This episode of Designing the Future is brought to you by Siemens Capital Electra X.

Circuit development for electronic devices has never been easy. The schematic is key, and the path from ideation to an understandable, buildable circuit is essential for project success. Today, there are multiple software tools that free the circuit designer from the traditional constraints of conventional circuit layout, opening the door to faster iteration, and better, more reliable devices.  

Joining engineering.com’s Jim Anderton to discuss how high technology can free circuit designers to innovate faster and at a lower cost is Thomas Yip, Software Development Director, Integrated Electrical Systems, Siemens.

Learn more about the speed, convenience and efficiency of Capital Electra X. And, sign up for a 30-day free trial. 

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Episode transcript:

Jim Anderton
Hello, everyone, and welcome to Designing the Future. Circuit development for electrical and electronic devices has always focused on the schematic. Circuits are generally complex, but a well-developed block diagram in schematic makes current and signal flow accessible and understandable and facilitates further circuit development as well as service. Now as part of an intelligent engineering development program, the process of ideation to manufacturing should not only facilitate understanding of device operation, but act as a launching pad for design iteration and both educate and inform the engineering design team. Now today there are sophisticated tools that take the grunt work out of circuit ladder and design and can help bridge the gap between concept and production release. Joining me to explain how design engineers and circuit designers can leverage these technologies is Thomas Yip, he’s Software Development Director, Integrated Electrical Systems at Siemens.. Tom, welcome to the show.

Thomas Yip
Thank you Jim. Thank you for having me here.

James Anderton
Tom, this is a fascinating subject. Now, when I began this industry over 30 years ago, layout, circuit layout was done on the back of a beer coaster or a napkin or a scratch pad and it was transferred to a paper schematic, which was then formalized with decals, with Letraset, and we would literally use a metal stylus and emboss these things in and it created a very rigid kind of a workflow or a system where making changes in the fly was quite difficult, quite expensive, and created a large sort of paper trail. How do these modern systems that we have today compare in terms of efficiency compared to the way that we used to do circuit design?

Thomas Yip
Well, this is what you’ll call that there’s a huge difference nowadays, mainly because the industry is driven by more and more complexity, more and more necessity for collaboration. Teams are spread all over the world, most of them remotely. Right. So there is a need to have these things done collaboratively with good workflow, version tracking and so forth. So that is a huge difference nowadays. The demand for such tools, especially in the last couple of years, has really accelerated.

James Anderton
Now we historically, there used to be a development process where we’d have an idea and then perhaps a block diagram and the block drive, we’d tend to compartmentalize or modularize the design and perhaps you might have a radio frequency expert on your staff or someone who’s the power supply man or woman, and of course you would then sort of sublet the design to those people, then integrate it later at this point. How is that design methodology? We live in a world now where your radio frequency expert might be a continent away or halfway around the world. How does that change the way that we design circuits these days?

Thomas Yip
Well, this is very common. In engineering, we tend to break down a big problem into smaller compartmentalized problems. Right. So with regular tools it is going to be a bit more difficult because it seems to be everyone has their own PC and their own software installed on their own PC. So in terms of collaboration and in terms of sharing it’s going to be a bit more difficult. But right now we have these cloud native tools, right, which essentially makes all these collaboration superbly easy. We also have real time collaborative diagramming. That means at the same time, Jim, you can work on your part of the circuit and I can work on my part of the circuit.

So these kind of tools are based on the cloud. They are built with the cloud in mind, enhanced with every layer of security in there to ensure that third parties don’t get access to the circuit that you and I are working on. So right now, especially so in Siemens, we are really pushing this sort of collaborative software and cloud-native software out there so that the users get to benefit from all this software without the need to set up their own IT team, their own servers and so forth.

James Anderton
Tom, there used to be an old expression that too many cooks spoil the broth and with collaboration, of course, we have the ability now to bring a very large number of people into the design process at this point, and historically with traditional systems, it’s been very difficult to corral and control the revision process to make sure that we didn’t go through the alphabet and have many, many dozens of different revisions before we lock the program down. It sounds like you’re talking about a way to sort of create some order from that chaos.

Thomas Yip
Yes, absolutely. Especially for us, we have the ability to say limit customers to view permissions, right, and comment the permissions. So certain people can comment but not work on the diagram. And even for people who can work on the diagram, there is a list of version history. We know exactly who has changes at what time, whether in the middle of the night or in the morning. Right. And we have the ability to be able to compare each different versions, compare yesterday’s version with today’s version, and if we didn’t like it, we have the ability to roll back and every single one of these things is tracked and can be rolled back in a huge jump or can be rolled back in a step-by-step. So these are automatically built into all this software.

James Anderton
Now I come from a manufacturing mass production world where designing consumer products in particular and consumer product design has some interesting complications. It could be as simple as, for example, a purchasing manager approaching the engineering department and saying, I need to change the source for a power semiconductor, which is functionally the same part but from a different vendor, which means it has to have a different part number. So a different part number has to be reflected in the bill of materials, in the documentation all the way backwards and forwards. So now we suddenly have a different schematic, we have a different bill of materials, we have a different part, essentially all of which has to still functionally operate in a production environment. How can systems like you’re talking about here help sort of smooth over that change? Because that used to create chaos, those sorts of changes.

Thomas Yip
Well, right now the issue at hand is that it used to be if you and I need to share these sort of drawings, I need to have these sort of drawings in multiple places, right, we used to do it by sending emails and then renaming it to V1 and V10 and V20 or this final version. Right. Because of the nature of the software that is built at the current moment, right, it sort of lives on the cloud and hence you can be on the plane and you can ask me to update something. The moment you land and we open your laptop, you can have the latest version. You can be assured that is the latest version.

So this save us a lot of time and a lot of problems in terms of disseminating all these things. We could have, the production shop floor have a view permission and I could be anywhere in the world edit that drawing. And the next morning if there’s a production run and when they open that drawing, it’s going to be updated, confirm. Right. So this sort of collaborative cloud software bring a lot of this sort of automatic syncing, right, that is extremely beneficial to the customers.

James Anderton
Yes. Tom, historically in my experience, there’s been a little bit of natural and actually beneficial tension between the production engineering staff of course, and the pure design element. It’d be quite common where the physical constraints imposed by the system, heat sinks, the physical size of components for example, I’ve seen circumstances where the substitution of a cheaper ceramic capacitor for an expensive tantalum capacitor meant a physical problem with printed circuit design, that kind of thing. It sounds like we’re talking about a way in which we can perhaps get the design engineers to work more closely with the manufacturing engineers.

Thomas Yip
Yes, absolutely. We have done a ton of studies and we find that lately not only that the project complexity has increased, a lot of companies are facing time to market problems. That means it used to be, for example, you may be able to design a new car in three years. Right. Right now that is not competitive anymore. You need a new car probably every two years or even every year.

So the amount of people that is needed to collaborate on all these things, including the production flaw and all these things is going to be increased by multiple fold, and hence there need to be a way for all these people to be able to edit and communicate. The reason a project is successful or failed relies heavily on whether the stakeholders is able to collaborate and communicate. Right. So we are extremely happy that the platform or the architecture, especially for our Capital Electra X, has all this collaboration, and syncing, and communication built in that will easily allow all these parties to be able to say, the shop floor make a command, okay, this capacitor is causing me a lot of problem, and that guy somewhere over a remote or in HQ can look at a command and can make changes and be rest assured that those changes are propagated to the shop floor again, really easily.

James Anderton
Tom, there is an increasing transaction in all manufacturing to push some of the design responsibilities out to the vendors. So we see sort of a decentralization of design responsibility and in some ways that makes obvious sense. If you have a semiconductor supplier for example, they will have expertise in their product, and so we’re at a point now where it used to be we would ask for a spec sheet and we’d get the specifications, and then we would turn around and we would then keep the entire design in-house. Occasionally you might telephone perhaps an engineer at the vendor there who might give some advice. Now it’s going a bit beyond that. We’re actually asking them to come into the design to a certain extent and say, show us how to use your product or your component in here. Is that going to change do you think, the way we do it? Is that the kind of thing this software can be used for?

Thomas Yip
Yes, absolutely. The way we do it is that everything is based on teams. You can look at it as a team or as a project. We have folders. In fact, everything, folders and files that you can share with anybody you like. So for example, if you’re working on a project, you can say this project is to be shared on a couple of vendors and a couple of customers. Right. And you can bring the expertise in and for them to help you and look at your circuit design and vet the circuit design or even suggest components that can be used in that. So traditionally this is going to be really difficult because you’re really worried about security and you don’t really want your file to go out and be passed around without control.

But with such a system, all you need is a browser and a secure sign in. And as you know full well, cloud-based applications, right, there’s a huge focus on security. So the onus is on the manufacturer of the software to put in all the security layers to ensure that this guy who signed in is really who he is and not some third party. Right. So you can be rest assured that you’re sharing to the right people, people who you really want to have that customized permission.

James Anderton
Tom, I’m glad you brought up the issue of security because we also live in a world now of iter of absolute restrictions on some things which are not just military, but an increasingly broad category of dual use because they’re dual use products in particular. I have met, for example, on the radio frequency side, some RF experts working in say millimeter wave microwaves where they’re not permitted to even publish their thesis from university because they’ve been classified. So we live in a sort of world where you need to get your product out there and sell it and collaborate globally, but at the same time, you’ve got to assure that if a customer or a military or a government agency approaches you, you can demonstrate that you have the security in place. Is that demonstrable level of security, is that the kind of thing built into these packages now?

Thomas Yip
Absolutely. Siemens takes all these things extremely seriously. We have in place many, many protocols and we go through a lot of vetting including ISO standards to ensure all these security layers and all these software are compliant, right, on multiple levels in terms of data privacy, in terms of security, in terms of sharing, even internally within Siemens itself, we have in place processes and so forth to ensure that data stay in a particular geographical location. So Siemens takes all these security extremely seriously, export control and so forth.

James Anderton
The common concern especially many SMEs have about advanced software packages always is that their engineering resources, which are expert at designing circuits, need to design circuits and not become experts in operating software. The difficulty of taking complex software where it’s a useful tool, but it also absorbs time and expertise to learn how to use the tool. Is simplicity of use, how much is that a factor in the implementation of these packages?

Thomas Yip
It is extremely, extremely, extremely high. I was in Silicon Valley and I met the guy, Barry Katz who invented the Apple mouse from IDEO. Right. So the fact of the matter is that right now with the proliferation of hand phones and so forth, our attention span on our screen has increased so much. So the way I look at this problem is that, right, if we require the user to spend time going to classes or reading a tech manual book to learn how to software, then we would have failed. I’m very sure the many new applications that goes to your phone or goes to your laptop nowadays do not require that anymore. 

So unfortunately, in terms of computer aid design software because of legacy problems, that’s still a little bit of a steep curve to get what they call that up to par, but especially so for Capital Electra X. When we first developed the software, one of the main criteria is that we want people to be able to use it without needing to read a book or go through any classes, hence everything is very intuitive web standards, drag and drop kind of thing. So we have made this one of the most important point because we understand as electrical engineers ourselves, that we really don’t have time to learn software. Right. We are needed to design a circuit and we are needed at a production site to commission the machines and all these things. Electrical engineers are really busy people, hence the focus on ease of use part of the software.

James Anderton
Now, traditionally, of course, many of these packages originally were derived from mechanical engineering software. So I always find it quite ironic that someone whose perhaps designing a motherboard for a personal computer is using similar software to someone who’s designing a bridge. So as this software specializes and deviates from that, will the things that the young engineer learns using basic simple general purpose design software, will that knowledge translate to this new realm?

