I'm Frank Clayton, senior consulting engineer at Trimech Simulation Solutions. Yeah, I've been with the company now for eight years and started off as essentially a graduate engineer working on various projects. and then after a few years, sort of started working more with some of the more junior guys and supporting our engineering director, getting a bit more involved in some of the, the project creation sides and the, the statements of work and liaising with customers more, representing the company events, that sort of thing. I am, I am Kyle Grubb I'm a consulting engineer for Trimech Simulation Solutions. And so I've been working for them for maybe a decade now at this point. So part the furniture and I guess I started straight from university as well. This place is the only job I've ever known. Is that part of the family at this point is. I'm Laurence Holness and the head of software engineering at TSS and I've been here for seven or eight years now and my role is all about kind of developing solutions for the end customer, whatever that may be, whether it's bespoke software or the odd script here. And that for the consulting team. my name is Matt Dent, and I have been at TSS now for eight years. I came straight out of university as a graduate design engineer and eventually moved over into simulation and actually work and yeah, as a consulting engineer. Now I take on customer portfolios, managing, liaising with clients and leading small teams of engineers. think the most obvious driver of using simulation is to cut down on testing cost and tend to find that know traditional routes for design methodology was to generate something manufactured part, test it, and then you have to go back to the to the drawing board, as it were. Whereas using simulation you've got a faster, you know, approaches to modeling, modifying methods and, and running rerunning simulations. You can also run multiple at once, which helps really cut down, you know. Kind of you can explore multiple avenues very quickly, but the whole design cycle without having to, you know, physically go and make it with any kind of lead times on any physical parts you want making. I guess that's where that kind of the optimized engineering design and optimization led design approach comes into it as well, because you've got so many more avenues, like you say, lower. So you can go down this, you can you can investigate a much broader scope of potential solutions without the time and cost that would traditionally be associated with multiple prototypes or lead times manufacturing. Like you say, if you get that simulation aspect into a project much earlier into a lifecycle much earlier, then you're much more likely to arrive at an optimal solution. So, you know. I guess, yeah, I guess it's mainly a cost saving measure in a sense, isn't it? You're trying to represent the real world and then there's also an aspect of you can also do it in a validated sense as well. So some people, if they've already got a benchmark result in mind, if they make a small modification to that, you can just do it from a pure simulation sense and then it can be certified in that way without the need for them to have to go down the physical route as well. So this aspect of it as well. So yeah, we see that quite a lot in terms of, you know, you, you call it simulation and then there's a manufacturing defect or something happens in service that wasn't planned and you can just rerun the simulation to validate that it's still going to be fine and you're not going to develop any long term fatigue issues or anything like. I guess the other scenario as well is, is when testing, is it feasible that there are there are plenty of scenarios where you physically can't test something quickly or reliably or generate dangerous interest. Exactly. Yeah. some of the kind of the aerospace solutions and that sort of thing is not really feasible to crush something or send something up into into high altitude. So that's where the simulation comes into it. Content design ready. So as you are saying, some of our customers are allowing their designs to go through approval bodies without the need of testing. Nowadays there's so much confidence in in good fee models providing you can give validation for material models and things. Another reason, I guess thinking of vision assessment as an example is that we've found a way to use simulation tools as a repeatable approach where traditionally there wasn't wasn't any numeric method of solving a problem. So we had two is a generate a methodology in which that we we could repeat a process several times, giving us a repeatable answer that we can then use and we have used across multiple different projects now. Give you a good baseline comparator that you can use to compare different things and you wouldn't have otherwise. Yeah, I think those comparisons and sensitivity studies, kind of like you said, with the manufacturing defects and that sort of thing, being able to make small changes that either would require a large number of samples for testing or long lead times, but being able to do a quick sensitivity check on, okay, what happens if I do have a slight defect in this part? Can it still go out? Or what happens if I do have a manufacturing tolerance that I can't take into account or I can't improve if it's slightly bigger, slightly smaller, is that going to impact actually the performance of the product. the end of the day, simulations about achieving what the customer wants their part to do in a cost effective manner. Test