consulting project for, for a company. But if it's something that is that you need to do on a regular basis, you know, maybe it's not a one off thing, maybe you need, you need to run these tests constantly on every design that you have. Well, in that case, it makes sense to bring the software in house and have a trained engineer or trained team of engineers to run the software.

Unknown:

And there are different levels and packages that you can get with simulation. What's what's the kind of like the ballpark range that companies should expect to invest to get, you know, a license of these, whether it's just FDA or CFD or different levels, is it like five to $20,000? Or does it vary, you know, even more than that, yeah.

Krishna Raichur:

So at the at the lower end, some for some basic simulations, you can get away with, you know, you know, five to $10,000, you know,$15,000 range. And then if you want to get into really, you know, high fidelity simulations, and you want to do what we call multiple physics, you know, you may, you may, you may be interested in not just stresses, and deflections, you may also be interested in fluid flow, and like that pump we were talking about, you may be interested in fluid flow, you may be interested in the, in the electromagnetic behavior of things, you know, maybe you're designing a satellite, and you want to see how well the satellite behaves, and so on. So, depending on the type of physics you're doing, you may, it could be, you know, multiple, six figures that you may need to spend. So it really is a huge range just

Aaron Moncur:

for the software.

Krishna Raichur:

Just for software. Yeah.

Aaron Moncur:

Interesting. I did not know, it could range that greatly. That's really interesting. Okay. Great. Well, let's see, I think just I have one more question. And then we can wrap things up here. What do you see changing in the future of simulation? Obviously, it's come a long, long ways, since the command prompt days back in the 80s. Looking out into the next 510 years, what what do you see as the the next evolutions of simulation,

Krishna Raichur:

I think, where we're heading is really artificial intelligence, and so called machine learning. Okay. Where the idea is to, to, to feed the years and years of experience that people have with their models into computers, and let the computers kind of come up with, you know, answers very, very quickly. Okay, so you have the potential to solve highly complex simulations, in a matter of seconds, just because of the history that you have. And, and, and so on. So, with artificial intelligence machine learning, I would think that's the, that's the next frontier and already, you know, we've, we have made some strides in that in that direction.

Aaron Moncur:

Very cool. That is super exciting. Anything we can do to make the work just a little bit easier, a little bit faster, a little bit more straightforward. Okay, well, Krishna, what, what a pleasure. It's been to speak with you today and hear all about simulation and the different tools available to engineers in that that category. Is there anything else that we haven't talked about that you think would be useful for engineers, engineering teams out there to know about simulation?

Krishna Raichur:

I think the main thing is, don't be intimidated by simulation. You know, just like today, CAD software computer aided design is is considered an integral part of almost any design, type work. We Think of simulation as equally, you know, in the same ballpark as computer aided design or CAD software and so on. You know, simulations should be in the same category treated in almost in the same category. Because it's so important, I think, to do simulations upfront. Terrific. Okay. Well, Krishna, how can people get in touch with you? Well, they can get in touch with us, you know, with our company info at Simutechgroup.com would be one way to get in touch with our company. And of course, I am on LinkedIn, Krishna Raichur, look it up in not very, very active on LinkedIn. But I'll be glad to interact with anybody who would like would like to on LinkedIn.

Aaron Moncur:

Terrific. And we will put the email address of SimuTech as well as the website URL and Krishna, your, your personal LinkedIn URL in the show notes here so everyone will have direct access to that. Okay. Well, Krishna, thank you again, so much for being with us today on the being an engineer podcast.

Krishna Raichur:

Thank you for having me.

Aaron Moncur:

I'm Aaron Moncur, founder of pipeline design, and engineering. If you liked what you heard today, please share the episode. To learn how your team can leverage our team's expertise developing turnkey equipment, custom fixtures and automated machines and with product design, visit us at Team pipeline.us. Thanks for listening

Introduction and overview of PDX engineering conference
Introduction of guest Krishna Raichur
What inspired Krishna to become an engineer
Challenges in implementing simulation technologies
Case study on simulating blood flow in an artificial heart design
Explanation of static vs dynamic simulation
Discussion of digital twins concept
Differences between FDA of metals vs plastics
Common misconceptions about simulation
Cost considerations for simulation software
Future developments with AI and machine learning