Thomas Yip
Well, slightly. What we try to do is to ensure a good user experience. Right. And if the user have experience with CAD software, then that stand them in good state. Right. But we look at it this way, right, we want to focus on something where we have domain expertise and Siemens has a ton of domain expertise, especially so in terms of electronics, in terms of electrical, and of course also on the mechanical CAD part. Siemens has a complete portfolio of all this software. On our side, we are so much focused on the electrical part of the solution, therefore we want to be able to help the engineer, right, do the stuff that is important to them. That is we want to have the software automatically do a lot of the nitty-gritty details, but allow them yet to focus on safety and great design.

Now, there is design also need to be cost-effective. Right. So you need to focus on innovative design, cost-effectiveness and safety, right, rather than the nitty-gritty details. As you mentioned earlier, you need to make sure all these buildup materials, the numbers, the components and all these things must be correct. Right. So the software tries to help you in all these things so you can focus on the design. That’s what we are trying to do.

James Anderton
Tom, in the last few decades, a new generation of designers emerged who were what we used to call cowboy designers. They’d use this dead bug prototyping technique, for example, and they would build things and blow them up, build things and then say, we’ll put a trap in here to take out that erroneous signal. We’ll clamp the voltage here, and they would just sort of throw components at designs and then iterate their way and just run the schematic on a continuous basis to sort of get the outcome they wanted.

This is the opposite, of course, the original way that the traditional formerly trained designers would do it, which is based in mathematics and physics where they would carefully think their way forward in a block wise fashion from the front end of the circuit to the back end of the circuit to try and get to the finished schematic in as few steps as possible. Now, the current sort of thinking is that if you can get there by iterating multiple times, but do it quickly, it’s cheaper and better. Does software like yours, is this the kind of thing that’s going to basically allow that quote, unquote cowboy or that fast thinking young designer to just basically try things faster?

Thomas Yip
Absolutely. At Siemens, we’re working really hard, especially so in the areas of AI and generative AI. And the fact that the software can allow you to modify all these circuits and all these diagrams relatively quickly. We don’t allow problems because there’s always the possibility of rolling back to your original design if something doesn’t work out. So hence, this sort of software plays a huge role in the user’s ability to be able to shorten their design time, right, to be able to iterate all these things really quickly and get their product to the market. 

James Anderton
What about the “what if” scenario? In most engineering meetings I’ve met, there are all sorts of speculative questions like “what if”. What if we reduce the size of that inductor? What if we switch to a switching power supply, what would the consequences be? This sounds like a way in which you can sort of experiment in a much cheaper way than sort of building and blowing things up.

Thomas Yip
Absolutely, absolutely. In Siemens, there is this, what we call that, huge effort collaboratively with many big players in the industry in terms of building digital twins. Right. You would absolutely want to do these things digitally without a lot of costs. Right. While at this current moment, some of the models that is in the CAD drawing are actually accurate in terms of physics. They are just not a model, but they conform to what they call that, physical performance. Right. Okay. So that you can change something and you can understand and can see almost immediately whether those things affect your performance.

James Anderton
It’s a fascinating subject. We could talk about this for hours. You mentioned AI, of course, and naturally it has to come up because it is such a popular subject right now. It’s evolving so rapidly. And we’ve talked about the ability to simulate our way to success virtually using digital twins rather than having to go through the old awkward process of prototyping and bench testing at this point. Is AI going to replace the conventional human circuit designer in the future? Can you see a future basically when software like yours, for example, becomes itself a universal designer essentially, and a non-expert simply asks for a new circuit and gets one?

Thomas Yip
I think that’s possible, but I think at the current moment, I don’t believe it will replace the human designer. Right. I think the human has a lot of ability to put things together. You can ask the software to generate one part of the circuit for you, absolutely, but in terms of understanding the bigger picture and so forth, sometimes it is extremely difficult to articulate. So we have customers who design small machines, but we also have customers who design an entire factory. So there’s a lot of moving parts everywhere from the front of the conveyor to the machines to the interaction and all these things. I think it is possible that AI will generate some circuits for you, but I think you still need a designer to sort of put them together and ensure they work well together.

James Anderton
A brilliant future. Thomas Yip, CEO Siemens Capital Electra X. Thanks for joining me on the program.

Thomas Yip
Thank you, Jim. Pleasure.

James Anderton
And thank you for watching. See you next time on Designing the Future.

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Learn more about the speed, convenience and efficiency of Capital Electra X. And, sign up for a 30-day free trial. 

The post New Tech Unlocks Circuit Design Potential appeared first on Engineering.com.

This episode of Designing the Future is brought to you by Altair.

Engineering is applied science, the practical application of fundamental laws in disciplines like physics and chemistry, and is rooted in the mother of all sciences, mathematics. 

The post AI-Powered Engineering, Beyond Simulation appeared first on Engineering.com.

This episode of Designing the Future is brought to you by Siemens Digital Indutries Software.

In engineering, moving a concept from idea to eventual hardware has always been a challenge. And when those engineering projects are large and complex, the need to establish a single source of truth to keep multiple engineers and development teams working together is essential. Configuration control of conventional CAD processes help, but today’s computer-aided engineering includes development tools that go far beyond rendering, such as computational flow dynamics and simulation. The ability to iterate quickly and virtually puts a premium on project management.

The solution is the digital twin, which promises to maintain order within a rapidly accelerating design iteration process. And the executable digital twin is the key to real-world, cost-effective application of the digital twin concept.

Joining engineering.com’s Jim Anderton to describe the executable digital twin are three experts from Siemens Digital Industries Software: Ian McGann, Director of Innovation for Smart Technologies, Doctor Leoluca Scurria, Product Manager, Executable Digital Twin and Doctor Durrell Rittenberg, Director, Simcenter Experience Product Management.

Learn more about the executable digital twin (or xDT), the next evolutionary phase of the digital twin.

The transcript below has been edited for clarity:

Jim Anderton: In engineering, moving a concept from idea to eventual hardware has always been a challenge. And when those engineering projects are large and complex, the need to establish a single source of truth to keep multiple engineers and development teams working together is essential. Configuration control of conventional CAD based processes help. But today’s computer rated engineering includes development tools that go far beyond rendering, such as computational fluid dynamics and simulation. Now the ability to iterate quickly and virtually puts a premium on project management.

The solution is the digital twin, which promises to maintain order within a rapidly accelerating design iteration process. And the executable digital twin is the key to the real world use of the digital twin concept. Joining me to describe the executable digital twin are three experts from Siemens Digital Industry Software. Ian McGann, director of Innovation for Smart Technologies, Dr. Leoluca Scurria, product manager for Executable Digital Twin, and Dr. Durrell Rittenberg, director SIM Center Experience Product Management.

Can we kick off just a sort of level set and establish something basic? What is a digital twin?

Ian McGann: Well, for us it’s a virtual representation of a physical asset.

Jim Anderton: It’s a very simple way of describing that, but it’s in a sense, isn’t everything a digital twin in a sense, the concept or the idea an engineer has for design is in a sense a digital twin and a rendering is in a way, a digitized twin. But we’re talking about something that’s a little bit different from just a CAD file, aren’t we?

Ian McGann: We are, yes. Like you mentioned, documentation can be a form of a digital twin as well. What we’re talking about is when you can capture the dynamics, the movements, the forces, the stresses. That’s for us in engineering, let’s say a more functional digital twin and capturing all of that, let’s say complexity of the structure. So we would say we have a complex digital twin or a comprehensive digital twin. So we’re capturing the fluid structure interactions, the mechanical, the electrical, everything in that digital twin model that represents and is linked and connected to the physical asset.

Jim Anderton: Leoluca, we’re talking about the executable digital twin. What is executable? What makes digital twin executable?

Leoluca Scurria: Yeah, so this links actually to the definition of digital twin because usually these digital twins are used for specific purposes. So when you have multiple representations of different, let’s say physics and behaviors of the assets and the way we make it executable, basically we package it and we package it in a way that can be used also for other purposes. And this is key because our customers put a lot of efforts and invest a lot in the creation of digital twins. And basically our goal is then to enable them to leverage these descriptions, these behavioral representations also outside of the usual, let’s say, environment. So you can imagine a digital twin that gets created during the design process then can be reused also on the machine to optimize the control or the maintenance, and basically can extend the value creation over the entire product life cycle.

Jim Anderton: With a rendering, a rendering is a digitized version of what used to be an ink on paper hard asset, something that was a physical touchpoint you could hold in your hand and you could refer to. So if there was a question, it would be, let’s check the print. Now it’s let’s check the rendering, let’s check the CAD file. Does digital twin have that same relationship to a physical asset? Is this something where you’d think on the shop floor, or if there’s a question of a manufacturing engineer talking to design engineer, you’d say, let’s check the digital twin?

Leoluca Scurria: Well, that’s one possibility, and that links to the one source of truth that we always worked over. So, what you have with rendering is really an image or like a visual representation we instead look at representing the physics of the system. So when we go into how is actually this asset meant to work, then we can look at the digital twin and say, Hey, this is how we see the physics working and interacting with external world.

Ian McGann: Well, like Leoluca says, it’s brilliant in that and in this same reference that you had related to the CAD file, like you’d look at the CAD file, but that’s always a point in time. Once the machine is made, things change and the CAD file is still representing the perfect scenario the way we want it to build it, but it doesn’t represent that moment in time. And it’s the same if you just looked at a physics model. You’d see that’s the way we intended it to work.

But what we’ve done with the digital twins is we’ve gone that step further, is we’ve made a connection between the physical asset, the machine, and that physics based model or complex model, let’s say, and the model updates into match the virtual in real time. So if you take a look at it, you’re on the shop floor, you say, okay, let’s take a look at the model. You’re going to see exactly the state of the machine, the physics behind the machine, at that point in time. But the cool thing is you can go back in time as well. You can say, well, what was it last week? Did it change?

Durrell Rittenberg: Well, one of the things also is to think about the types of challenges that an executable digital twin can really address. And Ian, one of my favorite examples is we’ve done a fair amount of work in the automotive industry, but in other industries as well. And we work with one of the larger, actually work with all of them, but this is one of the larger German auto manufacturers. And they came to us with one of the challenges they have, which is, Hey, you’re doing such a great job of simulation that you’ve actually reduced the need for physical prototypes, except we still need physical prototypes. We need to be able to test them. We need to be able to validate them. What can you do within Siemens and your digital strategies that might help us improve what we get from a physical model? And so we started by going back to the engineering groups and saying, okay, well what kind of digital twins do we have that we might be able to leverage?

And we actually chose vehicle dynamics, it’s not important. What is important is that working with that team, we were able to show them how a digital twin could actually help them on the physical side. And they went from setup times that were in the three day, four day timeframe down to about four hours, which was fantastic. But more importantly, within those four hours, once they got it out on the track, they were getting 10 times more information because they were using the executable digital twin as part of the process.

As a result, their models, they didn’t have to go through five days of testing, they could get it done in one day. Which means that the amount of time, calendar time that they have, wall clock time, they can use this physical prototype for other types of analysis, went way up. So it’s the kind of thing that extension of the engineering that’s done typically in design and extending it into the physical prototype. And ultimately those models with the executable digital twins can be deployed either in the AV testing mode, so they can put it inside of a model that runs for AV testing, or it can actually be deployed on the vehicle itself, which means that your car has an executable digital twin running in the background that helps the car improve tuning of the suspension system, the breaking system, the safety systems. All of that now is leveraged based on the work that was done by the engineering teams back in the design process.