correlation really, isn't it? Yeah. You just the most straightforward answer is to have it begin with test data from a a tested sample. I mean, correlation comes in many forms as well from small coupon material samples that we can generate material models from replicate in test data going all the way up to full crash analysis and blast analysis where we have something that's gone off for a test and we check that our models are validated against these results. And what we find is a strength for us. Our team is that we have that feedback loop with the customer. So even if we've done some kind of pioneering in an early design concept phase and we find it's going to test, we like to be fed back the results to see how we marry out, to find any differences between and over time. You build up this confidence in various aspects of your of your modeling and simulation. Yeah, that's the classic example of that is that nose cone crush that we've got on the we got the F a simulation and the real world test and you can just overlay them and see that they perform the same. Yeah. That was a was that former student. Yes. No composite. Yeah yeah yeah I think ultimately we with simulation like a lot of things it is the quality of the output is driven by the quality of the input. So if you don't have good data going in driving, driving your model, then you're not going to get a good answer. You might, you might get an answer, but I have confidence in the answer. You've got to have confidence in what data is going in. In the first place. And like Matt says, that can be material test dates or material data. It could be test data. It could be, yeah, it could be making sure you've got a good understanding of the boundary conditions of a problem. For example, we do a lot of simulating and simulating tests and no test rig or test structure is infinitely rigid, but it's quite easy to make something infinitely rigid in in a free world. So being aware of those limitations and where you're making assumptions and ultimately making sure that that the information you're putting in is appropriate and accurate and to guarantee that you going to get a good answer. Is one thing we've taken advantage of, actually, is we have a test lab, a small test lab in in our office in Leamington. And so what we can do if the customer is not testing something we've commonly going to build a little test rig should costing allow for it and we can validate ourselves small parts that we might be unsure about too. So that comes in quite handy. I think that's a good reason why a lot of customers will come back to us because we've got so much experience in that room. You know, there's 50 years of experience almost around the table here and and then people come to us because we know the nuances of the simulation world and how to make it applicable to the real world and how to tie them up. Yeah, I guess it all comes down to, again, the grass roots level really isn't that so? If you get the material data right based on your own experience evidence, then what formulations are correct? So what you've done before is bring in that knowledge from yourself, everyone around you in the team as well, and then put together and then compare it with test data. Then you're a pretty strong position. And as I should have in theory, I think has gone. And I guess a lot of that is is like you say, how drawn from the experience in the team where someone in the office knows that the answer is actually you just change this one to a to somewhere on a on a on a card and being in the office and being able to to talk to each other and make sure that everyone is aware of what everyone's doing and what the challenges are on various projects. It means that other people can can offer their input and insight from something they did seven years ago that might affect what we're doing today. And it's just that little bit of knowledge is locked away up in someone's head. But having that good communication within the team means that we can share easily and and disseminate that knowledge everywhere. I guess it's the aspect of quality as well with to really hone in on that in recent years in terms of our quality assurance processes too. So making sure they go for a robust checking process, for not always making sure they're vetted before they even get put in front of the customer is quite an important aspect to it now as well, trying to convey, particularly for new staff, other people join the company the difference between stuff like precision and accuracy. You can be consistently wrong and not realizing it's having the accuracy is key and sometimes it requires that knowledge within the team. Someone the second pair of eyes to actually see if it is indeed correct is what you put it in standard from experience. So I think that's something that we've really sort of developed, particularly in recent years as well, and what customers keep coming back to us. I mean, it's one of Oliver's favorite things, isn't it? Is quality assurance versus quality control. You want to make sure the quality is there at the start, not just catch it at the end. You want to make sure that the quality is there throughout, and that's your quality. Assurance is ensuring that you've got good quality in your model and in your data and in your results, not just the quality control which is catching. If there is an issue at the end you want, you want a combination of both, but if you get the first one right, you need a lot less of the second. I guess a lot of us are in the office. There's a, there's a lot of motorsport fans and we're all generally engineering fans, but we, we do get a lot of a lot of exciting motorsport projects through. Be that working with existing teams, working for example, the moment we're working on a on a on a ground up one off race car for a, for a very specific race series that will truly be the only one in existence. And we've been involved in that from the very start, which is quite exciting, quite, quite interesting. Obviously being approved by the FIA to simulate road cages and rollover protection systems. that's quite exciting because then when you're when you're watching motorsport on the telly, there's a good chance that, that one of the cars has got, got a rollover system in it that's been through our office and I think that that's generally pretty exciting. There's, there's, there's a few of us in the office that are very into our motorsport. So to be so tied to that is, is really quite, quite good fun. I know it's not quite everyone's cup of tea, but. I think in a way it's almost normal to it in a sense that we realized until we got the new new guys in and then we realize, hang on, how exciting it is to be working on something that the public won't see for another four or five years time, like a new JLR vehicle, for example. And you sort of become desensitized to after a while. You don't appreciate that somewhere I would be like completely make that day if they were working on that. So there's that aspect to it, I suppose. Yeah, And even outside motorsport, we're staying in automotive. Some of the the obsolete high end automotive products that we've been involved with are on the leading edge of technology and on what automated vehicles can deliver at the moment is is is really exciting. Being involved with Hypercars down to personal mobility systems, that sort of thing. And when you see something on the Top Gear website and you know that we were involved with it, that's, that's, that's pretty cool as well. So taking on that I suppose is a lot of the electrification is going on in the automotive industry too. We've been quite heavily involved in quite a few projects and seeing how people are pioneering different, different ways of approaching what we've known as traditional vehicle architecture for the longest time. Yeah, I think from my point of view of what the work I do is relating to composite materials and obviously we've got some big name customers in that space. I love the software development methods comes directly from the really complicated questions that they're asking us. And then we can then see those results being played out in the on the race track, as it were. And you can see the effect of the software that we've produced and developed specifically for them having a direct impact on their day to day activities. I guess with the software side, having Formula One teams come to us to buy software that you've written and you and Martin have developed over the years and are the new customers or or some of the recurring customers that every year is kind of their intention to come back and buy it again and again? It's pretty exciting. Yeah. Yeah. I get the stats, say something like 15 of the last 16 years we had the world championship team in Formula One using our software and and we work closely with them and that's always pretty cool. You know, when I called and you look at something and then go, no one's going to see this for a little while, but yeah, it's always exciting and. It's on the research and development stage and stuff as well. We've been a few projects. We as we were involved in a couple of Innovate UK projects as well for years and we as of this latitude we worked on as well. So yeah, that side of things can be, although it's a very long and drawn out process, it takes, I think the last one to worked on that is some project that took four years in the end because of COVID. But seeing the end of it come into fruition, how it comes back around for us will also be quite rewarding as well. So and the opportunity with those projects is is working in conjunction with so many other different companies that we might not necessarily be involved with. And some of the connections that you make and actually you get real understanding and insight into what other areas of the industry are doing that we might not otherwise otherwise see. And so that you walk away from them with some really kind of off the wall ideas that even apply to our own industry. And then, you know, six months down the line, someone to say something, you know, where we did something a bit like that in and Innovate project so we can take our methods from that and just tweak it. And, you know, now we've got this thing that you really wanted and you didn't think was possible, but we've turned it around for you in three months as a. Guess on a personal level as well. It tends to be an opportunity. Been here for ten years now. It's the ones that are a bit off the wall that excite me after a while of stuff. Then recently, anything from what? Being expert witnesses and court cases for aerospace manufacturers and things like that. You just spend like weeks trawling through thousands of disclosure documents, which can be quite tedious at times, but also really just eye opening. I see how how different it is from the day to day before. And so things like that as well. And that's one of the things I get involved with is I guess was a pattern at one point for like a, I'm trying to remember what it was now for us, the outcomes of direct capture and the voice. That