And it’s that kind of connectivity that the executable digital twin is bringing to modern engineering context, that this connectivity, this representation is really changing how people think about what they do on the engineering side and how that engineering work can be transformed and used in different contexts. And I think that that kind of helps phrase exactly the types of problems we’re trying to get after. Yes, the technology is based on lots of different types of engineering and types of model reductions and these processes are real time, but it’s important to take that step back and think about how this is helping modern engineering corporations and companies around the world tackling difficult types of problems and do things in a very different way. So it’s really quite exciting.

Jim Anderton: Durrell, you brought up a couple of things which are worth unpacking that feel like a paradigm shift. Look, traditionally in product development process from engineering perspective, it doesn’t have to be automotive, it could be almost anywhere. There’s a design iterate, test, break, redesign, iterate. And there used to be a saying my old engineering boss used to say, there is no design, there is only redesign. And so the process is about how fast can we iterate our way to an acceptable solution and then we go.

And we iterate our way, historically in many cases by building prototypes and then real world testing them and then breaking them. Now we’re looking at a world in which not only can we virtually test things and break them, but Durrell is talking about a world in which we can deploy product into the field and then get real time feedback about the performance of that product and cycle that back into the redesign process. So are we blurring the lines between where design stops and production begins? Are we going to see a future where redesign is constant for everything from the shoes in our feet to the cars we drive?

Ian McGann: So that’s a good point. So we have to combine the shift left strategy that we see in our customers. So less prototypes. We need to achieve the same output in shorter time reducing the prototype cost. And so we have two ways of doing it. So one way is to basically accelerate the design process by combining the digital twin with the physical testing to get more information during the design phase. But also we need to connect the products that are out there in the field creating very useful and meaningful data connected back to the design phases. And this creates, let’s say an ecosystem, a digital thread throughout the entire life cycle that allows a quicker innovation, a quicker improvement of the product themselves. So this is the ultimate goal to really create an ecosystem where the information can flow across the enterprise and the product lifecycle.

One of the things I love about our customers is they always push us. They challenge us a little bit and the latest challenges, well yeah, I want to know what’s going on in the vehicle at this point in time, but I want to know, well, the example they have is a battery degradation. They want to be able to take the battery that’s in the vehicle and resell that at the end of its life, but they don’t want the battery obviously to be damaged when it gets resold. So there’s an optimal point where you say, “Okay, now the battery is the perfect point to resell it. Let’s take it out as a vehicle put another one in and then give that battery a second life.” So the analysis and the work that you have to do, you don’t want to put sensors all over the battery.

So we use digital twins to give us these virtual sensors to do battery degradation analysis in real time and then report back to either the tier one, if that’s the owner, let’s say of the battery if you’re leasing the vehicle or the OEM themselves or even the owner of the vehicle. So he could get that information and say, “Ah, you know what? Time to change my battery. I’m going to install it in my house as a backup generator and go get a new one.” But it’s that type of thinking that I love. That’s what our customers are bringing to it that we didn’t have before.

Jim Anderton: Durrell, that’s an interesting point is that we live in an IOT age where we’re talking about a future in which we have sensors embedded in everything and many sensors embedded in a product. The feedback I hear from individuals working in this sector, there’s a worry that we’re going to overwhelm engineers with data and that processing that data is going to be a factor. The simulation community turn around and say, “We’re not going to need 10,000 data points for a tennis shoe. We’re going to simulate the product up front basically, and we’re going to optimize it to the point where we can do three sensors and we can get the actionable information we need. Is simulation going to basically sort of wrestle that data overload problem to the ground, do you think?

Durrell Rittenberg: I think, there’s two pieces to that. One is obviously simulations are getting faster, more accurate, and you can get more information in that design phase and that actually can increase confidence for sure. But there’s another piece to that which is, there are strategies with machine learning and AI that we’re using that help kind of take that information and actually provide insight, not just data. I mean, the problem we have right now is it’s easy to create so much data that you really find yourself in the, my favorite is Delta Airlines. The CEO actually talked at a conference I was at which was an engineering conference. He said for every flight they’re bringing back about a, I think it was four terabytes of test data for every engine and every system in an aircraft.

And he said, “We have all this information and someone said, “Well that’s great. What do you do with it?” He said, “Well, we send it back to the engine manufacturers, let them make sure that they know we have this data.” But ultimately what they want to get to is how do you get information back to the people who have to maintain that aircraft that you know what? Engine number two is probably getting close to end of on wing life and needs to be refreshed and how do you predict that. And that’s where the executable digital twin, which can be powered by IOT, which can be powered by machine learning algorithms and other strategies can help predict when you might need to do that. So that idea of predictive maintenance which is kind of where the goal of a lot of the work that’s being done in engineering today is really around that because there’s such a business reason to do it.

It also comes back to that performance optimization. Ian brought up a great point, which is how do you make those batteries last longer? Well, you do that by understanding how they’re performing. And you do that by understanding the information that’s coming out of those using this an encapsulated, executable digital twin strategy within that battery pack, and it’s giving you more information, more insight about how the battery’s performing that you wouldn’t get otherwise. And it’s that insight that allows you to say, you know what? It is time to slap that on the wall and make it a Tesla battery or whatever, the one you put in your garage that can power the house.

So that’s the kind of thing we need to be really thinking about, it’s a shift in the way we think about how information could be used and the kinds of things that these executable digital twins can provide in terms of information and insight into a complex engineering problem. It takes it out of the engineering domain and takes it back to the guy who’s got his new Lucid or whatever the most recent, EVS and it’s telling them, “Hey, you know what? Look, the way you drive is impacting the battery, you might want to consider changing it or maybe it makes a shift inside the actual drive mechanism to save battery life.” Anyway, these are the kind of things we need to think about. It’s a lot more than just the technology. It’s what you get out of it.

Ian McGann: My little addition, so the machine learning aspect of this I think is quite interesting. We have one customer who is using the digital twins combined with physical assets. So they have a physical asset, they have a digital twin, physics based digital twin combined with it. And what they want to do is generate massive, massive amounts of data for the purpose of machine learning. They don’t have a history, that’s the problem with it. So in order to get that data they’re using the digital twin models with defects programmed into them that allows them to annotate or label the information far more accurately. So that when they’re creating that machine learning algorithms, they now have labeled information of thousands and thousands of data points that they wouldn’t have gotten until the systems were deployed. So that’s the other use case where you have the digital twins are complimenting the machine learning and actually generating more data for you. But it’s smart data, it’s insightful data.

Jim Anderton: Product design has always been constrained by manufacturability. You can design anything, but can you make it? I’d say we’ve got some technologies like additive manufacturing, which have removed those constraints to a great extent. If you can imagine a shape and test that shape virtually you can make it now. Is the executable digital twin, will that work with technologies like additive or process automation do you think, to alter the way engineers go about the design process? Are they more free now with this technology?

Leoluca Scurria: So when we look at innovative production systems like additive manufacturing, the knowledge that the customer have on the production processes is limited. And often when we look at manufacturing, most of the decision making are based on prior knowledge about the production process. With executable digital twin, basically, we can maximize and give smart data to our customer based on few prototypes of the innovative production system. And that basically allows to come up with a, let’s say, effective production process much quicker.

We actually had a talk with an aircraft manufacturer that was trying to optimize some manufacturing application for composites. They were saying like, okay, we have these new production processes that we want to speed up, but we don’t know what is the right starting point to optimize our parameters. So, that’s where we started engaging and talking about how we can really reduce that lead time from initial prototypes to, let’s say, actionable production processes through virtual sensors, performance optimizations and performance predictions. And that you can only do it if you can combine the few information that you have from initial prototypes with a digital twin to maximize not only the information, but really the insights about your production process. That’s where the real value of executing by digital twin is, it’s really to transform data or big data that you have in two insights and actionable information.

Jim Anderton: Durrell, we see in business software world, the movement towards software as a service rather than selling a CD-ROM with a package in it and then mailing updates. An individual in the electric motor industry was in conversation with recently mentioned that they felt that the future for that ubiquitous product, which is used in engineering manufacturing everywhere, was to perhaps go away from selling an electric motor to a customer, but actually selling power by the hour and a model used by the jet engine industry before. Because this ability to feed real realtime information back means that the electric motor manufacturer could schedule preventative maintenance or even swap the motor out without the customer even being aware of the performance of that motor.

So, in a perfect world that imagine that you’re leasing the motor and the motor manufacturer sends a technician who services it or even replaces it, perhaps without the customer even knowing that individual’s coming. So it sort of worry free, trouble free. If you extrapolate that world, you’re talking about a potential world in which everything from the clothes on our back to the shoes on our feet to the robots that build our products down there no longer exist as an owned asset on the factory floor. The executable digital twin, does that play into that future?

Durrell Rittenberg: It absolutely does. And actually I think the example that you just gave with the electric motor or even with the gas turbine or wind turbine, really doesn’t matter if you start to think about it as, as an organization, we basically get paid for how much power we generate with our wind turbine. And if we can start to figure out ways through the executable digital twin to improve outcomes by looking at not just one wind turbine for example, but maybe looking at all the wind turbines that we have in a wind farm. Within those wind farm wind turbines, we have executable digital twins running within the motor that gives us information about the mechanics of the motor and whether it needs to be serviced. We can also look at wind performance. We can start to bring together all this information and use that as a decision making platform to optimize how much power we’re generating. That’s going to help someone who’s in the business of selling that power maximize their return.

It also has an environmental impact that we don’t often think about that. But the idea that you can improve not only the efficiency, which has a direct impact to how green a particular type of energy might be, you start to think about how an executable digital twin strategy can be part of a sustainability effort within an organization. There’s another example which is kind of slightly tangential to the wind power, but it has to do with food manufacturing. We don’t think about food manufacturing the same way that we think about building a jet aircraft. But food manufacturers generate a tremendous amount of product and one of the main people working with right now, they make cheese puffs. We all like cheese puffs. They’re delicious. But you’ve probably been in a situation where you took a cheese puff out and you bit down on that guy and it was super hard, you almost broke your teeth and you’re wondering what the heck is going on? Well, it turns out that food science, the process by which they make those cheese puffs, that whole process is heavy engineering.

So if you can come up with this executable digital twin that can provide insight to the manufacturer that, hey, my extrusion mixture is off just a little bit, and it has that outcome where you get things that are just not sellable, that executable digital twin is going to save them a tremendous amount of energy because they can actually optimize their output, it creates a better product for everyone, and it does it at a more sustainable way in the context of how much power he uses. So when we think about these digital twins, we really need to be thinking about how executable digital twins and this overall digital thread from requirements through the design, through the actual manufacturing, and ultimately into the bag of cheese puffs, all of that, there’s digital twins across that entire workflow. The more that we can start to leverage that, the better outcomes in any one piece of that.

But this is the kind of thing we should be thinking about as an industry because we need to figure out how engineering is going to help us address some of the major challenges we have as a population. These are not just making novel devices. This is about really looking at how we can impact the world in a positive way.

Jim Anderton: Ian, Durrell has just touched on sustainability and there’s no way to have any talk about engineering at all in this day and age without talking about sustainability. And how many times have we talked to … I talked to manufacturers, engineering firms to say, “Great, I’d love to reduce my carbon footprint, but I make valves. I’m not in business to reduce my carbon footprint.” Can you talk about how the executable digital twin can help them square that circle, which is address sustainability issues, but not lose focus on the core business?

Ian McGann: If you look at where the valves, pumps, et cetera, will be used in the end, it’s going to be in … Well, we have one example. Customer is a water reservoir. And it’s covering a country. Right? So this is quite a …. 16,000 pumping stations at extremely complex set up. And the problem is that you’re pumping water from one location to another, and by the time you get the real data back, it’s too late. You’ve already pumped too much or you’ve already distributed too much water. Right? So that the information doesn’t come in from the real sensors in time.

So we create a digital twins of the entire setup, all the pumps, all the stations, everything. And we then start optimizing that. So it’s a information leads to insight, leads to optimization. And I think that’s the point that we’re getting to this, is that if your valves, your pumps, if they have these digital twins connected to them already, you’re selling that information as well as the value of the pump. Right?

And that’s connecting to a bigger system. And that bigger system is basically conserving energy by saying, “Well, you don’t need to pump so much because we’ve predicted that it will be full. So stop now before the real data gets there.” So it’s like an extremely advanced model predictive controller. But the model is of the entire system, and to the levels of complexity that you normally wouldn’t see in its standard MPC.

So yeah, we have examples of customers and they don’t ask us for a digital twin. That’s the nice thing, is they don’t say, “Oh, we need digital twin technology.” No. They come in and say, “We want to be more energy efficient. Can you help us? We have this target. We want to …” You know, “How can we get there?” And in pretty much all of those cases, the combination of the executable digital twin and the physical sensors that we have and can distribute throughout the systems is what’s allowing them to get there.

Leoluca Scurria: And another extremely important point is that, as Ian was mentioning, that we have customers which have system in place since 30 years. Right? And you don’t want to go there and say, “Look, I got an amazing solution for you, but you have to go back to your system and put, I don’t know, 1000 sensors of it so we can optimize it.” With executable digital twins, we can be what is called brown field compatible. Right? So we can transform the information that you have from your system into meaningful insights.

And that’s what drives in the end your, let’s say, the business value of it. Because we can really reuse what is already in place. And then you can imagine that there are also companies that then use it also to improve their business models. So they can say, “Okay, are you a power user? Then you need to have this type of capabilities on your machine, this type of performances.” And as it is software based, they can easily switch it on and off depending on the customer need, to optimize also the usage and their business model in the end.

Jim Anderton: As a question, sort of a wrap up question, can I just go around the horn quickly and ask about the executable digital twin as a starting place? How can an engineering organization now which uses conventional software-driven design processes make that transition? What is the first step that they need to do? Durrell.

Durrell Rittenberg: It’s a great question. Of course, it’s going to depend largely on the type of engineering they’re doing, but it’s kind of going back to what I talked about in the automotive sense. It’s really starting to look at where are those challenges that we want to address, and looking at the kind of information we have to start. And that’s really where we would come in and work with an organization is to try to understand, “Okay, what is your ultimate outcome? What are you looking to do? And let’s take a look at the assets you have in the context of the water system.” And using that as a start point, really trying to connect the dots.

Siemens, as an organization, as you know is somewhat broad in terms of the kind of things we do. We have motion control, we got the factory automation bit, we’ve got the software, the part that we fit into. But all of those pieces are part of this broader ecosystem of digitalization that organizations are going through. And it’s really taking a look at, “What are the outcomes?” And starting to look at the engineering information that’s available as a start point.

Because what we find is oftentimes there’s enough information to get started within an existing design process, that we can start there as long as we know where we’re going. And that’s kind of the key thing. And we can help with that as well.

Jim Anderton: Leoluca, your recommendation, how do they start?

Leoluca Scurria: Yes, indeed. So I want to link a bit to what Durrell was saying because what I always say when I talk to customers is like, “Okay, we have this amazing technology that really solve business problems. We are expert in digital twin, executable digital twin, but you customer are the expert in your application, and you only know your problems.” So we always start with having a conversation, and understanding how we can find a compelling problem that we can solve with executable digital twin.

And then from there we can move in different directions. So that depends on the stage of digitalization of the company. So you have some enterprises that already have a broader adoption of digital twins. So in that case is more shifting the paradigm to also use in service and for maintenance. And we have companies which are, let’s say, that use less digital twins. And in that case, we can also, let’s say, go on a journey together starting from digital twin creation and then leverage this digital twin also outside of the, let’s say, more economical usage.

Jim Anderton: Ian, what’s the first step?

Ian McGann: Well, the first step is creating your digital twin. Right? So that’s the first step. And if you go back five years ago, it was an extremely difficult process. And we worked really hard to make that one button click and it’s done. And it’s an executable digital twin that you can now deploy, manage across your machines. And I think that’s the point is people might have looked at the digital twin technology three years ago, four years ago, and they said, ‘Ah, it’s too difficult. It’s not working.” Or, “We’re not getting the connections between the physical assets and the virtual like we want.” I’d say, “Take a look at it again. And what you’ll find is that that creation process, the deployment process, that’s in place now, the management process is a lot, lot easier than it was five years ago.”

Jim Anderton: Durrell Rittenberg. Leoluca Scuria, Ian McGann, thanks for joining me on the show today. And thank you for watching. See you next time on Designing the Future.

The post Design Speed with Control: The Executable Digital Twin appeared first on Engineering.com.

This episode of Designing the Future is brought to you by Altair.

Engineering is more than applied science, it’s the application of creativity to solve real-world problems. Ideation is the cornerstone of design engineering, but a major difference between good engineering and great engineering is the ability to transfer ideas into renderings and workflows that generate products and processes that are true to the original concept. That concept itself is usually iterated, both in the mind of the designer, and in the development process itself. For most of history of engineering, the interface between ideation and rendering has been an informal process, buried in the mind of the designer. 

Today, that’s changing with new generation of engineering tools that simultaneously impose rigour on the design process, while freeing the designer to explore novel solutions, many of which would be impossible to execute with simple computer-aided design. It’s the digital twin which makes this possible. Jim Anderton discusses the implications of digital twin in real world applications with Keshav Sundaresh, Global Director of Product Management – Digital Twin and Digital Thread at Altair. 

Learn more on how digital twins help companies optimize product performance.

The transcript below has been edited for clarity:

Jim Anderton: Hello, everyone, and welcome to Designing the Future. Engineering is more than applied science. It’s the application of creativity to solve real world problems. Ideation is the cornerstone of design engineering, but a major difference between good engineering and great engineering is the ability to transfer ideas into renderings and workflows that generate products and processes that are true to the original concept. Now, that concept itself is usually iterated both in the mind of the designer and in the development process itself. 

For most of the history of engineering, the interface between ideation and rendering has been an informal process buried in the mind of the designers. Today, that’s changing with the new generation of engineering tools that simultaneously impose rigor on the design process while freeing the designer to explore novel solutions, many of which would be impossible to execute with simple computer-aided design. It’s the digital twin which makes this possible. 

The post Making the Digital Twin Work in Your Application appeared first on Engineering.com.

This video is sponsored by SIEMENS.

Feeding the population of the planet of 8 million and growing, is a fundamental challenge for the 21st century. The green revolution that began in the 1950s relied on massive chemical inputs, in fertilizers, pesticides and herbicides. Today, environmental concerns, plus a warming climate and limited agricultural land has been the impetus for new ideas in agriculture. Could we go below the surface and use advanced technology to allow food production anywhere, including cities?   

Montreal, Canada-based Greenforges has developed a novel vertical system that allows agricultural production almost anywhere, without the traditional constraints of weather, irrigation or land-use. It’s harder than it looks to grow food underground, and development uses advanced tools to iterate cost-effectively. Joining engineering.com’s Jim Anderton to describe the technology and how simulation was essential in its development is Jamil Madanat from Greenforges and Carl Poplawsky, Engineering Services Manager at Maya HTT.

Learn how Simcenter unleashes your creativity, granting you the freedom to innovate and allowing you to deliver the products and processes of tomorrow today.

The transcript below has been edited for clarity:

Jim Anderton: Hello everyone and welcome to Designing the Future. Feeding the population of the planet, 8 billion and growing, oh, it’s a fundamental challenge for the 21st century. The green revolution that began in the 1950s, well, it relied on massive chemical inputs in terms of fertilizers, pesticides and herbicides. Today, environmental concerns plus a warming climate and limited agricultural land has been the impetus for new ideas in agriculture. Could we go below the surface and use advanced technology to allow food production anywhere, including cities?

Well, Montreal, Canada based GreenForges has developed a novel vertical system that allows agricultural production almost anywhere without the traditional constraints of weather, irrigation or land use. Now it’s harder than it looks to grow food underground and development uses advanced tools to iterate cost-effectively. Joining me to describe how the technology works and how simulation was essential as development is Jamil Madanat from GreenForges and Carl Poplawsky, engineering services manager at Maya HTT.

Jamil is a bachelor’s degree in mechanical engineering from McGill University where he specialized in machine design and project management. He has five years of professional experience in the impact startups world with a focus on social entrepreneurship and sustainability. Jamil is currently the CTO of GreenForges, the first underground controlled agriculture environment farm early next year.
Carl holds a master of science degree from Purdue University in Mechanical Engineering, and before his appointment as engineering services manager at Maya HTT, he was a senior applications engineer. Previously, Carl was VP of engineering at the Engineering Sciences and Analysis Corporation, and was technical consultant with the Structural Dynamics Research Corporation. Carl and Jamil, welcome.

Jamil Madanat: Hi, Jim. Thank you.

Carl Poplawsky: Thank you. Glad to be here.

Jim Anderton: Jamil, can we start with you? This is an intriguing solution to a pressing problem, feeding 8 billion plus people and growing. The stresses on the environment, in the Western world, we’re paving over agricultural land. We’re looking at a change in climate. There are a lot of factors here that are putting constraints on agricultural production. How big is this problem? I mean, do we need to find these ad advanced technology solutions to feed ourselves?

Jamil Madanat: Absolutely. Actually, this is how the idea came to me. So our founder, Phil, was going through a report describing projected food shortages all around the world, and the report was looking into different methods and means that will alleviate this food crisis issue by looking into urban agriculture. The study looked into, okay, how can we leverage urban agriculture to provide more food in the cities, and was looking into rooftop farming, indoor farming, shipping containers. A couple of days after that, Phil was happened to be thinking about this problem looking outside a window and saw a water well.
That’s when it clicked in his mind, why can we use the underground for food production? So the conclusion of that report saw that even if we leverage urban agriculture, we would still cover 4 to 5% of that food shortage. But now with underground being an option, I think we have a lot of more space to utilize and a lot more means to alleviate the projected food crisis that’s approaching faster than what we’re ready for.

Jim Anderton: Jamil, can you give me a brief overview of the GreenForges’ system? You mentioned underground. Underground, sometimes we think of sort of an abandoned coal mine or a cavern or something, but these are rather more like missile silos, aren’t they sort of a cylindrical vertical shaft?

Jamil Madanat: Precisely. So we’re taking a more simpler approach here. So what we’re starting with is think water wells or just pile foundations, same ones you would may use for building foundations. Initially, we’re starting with a diameter size of 60 inches, so closer to 1.5 meters. So it’s nothing too big. But the advantage that you would really get is going underground and now we’re experimenting with a model going 15 meters underground. With that you’d be surprised how many plants you can fit in there and grow. The scalability would just really grow much faster as you arrange these forges in a grid system.
As you mentioned in the intro, it is a controlled environment agriculture system. With that, it means we get more control over the plants that we want to grow, expedite the harvest cycle, get more precise and refined flavors and control the crops that we want to use. So it’s not just only we’re leveraging the underground system, filling it with plants, but also expediting harvest cycles one, and two, just becoming weather independent. So that will give a lot of additional advantages with keeping the food production running all year long too.

Jim Anderton: Jamil, is this a hydroponic process? Are you growing plants in soil? How does it work?