was a bit of a weird one. So that was kind of sort of go putting aside all of the tools we normally rely on, putting that out the way we couldn't use and we have to rely firstly on first principles, those sort of hanging calculations. That's how we're going to prove it. We're not going to be using any of the tools we normally fall back onto so that things like that can be challenging or also quite exciting as well because you have to go by and sort of pull back to basics in a way that. It's not always the project itself. That's exciting, isn't it? Sometimes the that the product or the project can be particularly mundane or boring, but if there's a challenge behind what we've been asked to do, that means we're coupling different softwares and trying to, like you say, go back to first principles and really develop new methods that that can be just as exciting as a Formula One car or a hypercar or something like that, especially when someone else has already tried it. They didn't work, so they came to us. It's a slightly different route. The last few years I've been spending a lot of time doing blast protection systems in the defense industry, So simulating explosives, hitting hitting vehicles and what we've what's currently going on in the global climate, that's that's ramping up a little bit. And for me, that was quite exciting to get involved with, with pretty secretive high tech bits of kit and and seeing just, you know, quite how big this industry is. It's crazy for a team of what we ten or 15 people in our office getting involved, as Frank was saying, into global supply chains and seeing all these different levers. And here's our team having quite a significant impact on the end product. The end result was it's good. How many, how many weekly calls I got with everyone. Feeling happy about it. And I would. Say three or four. To two or three days a week on this phone call to that point, four weeks. Shaved or as I say. It's funny in that world because everybody knows each other and people move around. You might have a call one week with one set of people and they say, I've got this really novel idea and can we just do something like this? Is that possible? And then know, two or three weeks later, somebody from another team or I heard that somebody is doing this is not something that you could do and you have to be like, well, here's a box and a simple box and this is really simple example of this method that could be done. But we're not saying how it could be used in any way. It's a it's a real balancing act. Sometimes. Tend to finance what excites us the most. And if something's not. Yeah. Especially if a customer said, we've tried to do this, we can't do this. Or the ones that aren't. The ones that aren't challenging generally aren't exciting. Yeah. Quite Yeah, it's not very much fun and just putting some numbers into the box and putting go on the server. I think that I think that that partly speaks to the difference between us as an engineering service as opposed to an analysis service. And we typically try and avoid the role of just cranking out analysis, turning the handle data into one hand or data out. This is trying to provide a full engineering service so that we can say, Well, we looked at your design and we think this we got to do this analysis. Even though we said we might do this, we think this is better. Having run the analysis, we now think you should do this, this, that, etc.. And it's I think that's what keeps it interesting and challenging is actually trying to sort of go not necessarily above and beyond, but but deliver more to the customer. And the thing that I always bang on about in the office is data versus information. You can have a lot of data, but not always information. And the customer generally wants information, not data. So if you if you're just turning the handle on a project, getting results out, actually, what does it mean? What does it mean for the customer? Was it made for what they should do next? What's it mean for where they've been? What does it mean for what they're doing now? And I think that's that's generally why the challenging and the exciting, the kind of the saying. I So yeah, and I think, you know, we try and give the customer the engineering insight rather than some numbers that we've pulled off a survey of what we. Think the customer's in a way, while some of them seem to almost use this as like a brain trust in a way because they know that we're quite capable individuals and probably some of those over the years have a lot of experience within the team. It's just the depth and breadth of knowledge within the team is always worth testing itself and seeing can we solve the problems that they have? That's the great advantage of consultancy, isn't it, That you have an experienced team that you can call on and when you need them and you turn them off and there's no expense to your business when you don't need them? Because ultimately a lot of the time our customers are coming to us because something is challenging and the, the, the more straightforward run of the mill projects they might keep in-house or something like that, they might have the right member of staff staff that can do it. But they come to us because something's challenging, because they don't necessarily have the expertise. And going back to to what you said, I always and what Laurence said is we see a lot of the same people popping up at different companies say, so someone might be at an automotive company for one year and they come to us and we we do a lot