Jamil Madanat: Yes, it is a hydroponic system. Basically for those unfamiliar, a hydroponic system would be just water mixed with nutrients and oxygen that the plants need and runs continuously just touching the roots, the back of the roots, giving the plants the nutrients that they need. It is a continuous loop system. So really we just keep continuing using this water that’s getting fed to the plants while monitoring at the surface kind of the nutrients being consumed, how much extra oxygen it needs, fill it, and then recycle the water.
So on one hand, this saves a lot of water, almost 90% of the water is, if not more, is getting recycled back within the system. The second advantage is pest management. So we see a lot of contamination that happens when using soil based systems, but with hydroponics, you’re really creating that barrier for preventing a pest and contamination.

Jim Anderton: What sort of crops do you anticipate will be used with this system? I see some green leafy vegetables. I believe it is in your background.

Jamil Madanat: Yeah. What you see here is a couple of what we call grow modules being harvested from underground, extracted and just organized in a radial manner here for harvest. Initially, we’re starting with leafy greens and herbs. Now, the good thing about leafy greens is that they require less input from nutrients to energy to light, and they have faster harvest cycles. So starting with those gives us the advantage of running harvest cycles faster. So we get to iterate faster. Plus, these plants just tolerate variations in the environment, in nutrients much better. So this way, any changes and tweaks we run to the system will still end up with a higher success rate of production.

Jim Anderton:
Now, it’s interesting. So you’ve found a way to take that farming underground, and of course we’re interested in the engineering aspects of this. By the sound of it, there are several. It sounds a bit like a closed loop system. So we’re talking about gas, we’re talking about water, we’re talking about heat. So there are energy flows and there are physical flows of things happening inside these chambers, and there’s also a structural component, in that you have a vertical shaft. Are these things made of ferrous, non-ferrous metals, reinforced concrete? What’s the basic structure made of?

Jamil Madanat: So the casing would be made of steel. We’re using special coating. The special coating has to take into account multiple factors. One, obviously being non-corrosive, nothing to contaminate the plants. Second has to be antimicrobial. So it just doesn’t promote any growth of algae. Third, we’re adding a white coat layer that incentivizes light reflection. So this way, in this tight space that you have underground, you’ll be able to capture most of the lights.

Jamil Madanat:
In this tight space that you have underground, you’ll be able to capture most of the light that’s feeding the plants. So the casing is still with coating on the outside and on the inside. As for the internal structure, it’s a pretty simple structure. I can’t get into too much detail about the current design that we’re working on, that’s still being developed and just has a patent protecting behind it. But I wouldn’t say anything too complicated and we’re always keeping food safety in mind and operational ease in mind when designing these facilities.

Jim Anderton: Well, it sounds like you have multiple systems that are sort of interrelated and overlapping at the same time. You have a mechanical engineering task, you also have sort of dynamic systems operating at the same time here. How complex is this from an engineering perspective? What sort of tools do you use to design these things? Are we talking about a conventional CAD system, FEA simulation? How do you go about this?

Jamil Madanat: Absolutely. So generally when we look at the design of the forge, we split it between structural, mechanical, electrical, and digital systems. Now, the biggest challenge that we found with designing the forges is that once you go underground, there’s very little literature on the climatization of these farms, which has to be done very finely tuned, very controlled environment. As a side note, you’d be surprised how plants sometimes can be very sensitive, certain variations, as we plan on expanding the crops. So we got to be very certain what the environment looks like underground. And to do that, we had to work with Maya HTT to simulate how the heat transfer happens at different soil levels, at different soil types, different humidities. So maybe that’s something Carl can tell us a little bit more is they provided cell work to us, helping us understand better how to climatize the environment underground.

Jim Anderton: Carl, tell us about it.

Carl Poplawsky: The simulation services group at Maya HTT focuses on what we call virtual prototyping. Virtual prototyping is a technique that we use to test the mechanical design long before it’s manufactured or before physical prototypes are produced. We use computer rate and engineering software, CAE software to do that. We standardize a Siemens software products in center 3D, and we look at, in this case, the thermal and flow situations going on within the vertical farm. Our contributions to this project focused on energy efficiency and water use. So we use the software to predict the cooling load within the silo or the vertical farm. That cooling load is a function of not only conduction to and from the surrounding earth, but also quite a lot of heat load caused by the lighting that is necessary to provide the solar heating for the plants to survive.

Jim Anderton: Carl, that sounds like you got several inputs here going on at the same time. It’s also got something which goes is vertical, that’s goes to quite a depth at this point. Is there a temperature gradient from the top to the bottom of this system?

Carl Poplawsky: Yes, absolutely. It starts with the earth itself in that the surface of the earth is basically an ambient temperature. As you go down the surface of the earth reaches the constant, relatively constant temperature within the depth that we’re talking about. And so you have a temperature gradient going down through the earth, and then when you’re pumping air down into the farm, you’re going to see heat transfer happening, picking up heat, and then you have to bring that hot air back up and through the heating, ventilation, air conditioning system. We call it the HVAC system. So we help to size the preliminary sizing for the HVAC system and also the piping and pump sizing in order to remove the condensation that collects at the bottom.

Jim Anderton: Carl, tell me about humidity. that’s something where anyone who’s a greenhouse operator, of course controlling humidity is a major factor here. This sounds much more challenging. This is rather a closed system with artificial light and the heat input coming from that, plus the aspect ratio of this operation seems to be quite high. It’s a tall, skinny cylinder. Is that a factor?

Carl Poplawsky: Well, first of all, the software handles humidity and condensation and it can predict the condensation that collects on the walls and also provides the relative humidity distribution throughout the system. And of course, that has to be controlled pretty tightly for the plant’s health. And that will certainly influence how the final HVAC design evolves.

Jim Anderton: When you’re designing an HVAC system, and Jamil, maybe I’ll throw us back to you the same way is that it’s much engineering development is iterative and in a lot of cases if you’re breaking out, breaking new ground and doing something which has not been done before in the way that you’re doing this, building prototypes, testing, breaking them, going back and redesigning is a very common way to design components in areas like automotive that I’m familiar with. You’ve got a very large and expensive and complex process here. You can’t dig a hundred holes and then iterate a hundred different designs and then go back and then figure out what works down there. How do you cut the corner on that? We know simulation is a great tool to do this, but even with simulation is that you’ve got multiple variables interacting at the same time here down there. Do you have simplifying assumptions you work with or do you just crunch the numbers in a brute force way? How do you attack it? How do you attack this problem?

Jamil Madanat: Absolutely. So, obviously following the engineering method, we go with subsystem testing. So you can’t test the whole system altogether. And as you said, drilling multiple holes on your ground, it’s an expensive process. Same thing is you can just keep flying rockets every time you want to test something there. So what we really try to do is take isolate systems and try to experiment, iterate, and prototype with them. So be it that the lighting system that we’re working with, digital systems and how they work with the lighting system, the climatization system, and how it works with the plants and then for things that we can’t really replicate.
So we do have multiple labs that are running multiple experiments in parallel, whether it’s the extraction system, whether it’s the integration of, call it the hydroponic system with the controllers and specifically for the HVAC and climatization we said, okay, we want to validate a couple of major assumptions, which is how much heat does this soil absorb during the day cycle? How much heat does it retain during the night cycle? And let’s run the simulations based on these assumptions. That would give us at least the groundwork for what we know is at least true. And then you build foundations from there. But yeah, always working from first principles and running subsystem tests, then you can validate and build on top of these building blocks is generally the easiest. Easy is an overstatement, but the best approach to get a more comprehensive design.

Jim Anderton: Plants are an interesting phenomenon. We know that early designers of space station systems had considerable difficulty with things like humidity control, temperature control, also in a closed system. In this case you’re looking at plants and the amount of biomass inside your system is considerable at this point. And of course transpiration is a factor here. So the plants are an active component of changing the environment that they work in. Is there a difference depending on the type of crop that you grow, are those lettuce leaves different from a different type of plant?

Jamil Madanat: Back to kind of controlling the climate of the plants, we have external factors and internal factors. On the external factor side, obviously, and I think I’d like to point out an important piece of the design we’re taking into consideration that I haven’t alluded to before, but when you go underground almost worldwide, below the seven meter mark, the temperature converges to the annual average regardless of what the surface variation is like. When you do the simulation, you want to take that upper piece of variation into the climatization model and then account for that kind of all the way consistent climate that you want to account for. Now, the interior internal variations that are taking place is one, depending on the crop, and second, depending on the growth stage of the crop.

Initially, the first about two weeks, we almost assume no humidity generation. The plants are just growing, evapotranspiration is very low, and then it just exponentially increases in majority of the crops. Now, different crop size also breathe differently and need different humidity levels, different temperatures and different light requirements. Taking all these into account, initially we’re working with all leafy greens that just have a very kind of narrow window of variation. Then as we validate one, we just build on top of the others. Hope that answers your question.

Jim Anderton: It does. Carl, if a mechanical engineer were to design, for example, a ground source heat pump, you can approximate that roughly into sort of a coiled two heat exchanger and think about what we think as the classic sort of convection conduction radiation equations of heat transfer, integrate them with your form factors, your shapes. But in this case, they’re so many other factors going on here that are complicating this issue. Is this something you could run on sort of when you think of a stock simulation software. Does this require a coded solution, a low code, no code solution?

Carl Poplawsky: No, it doesn’t require any additional coding. The software out of the box handles this problem. It is quite complex. Not only do you have the temperature gradients going down through the earth, but you have the humidity distributions and everything else going on. Well, actually in the initial simulations, we looked at just pumping air down in without the HVAC in order to calculate what sort of HVAC requirements we need. The software is quite sophisticated at this point can handle those kind of things. Again, what we’re really trying to do is shortcut the design process to some extent in that. As Jamil mentioned, and you mentioned you can’t go out and drill 50 holes. So we’re going to drill 50 holes in a virtual environment with software and look at the mechanical thermal float performance of design. Then when Jamil is ready to drill those holes, he’s going to drill only a couple because he’s going to have a much higher probability of his design being successful, thanks to the virtual prototyping,

Jim Anderton: Carl, many users simulation tend to think of it in turn as a validation tool as much as the development tool essentially. We know where we want to go, here are targets, we check our design, does it work or does it not work. However, we know the simulation can also feed useful information back in multiple ways into the design process. The aerospace industry, for example, sometimes they discover things they didn’t understand or didn’t realize about a design by actually sort of flying it virtually with simulation. Does that happen in this case too?

Carl Poplawsky: Absolutely. One of the major advantages of this technique is we can look at what I call coffin corner conditions. These are conditions that maybe you can’t physically prototype. Of course one example is in a spacecraft craft industry, when you’re flying something in outer space and we get involved in a lot of spacecraft applications, you can’t actually test all those things in a terrestrial environment.

Jim Anderton: It’s funny you mentioned Jamil, more than one engineers proposed that what you are doing may be the only way to actually feed colonies some places like Mars and the moon. Is there a possible connection here?

Jamil Madanat: Potentially. I mean, virtually you can drill these systems anywhere. The fact that they’re insulated from the environment that’s happening on the surface, regardless of weather conditions, so severe hot or cold climates. The fact also that you can just build this standalone system, lock it, seal it, let it run its harvest cycle and then move after. With little maintenance, most of the work can be done once you put in the plants and then once you want to harvest them, makes them potentially viable for multi planetary agricultural systems, so you never know.

Jim Anderton: Jamil, how did you go about approaching Maya HTT and Carl in this process? Did you have a problem and then say, “we have a complex problem here we need to solve,” or did you run up against a roadblock that seemed insurmountable? How do you get to that point where you say, “Wow, I need to consult with someone outside my industry just to fix this problem?”

Jamil Madanat: I’ll share with you kind of the thought process that we went through here, which is try to understand how the environment for the plants will look underground and ultimately you just want to make sure the crops are climatized based on the what we call the crop [inaudible 00:21:59]. Now, generally you design based on surface conditions. You say, okay, well the temperature outside is going to be as such, we want to climatize the environment up. Inside we want provide this much heating or cooling or dehumidification because outside the kind of temperature and climate variables are very well defined. Underground, while looking through different literature and even engineering formulas in front of me, I realize that the problem is just multidimensional or multifaceted. It’s not only I have to understand, well, okay, we have these LED columns running between 12 to 16 hours a day providing heat to the soil.

How much of this heat is the soil going to absorb? What kind of soil absorbs the most versus releases heat the most? Once you go underground is you have just a wider gradient of different soil conditions and different humidity. Based on the humidity, how much heat is going to be absorbed, how would it be retained? And then let’s scale this a little bit more. You have a grid of, let’s say three by three or 10 by 10 forges, how close should they be to each other so they won’t affect the heat between each other. That kind of dynamic or behavior of temperature distribution underground at different soil conditions is not something that you just can pull off at the back of the napkin. That’s definitely where the Maya HTT team came in very, very handy and useful at helping us understand it.

Carl Poplawsky: Our simulations found that the performance is heavily dependent on the soil conditions, the amount of moisture in the soil, whether it’s sand or a clay or something like that. When you run these simulations, you have to make some judgment calls about how large the earth domain is going to be because it has to extend out way past the vertical farm in order to get correct results. So for instance, if you look at image number one, what we’re showing here is the temperature distribution in a cylinder of the earth, in which the shaft or the farm is contained. And we’ve got some color bars here. Red is hot, blue is cold. So you can see it’s cooler at the bottom. You can see how the temperature contours flatten out. That’s giving us a good indication that this cylinder, this basically arbitrary cylinder of earth is large enough so that the simulation will be sufficient. And of course, this was also going to tell us how closely you can space these things. If you look at image number two, this is the closeup of the air domain within the farm itself. This is just the top. That cylinder over on the left is the air handler.
You can see the little squares in the middle are the LEDs. You can see how they’re producing heat. Again, red is hot and blue is cold. So these are examples of the kind of results we can get. These are of course temperatures. If you look at simulation, I’m sorry, image number three, you’ll see that this is showing the temperature contours going down through the depth and it’s very easy to see now how we do have some significant gradients there. Image number four shows the velocity profile. So we’re pumping air down in and then it has to come up. And of course we don’t want to tear the leaves off the plants, so we have to be concerned about what that velocity profile actually looks like.

Jim Anderton: Remarkable. Intuitively growing things is something that’s as old as humanity. So intuitively we have a simple process. You’re going to take a greenhouse, we’re going to stick it underground. Carl, you just showed us, is that this is more akin to the engineering environment in a space station than it is to farming in a sense. There’s a lot of complexity, a lot of things going on here. Generically for companies that have things that have a lot of things going on, like you have here, Jamil, Carl in this case, how much does an engineer have to know to approach you with a problem? Do they simply have to say, “These are the parameters I have to hit with this design. Am I going to get there?” Or do they have to turn around and say, “I need results on this, this, this and this to understand how they interrelate?” Just how deep do they have to go?

Carl Poplawsky: Yeah, great question. Really, it’s the virtual prototyping techniques that provide the information for how all these parameters interact with each other. So as a mechanical engineer, we think about what we call control volumes. And so we have boundary conditions on those control volumes. Here, the control volume would be that cylinder of the earth, and Jamil is providing us certain boundary conditions that are going to influence the simulation. For instance, the heat dissipation of the LED lights, the transpiration rate of the vapor coming off the plants. So those are basic boundary conditions. And then we take it from there and providing the information on how all these parameters interact with each other. In particular, we can change the performance of the HVAC system and change the locations of air ducting, inlets, outlets, things like that, and look at the total performance of the system.

Jim Anderton: Jamil, did you have any idea when you joined this project that it would be as complex as it is?

Jamil Madanat: Not initially. Not initially, because you look into an idea and you’re like, “Okay, I think we can make it work.” But the more you dig in, the more you realize there’s just way more to it. It really is so multidimensional, as I mentioned, on the mechanical, the structural, the lighting, the just horticultural side. So ultimately we have the plans at the core of our design and we want to make them happy. Also, on the other side, we have our operators on the farm and you want to make it accessible for them to extract and clean and work with it. And the unit economics have to work out too, the carrier capital expenditure and operating expenditure. So try to balance all these parameters together is a challenge. But that’s engineering, right? It just keeps you going and it’s that excitement of discovering something new every time we approach a new problem. So yeah, so far really enjoying it.

Jim Anderton: It is exciting. And one last question, Carl. For design engineers that are working with complex systems and have problems that require the kind of professional help that you and Maya HTT can offer, what’s the number one piece of advice you could give them to prepare themselves before they approach you with a problem? What homework should they do, before they present you with an issue?

Carl Poplawsky: Yeah, I don’t think it’s really not that unusual. You have to think about what your goals are going to be. If somebody comes and says, “I need a thermal simulation.” I don’t have a lot of information there. If he tells me that I don’t want the material to exceed certain temperatures and I need to minimize my energy consumption, now we have something to work with. So it’s really just like anything else in life. You have to decide what your goals are going to be and then we help you meet those goals.

Jim Anderton: An exciting project. Jamil Madanat, GreenForges. Carl Poplawsky, Maya HTT. Thanks for joining me on the show.

Learn how Simcenter unleashes your creativity, granting you the freedom to innovate and allowing you to deliver the products and processes of tomorrow today.

The post Can Underground Agriculture Feed the World? appeared first on Engineering.com.

Truly autonomous vehicles have been the dream of motorists and automotive engineers from the beginnings of the industry and have been predicted by futurists and writers for over 60 years. Few technologies have been so desired and anticipated, and few have been so difficult to realize. The major inhibiting factor to its development, low cost and portable computational power, has largely been solved, and advanced driver assist systems exist today. 

But as of yet, full autonomy remains elusive. The problem is incredibly complex, and the imperative to produce systems that deliver road safety at levels matching or exceeding human drivers puts a premium on system redundancy and error proofing. An increasing number of industry experts believe that only simulation can deliver the level of testing necessary to create truly robust systems in a world where the number of edge cases seem infinite. 

Joining Jim Anderton to discuss the implications of simulation in this space is Mike Dempsey, Managing Director of vehicle simulation developer Claytex, a TECHNIA company. 

Learn more about how simulation is driving the future of transportation.

This episode of Designing the Future is brought to you by TECHNIA.

Jim Anderton:
Hello everyone, and welcome to Designing The Future. 

Autonomous vehicles, self-driving cars and trucks, it’s been the dream of motorists and automotive engineers from the beginnings of the industry and it’s been predicted by futurists and writers for over 60 years. The major inhibiting factor to its development, low cost and portable computational power, well that’s largely been solved. And advanced driver assist systems, they exist today. But as of yet, full autonomy remains elusive. The problem is incredibly complex and the imperative to produce systems that deliver road safety at levels as good or better than human drivers puts a premium on system redundancy and error proofing. 

An increasing number of industry experts believe that only simulation can deliver the level of testing necessary to create truly robust systems in a world where the number of edge cases seems infinite. Joining me to discuss the implications of simulation in this space is Mike Dempsey, managing director of Claytex, a TECHNIA Company. 

Learn more about how simulation is driving the future of transportation.

The post Is Simulation the Way Forward to Fully Autonomous Driving? appeared first on Engineering.com.

The design of the technologies that we use every day, from cell phones to jet airliners, has changed significantly over the last decade, a change that is accelerating. The tools that help engineering professionals advance the state-of-the-art are changing too, and those tools are arriving just in time to address a new set of unforeseen challenges facing designers in the 2020s, notably climate change. How do these new tools intersect with the new technologies designed by those tools? Joining Jim Anderton to discuss the issues is Rolf Wiedmann, Managing Director at TECHNIA.

Learn more about about how simulation-driven design can help make sustainable products.

The transcript below has been edited for clarity.

Jim Anderton: Hello, everyone. And welcome to Designing the Future. You know the design of the technologies that we use every day from cell phones to jet airliners. Well, they’ve changed significantly over the last decade. A change that is accelerating. Now, the tools that help engineering professionals advance the state of the art, they’re changing too. And those tools are arriving just in time to address a new set of unforeseen challenges facing designers in the 2020s, notably climate change. So how do these new tools interact with new technologies designed by those tools. Joining me to discuss this important issue is Rolf Wiedmann, Managing Director of TECHNIA. Rolf, welcome to the show.

Rolf Wiedmann: Hi Jim, nice talking to you.

Jim Anderton: Rolf, let’s dive right into this, because there’s so much to talk about and these issues are so important. Let’s talk about sustainability. It’s an important subject through all the engineering disciplines these days. I mean, what do we mean by sustainability from an engineering perspective?

Rolf Wiedmann: Sustainability has matured. So as of today, as we are meeting many engineers in our daily work from OEMs to startups to larger tier ones, tier two suppliers, we’ve seen that sustainability is no longer perceived as a cost factor. It’s rather perceived as a success factor, actually. And so the attitude of engineering, the relevance has changed significantly over time. We are seeing that it’s no longer an issue of cost. It’s more an issue of setting up the right products for the future and the boundary conditions we see from governments and organizations. Also in the behavior of our clients from the end user is that sustainable products are accepted and at the end of the day are the most cost effective things that customers can produce. So we see there’s a significant change to adopting sustainability concepts in their daily work.

Jim Anderton: For many years, sustainability meant coping with waste, recycling waste disposable. That’s how we thought of sustainability. If we dig a large hole and we throw the rubbish in and cover it over, then then we are sustainable. In Germany, the automotive industry led the way with a cradle to grave approach to this problem. BMW famously, I understand, will dismantle the cars at the end of their life and recycle the components. Now this must put pressure on the design phase. I can imagine that designing a new automobile, for example, with 200 different kinds of polymer parts would be a nightmare to recycle at the end of its use. Do you see the pressures from the sustainability aspect starting off early in the design phase?

Rolf Wiedmann: Even more true than before. Just to give you an example, if you look at state of the art engines in electric vehicles, you see that, for example, the topic of rare earth is extremely important. So especially in times where we all know some political crisis can happen each day, the question is how am I independent of such situations. We’ve seen him with the chip crisis also. So really our customers and especially OEMs are striving to be in control of their product development. And so they also focus it very much that they can produce without being too reliable on third parties or supply chains.

Jim Anderton: That’s an interesting point. I know that, of course, there’s a CO2 footprint to a large supply chain as well. And in manufacturing, we moved from many years a just-in-time model of attempting to get component parts and assemblies delivered from many, many suppliers, all coinciding at the assembly point with exact perfect timing. And I think the COVID crisis has shown that’s a very fragile system. That’s easy to disrupt. Is there a sustainability issue there? Can you make an argument for purchasing more locally to reduce that carbon footprint?

Rolf Wiedmann: As I mentioned before, it’s about being in control of their supply chains. And sometimes also the logistics of producing are changing currently. So new concepts are being applied and certainly also this gives some requirements to the engineering community. Maybe they have to design more alternative products, more variants to be more flexible. Even starting the process before that, if it comes to the supply chain management later on, they have simply more options to work on that one. So it’s actually also has some impact on our engineering teams and the engineering community.