of work with them. And then two years down the line we might be sitting there thinking, haven't heard from so-and-so for a while, and then they pop up a marine company or America's Cup Boat team or something like that. So because we are quite adept at dealing with some of those really tough, challenging engineering problems and we've got a good relationship with with all of our sort past and present customers, we do find that those challenging problems come up from the same people in different places. Quite often. There's a lot of talk about AI and its use in, in simulation, and there are people developing structural solvers and fluid solvers that are entirely based on on AI and sort of large data models and that sort of thing. But ultimately everything comes down to the laws of physics, the engineering first principles. So there might be many ways to to get to an answer. I think with everything, everything is generally going to go AI and to some degree in the future. And it's just managing that that quality assurance thing again and making sure that when you when you put a question into the big black box, then how do you know that what you're getting out is a good answer is definitely is definitely going to happen, is definitely going to it is definitely going to be a thing. And if it works and it's good, it's definitely going to improve computational time and reduce computational requirements and potentially improve delivery to customers, that sort of thing. But it's still very from our point of view, not being heavily involved with it. I suspect it's is still very immature and it's sort of life in its application and but it's going to happen at some point. I think it's going to kind of enhance that digital twin, which has been a bit of a buzz word for a few years now. And I think really what is probably going to offer and if game changing is that kind of long term product solution. So your A.I. might be able to be continuously updating the models that it was run on. And so you've got the validation cases that you've made when you built the model in the first place. 20 years down the line, you're not necessarily seeing the same conditions. And in the background the eye can be learning from the real world case data that's coming in and rerunning those simulations and just highlighting any potential areas of concerns before they crop up as a major issue or a major defect. You know, as Frank was saying, that we are still going to have quite a lot of human involvement in this process. And we see this when we use optimization tools nowadays. You know, the numerically driven that there's still a degree of a use a senior analyst looking at a result and saying, well, does this match the customer's requirements, i.e. from a manufacturing perspective? Or you might look at certain reinforcing material load fastenings and think, well, that's just not feasible in the real world. And so I can see that there being quite significant human involvement even when these tools start to get involved, you know, often think that when we look at similar projects in similar industries, you often have, I call them back pocket solutions. You think of seen as a problem. Now got something back online I can plug in there to fix that and I can see how jumping in on that early on and recognizing patterns quicker than we can. So there's a bit of evenflo's. And now with using these tools. Another tool in the arsenal really, I suppose I might be curious to see how it can be applied to the more routine or even mundane aspects of the job, which just like meshing. If you can train an AI to mesh properly, that would save time and a lot of cost of the customer as well. So it's the benefit for both parties ultimately. So it'll be interesting to see how that pans out with time. It's not like we haven't been using machine learning in some form or another for, you know, at least a decade in terms of design of experiments, exploration, that kind of thing. It's a new buzz word, obviously. I know, but the underlying technology behind it has been in place, certainly within our group for quite a long time. suppose VR is a is a is an interesting topic. I know it's becoming quite useful in larger design and card elements where people can get together and have a look at something together. And you know, I can see that being the same thing when you're working on especially bigger, more complex problems, especially if you're reviewing something with a customer because we've all been there with with especially larger projects that if you've got tight deadlines, then obviously, you know, you have to be thorough, rigorous through looking at analysis results. But if you had something that, you know, you can walk around, you can move around an object and bring other people into that discussion quite easily, that, you know, you might be able to visualize problems. It's a structure and things more easily. I can see that becoming, you know, more or more of a tool in our arsenal later on. Or easier to see your mismatches in VR than Yeah. Yeah. It would save us a good chunk of our time is on report and in post processing resource. That is to be able to just give a customer a file that they can actually just walk around and let's save them a lot of hassle. And trying to explain to us the, the consumer time behind them about different parts. Life on a video call it isn't it sort of cuts that out in the sense that it's more useful to them if they can just walk around the simulation and with our help service against another tool. And about