Jim Anderton: Rolf, when I was in the industry, design was really mostly about performance and cost. And that was it. It was essentially a matter of you’re tasked to design a component part or assembly that meets certain performance specifications and do so at the lowest cost. And things like sustainability were just not factored into that decision. How is that changing the mindset, do you think, of the design engineers now? Do they have to look at something and think, “Well, I would like to use nylon six for this part, or I’d like to use an aluminum alloy, but now I can’t because there are implications at the end life.”

Rolf Wiedmann: That’s true. But as we know, our engineers are extremely creative and they are defining, let’s say, the footprint of the product in a very early stage. So they determine it and they create solutions for that one. And it’s another challenge. Yes, it might be easier to stick to known concepts. But I think this is also what our engineers like to be challenged and to develop alternative concepts, take into account what, at the end of the day, our clients and our customer’s clients want to buy. And again, sustainable products are in the mind of our customers, our end users. And so the success of a product is certainly defined by its sustainability and how it can be presented in the market, in that sense.

Jim Anderton: We’ve talked about sustainability in reducing the impact of designs that we make now. But of course, reducing CO2 itself has become a large industry with new technologies emerging to things like capture carbon, sequester it. Europe leaves the way in many of these technologies down here. A growth industry, is that a place where you think where we’re going to see some innovative engineering growing in the future?

Rolf Wiedmann: So we have a constant dynamic currently. But it’s very interesting to see, I think, since I’m in industry, I’ve never seen a phase where so many new initiatives at existing traditional customers have emerged, driven by that momentum that we see here. And also so many new startups getting into business, taking care of these new topics. So there’s a big dynamic and great innovation. Especially, look at topics like battery systems that you manage. And here also, if we relate back to engineering tasks, we see that topics like systems engineering become much more in the mindset of our engineers. Because it’s not only creating shapes of products, it’s also predicting their behavior and optimizing their behavior. Also in with regard to their efficiency, like heating systems and so on, these new type of…

Rolf Wiedmann: If you take the example of electro-mobility, I mean, we are not only in the part of transportation, mobility, different industries, but especially in transportation. Mobility, it becomes topic about the heating systems, the climate control and so on. And that has also been the reason that we as TECHNIA, I just recently acquired a company called Claytex that especially engages in simulation and especially in the simulation of battery systems and so on. Where we see that our customers are facing us with the topic on a daily basis.

Jim Anderton: Well, I hope that we’ll chat in a moment about simulation in greater depth. But one thing I want to touch on and you brought up the systems engineering is at cloud connectivity. Now we’re coming out of a COVID-19 global pandemic first time in a century that this kind of thing has happened. But most engineering teams work collaboratively and they work with meetings and they’ve historically worked face to face. And that’s been important. I mean, historically global firms, like yourself, often would fly people from continent to continent to make sure that those meetings could happen to move projects forward. We have software that allows us to meet virtually, much in the way we’re doing it now. But engineering is also about sharing technical ideas, including renderings, models, algorithms, entirely different world at this point. Are we going to see a future from this point forward, where we decentralize everything in engineering, everyone designs and works from home, and we use technologies like we’re using right now to replace the face to face meeting.

Rolf Wiedmann: You’re completely right with that topic. But take in a bigger context, maybe. This is more or less a social change that is taking place that not only affects the engineering community, but virtually all workforces that we are having. But with the difference that for the engineering community, they sometimes have to do the hard stuff, to do the heavy lifting in terms of, as you mentioned, designs and so on. What we believe in, actually also in our company and many of our clients, is offices will merge into kind of a social hub. So there will be a new environment in the companies. A place to meet for business reason, but also to socialize. And it won’t be thus frequented as before. But it will be vital that the engineering teams can meet then be creative, exchange ideas together in a spot, in that hub.

Will they work the full week then in that hub? I don’t think so. And good news is, that as of today, tools have been available in the market. I mean, we are heavily pushing forward the 3D experience platform from Dassault Systèmes here to enable not only the collaboration, but really have the product described in a complete virtual way, combined with all the metadata. So it’s interesting. I had called some customers that we are adopting the platform. Hey, I can open up the design in the subway on the way to the customer or on my way home. And it has been extremely pushed these kind of solutions, especially by the change. Sometimes it needs such a dramatic change to create significant progress. And the adoption of these kind of tools has been extremely pushed forward what we are seeing here. And you also mentioned the topic and relation to cloud, and this all needs to be cloud-enabled or else it’s not feasible.

Rolf Wiedmann: And we are acting mainly also in Germany with a big chunk of our business. Traditionally Germany is quite conservative due to security and all this kind of stuff. But this has also changed in the minds of our customers. Because today is perceived to be more secure to be on the cloud, not to be exposed to cyber tech, in a sense. Easily to restore systems after a tech. And they believe that the big providers that are around like as Assure, AWS, and others. At the end of the day, they are more secure than the midsize, medium business that we have here locally. So it also has been a major shift in the adoption of cloud for technology reasons to provide that data in a distributor environment. But also it’s changed dramatically in terms of security. Has it been a negative point before? It is not perceived as a benefit for security topics to work on the cloud.

Jim Anderton: Well, I’m glad you brought up the issue of security because I’m hearing so much about it in our industry. It’s historically, of course, there have been trade secrets. There have been reasons to keep designs away from other people’s eyes. But there have also been issues with a customer. Specifically in areas such as aerospace and defense, where there may actually be legal requirements for keeping security of a specific design. I remember once visiting a division of a large aerospace manufacturer that we won’t mention that is based in Toulouse, France. And in that division, they actually were using specially made USB sticks and physically carrying designs from machine to machine to keep them away from the cloud out of security concerns. Yet by the same token, I see many firms today with current technology freely using internet cloud-based systems to move IT regulated designs. Designs that are quite secret in many cases down there. Is the confidence there? Do you feel now to stop worrying about hacking or attack from the cloud?

Rolf Wiedmann: We need to really distinguish between really mission critical things and the day-to-day work that our customers are doing. For sure there are standards, like tsocks and others, that regulate how and what data and what needs to be the secured infrastructure to work with. So that is a given that also will take some time. It’s not the case that this is completely free now at all can collaborate. But what we see is currently we have examples from large tier one suppliers that are setting up hosted environments, which are then secured by itself within the company. And then connect to gather around their teams in the world, to staff a big OEM project that are currently coming up. So there are many ways hosted-cloud, public-cloud. So certainly you have to find the right way to secure the needed security. But on the other hand, let’s say the frontier is really pushed forward currently. Also in the relationship between the OEM and the supplier, because simply the benefits are there.

Jim Anderton: Rolf, so much to talk about in connectivity. But I’d like to touch base about simulation. We’re hearing so much about it now in its new advanced forms. Of course, simulation has always been around. Engineering’s about simulation. We simulate in our mind. Perhaps historically, we would make small test articles or prototypes. We would test them in this respect. But we would iterate our way to success, start with an idea and then alter the design. Alter, alter, alter until we get something satisfactory. Now we’re looking at a world where we could almost take a crazy idea, even a loose concept, and then we can, using simulation software, actually simply test it rather than develop it in our mind first. It feels like we’re almost skipping a step where we can simply try 10,000 different ways to do something rather than simply attempt to narrow the focus from the beginning down there. Are we going to change the way that we approach this? Is this strictly about simulation to reduce cost? Or simulation going to get us to an ideal design faster? Both?

Rolf Wiedmann: It’s all about the concept of try and fail fast. And this is extremely supported by new ways of simulating. Not only design in a classical sense that you have this stiffness or the load on a part, it’s actually much more. We see it. For example, the current industry, you see, there are so many different types of cars brought to the market that customers simply don’t have the time to really do the home location with all of these cars. So they need ways of doing virtual testing that is per se, given for them. And virtual testing has also increased not only just to look in the rear mirror. Can you see the background in the right way? No, it’s much more. It’s the definition of complex systems.

And what I mentioned in the term of system engineering, system behavior, you’re virtually now the chance to simulate the performance of a complex system, including software. We all know how important software is now in the value chain of many companies. So a lot of the value of the product, also the maintainability is determined by the software they’re using. And there are some US firms that seem to have, especially in the car industry, some competitive edge. But as I heard, some of the German car makers are coming up.

Jim Anderton: Rolf, I think obliquely, you’re perhaps mentioning Volkswagen with their big electric vehicle push and that their growth has been remarkable and, in many quarters, unexpected. But you brought up complexity, I think. And I think that’s a key issue here for firms like Volkswagen as a case in point. When simulation began to really take off, we’ve noticed that designs became more complex. And we began to wonder our system’s more complex because simulation allows us to make things that are more complex. Or is it the other way around? In a sense, are the algorithms basically pushing designers to make things that are more complex because they now can optimize them at levels they couldn’t before. I think of additive manufacturing, for example, or you can make a simple bracket, which looks almost like an organic shape, very, very complex. Too complex to design in conventional ways. So do you think it comes from this advanced software or it’s a natural part of using this software?

Rolf Wiedmann: So I think just an old say form follows function. From the boho style before. So I don’t think actually that this drives complexity because there will be a negative aspect it’s much more. That also not only, as I said, the varieties are getting more, it’s also that we all know the lead times. The cycles are getting much closer. So per se, the complexity is increasing. So I don’t think that engineers just use complexity yet because they can manage it. I think still this perfect design, this perfect function is looked at. And it’s a major principle of successful engineering. So I believe in our community that they are really having these values in mind. But as we have shorter cycles and more variants, simulation helps keep track of it.

And as we are adding more components… I have an old timer from 1975. There’s not too much software, and I can tell you. But in the new cars there is. So it’s a given that also offering more functionality to products, we simply have more components. So we have to cope with software and all these kind of things. So I think these things are getting together. This is natural. Complexity is evolving. And so our systems need to keep track to help us manage it in a controlled and secure way.

Jim Anderton: It’s funny you mention that, Rolf. When I was a teenager, the Bosch Jetronic injection on a Super Beetle seemed so complex that I removed it and installed carburetors.

Rolf Wiedmann: Okay.

Jim Anderton: I drive a Honda right now and the technical manual for the Honda is 2,763 pages in length. So it has to be delivered as software. Because it’s simply impractical to print something. So the complexity is wild. But I come from the automotive industry. And I come from the automotive industry. And change is a natural part of all manufacturing. But configuration control is always an essential part. And you could see this really in older, more experienced engineers who are always very wary about constant modifications and changes to parts. And often they wanted to sort of suppress this as much as possible. Or tell a young up and coming designer and say, “Yes. You can change it five different ways to make it better. But we must hold and wait for next year or perhaps two years, hence, when we do an overall system redesign and we implement changes then.”

Jim Anderton: And one reason was, of course, the cost of tracking these changes, revising blueprints, for example, renderings. We used to say, “They go through the alphabet. So many changes, A B, C, D.” And so always there was a desire to crush this. Even if in their heart, the engineer knew I can make this better. If they’ll just let me. Are we at a point now where we can just release or free the engineer to go ahead and make those changes monthly, weekly, daily?

Rolf Wiedmann: You’re right. There’s a strong antagonism between what is possible and what is manufacturable on the other side. But this is a discussion we have having for many, many years now. Yes. True. But completely speaking, the new systems allow us to better keep track. I mentioned we are different industries as TECHNIA. One of our biggest industries is life sciences. And if you have an implant, for example, you need to keep track of it maybe longer as the projected lifetime of the patient is. And if you start to make a lot of changes in the product, and you’re not sure what is then delivered to the client at the end of the day, you are in big [inaudible 00:22:35] troubles. So especially in that industry, it is a strong momentum to use systems like engineering the platforms, the 3D piece platforms to really document where you are and have a concrete representation of the product and all its related data materials and so on.

So this is extremely important. And I think now that we mature on that one, customers trust to allow even more flexibility to come quicker, to better design and not restrict the young engineer with the prior idea to the next year’s release, where I can use that stuff. By the way, it’s the same thing with cars. That this automated update is something that is very much perceived by customers. Like with your phone, where you like to get a new update and new features in the product, not only all three years when you buy a new phone or on the fly. So this is also something that keeps you awake as a customer. So in order to track all this, it’s extremely important of systems in place. And we see heavy investments from the big OEMs that they’re using. Especially these type of platform systems to keep track. And on the other end allow flexibility.

Jim Anderton: Rolf, I’d like to ask you to project into the future. Artificial intelligence, AI, we have to mention it. It’s on everyone’s mind everywhere. In all aspects of software, not just an engineering software at this point. And it is an aid which helps engineering professionals do their jobs better. Will it replace engineering as we know it now? Is the age of the designer going to disappear? Will generative design and AI simply take the tools out of the hand of the craftsman and do the job itself?

Rolf Wiedmann: No, no, definitely not. The role of the engineer in the forefront of developing products, thinking of new products and initiating them. I think there’s a strong position in our industry for the role. Yes. Will there be additional tools that make your life easier? I mean, we’ve seen it in artificial intelligence in code making. So I mean, why is it important that I’m coding programs? Maybe, I just talk and say what function I want and it’s directly coded in a language of choice. So it’s a help. Like we have been introducing CAP Systems many years ago, still engineers are there. And so I strongly believe in that community. It’s growing, for sure. Everybody has to adopt his skills. One of our values at TECHNIA is keep learning. So I think we all need to keep learning and engage with new topics. And at the end of the day, be professional in what we are doing.

Jim Anderton: It’s an amazing future. Rolf Wiedmann, Managing Director at TECHNIA. Thanks for joining me on the show.

Rolf Wiedmann: Thank you for having me.

Jim Anderton: And thank you for joining us. See you next time on Designing the Future.

Learn more about about how simulation-driven design can help make sustainable products.

The post Advanced Designs Need Advanced Design Tools appeared first on Engineering.com.

This video was sponsored by SIEMENS.

For most of human history, the power to advance human civilization was derived from animals.  Human labor, draft animals and the use of wind in sails kept the pace of technological advancement slow, and the population grew slowly as well. 

But 250 years ago, pioneers like Thomas Newcomen and James Watt developed technologies that exploited hydrocarbons, notably coal, and discovered a way to harness the heat energy of combustion to create modern machines. The steam engine was the first of multiple technologies that converted heat into motion, delivering modern shipping, rail transportation, motor vehicles and finally aircraft. 

The growth potential of these technologies appeared unlimited, but in the last few decades, the environmental consequences of fossil fuel combustion have become clear: there is a price to be paid for unlimited use of carbon. But the demand for progress is relentless. Early engineers created the technologies that threaten the environment today, and today’s engineers are tasked with finding the solutions. The goal is sustainability, and the race is on to make maximum use of available technologies to transition carbon-based economies to cleaner alternates without serious disruption of modern life. 

To do this, advanced engineering tools, and new ways of thinking about power, work and energy are needed. Jim Anderton discusses these important issues with Chad Ghalamzan, Marketing Manager at Siemens Digital Industries Software and Stephen Ferguson, director of marketing content for Siemens PLM software.  

The post Advanced Tools, Advanced Sustainability appeared first on Engineering.com.

This video was sponsored by TECHNIA.

Engineers today face multiple challenges. Designs must be optimized for low-cost, light weight, long life and environmental sustainability. These requirements are frequently conflicting, but the search for the optimum design goes on regardless. New plastic resins, metal alloys and composite materials offer significant performance advantages over commodity materials, adding options for designers. Not only new materials, but new processes such as 3D printing offer design engineers new flexibility in making parts and products of extreme complexity—parts that could not be made with conventional processes at any cost. Simulation is the key to unlocking the potential of new materials and processes. TECHNIA director of simulation Johan Kölfors discusses the state-of-the-art with engineering.com’s Jim Anderton. 

The post Towards the Perfect Design: Simulation for Faster, Optimized Engineering appeared first on Engineering.com.

This video was sponsored by ANSYS.

Learn more about ANSYS Twin Builder and start a free trial at ANSYS.com.

The following transcript has been edited for clarity.

Michael Alba: Hey everybody, and welcome to Designing the Future. Today, we’re going to dig deeper into digital twins to discover some successful implementations and typical pathways to success. We’re joined by Manzoor Tiwana, lead product manager at simulation company, Ansys. Manzoor oversees Ansys Twin Builder, the company’s product for creating digital twins, and he’s previously held positions at Autodesk, MathWorks, and Bosch. He’s got an MBA from Carnegie Mellon, a master’s in automotive engineering from FHT Esslingen, and a bachelor’s in mechanical engineering from UET Lahore. Manzoor, thanks for coming on the show today.

Manzoor Tiwana: Thanks for having me.

Michael Alba: So, you’ve been in the industry long enough, I’m guessing to have seen maybe a few different interpretations of the digital twin. Could we just start today by going back to basics? Could you tell me how does Ansys define the digital twin today?

Manzoor Tiwana: Yeah. As you said, the term digital twin has been used in a lot of different contexts in the industry. And Ansys is part of the Digital Twin Consortium, and we have adopted the definition of Digital Twin Consortium. And they define digital twin as a virtual representation of real-world entities and processes, synchronized as a specific frequency and fidelity. What that means is that on one hand you have your physical asset and on the other hand you have your model or the visual replica or digital twin, and you want to connect them with some sensor. And with the help of this model, you can track the past, what happened to your asset. You can generate deeper insight into present, how is your asset is performing. And you can also predict and influence future behavior.

The thing that is very unique to Ansys is that it allows you to build these models from both physics or simulation-based model, what we call them, and we can also add data analytics to create those unique insight into your operations.

Michael Alba: So, what are some typical applications you’ve seen from your customers?

Manzoor Tiwana:  We have seen a large-scale adoption in several industries, from automotive to automation. But there are three major areas or applications that we have seen a lot of traction. Number one is this industrial flow network. This includes like the performance of your overall fluid networks, performance of your rotating machines, like pumps, compressors, turbines, process optimization of fluid mixing and blending processes.

The second area where we have seen a lot of use or a lot of traction is in the automation industry or in the elective drives. Some of the typical application includes the performance and thermal management of the drives. Also, in the EV and HEV space, in the automotive space, we have seen the thermal management of batteries, battery packs, and also the complete EV powertrain. So, these are all the application we have seen in this area.

And there’s this new or emerging technology or application what we are seeing in the industry, is also the heating and cooling application, like optimization of HVAC systems, zero carbon, and carbon capture. These are all the application what we are seeing in the industry.

Michael Alba: So, wide-ranging. Are there any particular exemplary success stories you’ve seen with any of your partners that would illustrate this concept in a little more detail?

Manzoor Tiwana: Sure. We have seen several customers from, as I said, we have seen a large-scale adoption from automation to automotive, and we cannot talk about all of them, as the customer relationship or they have not signed those disclosure agreements with us. But there are some customers that have done a press release with us, is ABB, EDF, A123, Volkswagen, to name a few.

One of the recent or the newer story is the ENGIE. They are the world’s leading supplier of energy efficiency services, and they are helping their customers to transition from traditional carbon-based energy to carbon-free energy. And they are employing digital twin to reach that goal, to improve or optimize the burning process to reach a zero-carbon target. In addition to that, we are also working with a number of partners in the industry, like Microsoft, Rockwell Automation, to deliver the solution to our customer.

Michael Alba: So just on the topic of those partners, you are one of the founding members of the Digital Twin Consortium, which I believe began early last year, could you update us on the status of the Digital Twin Consortium and Ansys’s role in it?

Manzoor Tiwana: Ansys is the founding member of the Digital Twin Consortium, along with those companies like Microsoft, Dell, and GE. And the goal is to drive the development and adoption of digital twin technologies, also to drive common terminologies and standard. One example is the development of digital twins definition language or DTDL. So, Ansys is collaborating with Microsoft to work on this DTDL. And we are also working with Microsoft to create the reusable reference architectures. So the goal is or the aim is to help these IoT solution to drive this standardization and help these IoT solution to talk to each other from multiple solutions, so you can combine them in a single solution.

Michael Alba: And how far along is this project? I mean, how mature is digital twin technology at this point? Are there still a lot of technological obstacles to overcome before we can really get to the vision that you’ve outlined?

Manzoor Tiwana: So digital twin technologies, I wouldn’t call them as emerging technologies anymore, so we have used this term that it is emerging, but I think it has established itself quite a little bit in this area. Having said that, several companies are still on the journey to explore and adopt the digital twins. One of the challenges that our companies are facing is both people that the organization need to evolve and adopt this technology, and also on the technical side, some of the typical challenges they face is how they want to put the sensors inside, how they want to collect the data, how they want to model these digital twins, from where they create those insights.

So, these are the very typical challenges that especially if you are basing your digital twin or model on the analytics only, it requires you to collect a large volume of process data for training and also the accuracy might be insufficient and limited just to the observed data and to the available sensors. On the other hand, Ansys provide physics-based models and virtual sensors that can integrate your physics-based model and the data analytics together to generate better understanding of your operations.

Michael Alba: Could you elaborate on that concept of virtual sensors? What does that mean?

Manzoor Tiwana: So virtual sensor is, in the physics-based modeling, you can predict quantities that are not measured directly from the sensors. To help understand you, I’ll use the example of an electric motor. For example, you have an electric motor and you want to, say, find out what is the temperature of your rotor is or what is the temperature inside the motor is, the one way to do is that to put a physical sensor inside, but that is not all feasible or it’s not very cost effective, or sometimes it’s physically not even possible to put a sensor inside. So, with the help of other sensors, for example, with the help of current, you can predict what is the temperature inside of your rotor is, so you know the properties of the material of your motor.

And with the help of some sensors, you can predict other sensors or other quantities like temperature in this case. So if you have increase in load, so that will increase the current in the motor, and with the help of these models, you can predict what’s the temperature is going to be in the future, what’s the temperature is currently. And also, if you keep on using this motor, how long you have till it will reach the critical temperature. So all these kind of things you can do with the physics or the virtual sensors.

Michael Alba: So, in your digital and physical twin, you’ve got a combination of physical and digital sensors, giving you a complete picture of the system. What’s the balance between those two types of sensors. Could you theoretically have all physical sensors and still make use of a digital twin?

Manzoor Tiwana: Of course, you can have both, all your data, it could come from your physical sensors, but as I said before, it’s not always possible to have those sensors there. And it’s usually not very cost effective way to put a lot of sensors inside. One example is that some of our customer, they have these brownfield applications, where they have these large applications where they have built 10 years, 20 years ago, and they want to now employ digital twins there. And to do this, they have to put new sensors inside. So the cost of putting these sensors inside is really great, it’s prohibitive. And with the help of some sensors, like, say, you can put some sensor as a flow sensor, and with the help of those sensor and using these digital twins, you can predict how your system is going to behave. So you can use a subset of sensors, but still get the full accuracy.

Michael Alba: So, you’re talking about essentially simplifying the complex system and this is an important point, I think you bring it up often when talking about Twin Builder is this concept of model order reduction. Can you tell us about that and how it’s achieved in Twin Builder?

Manzoor Tiwana: So a reduced order model or a ROM, how we call it, it’s a simplification of 3D physics. As you know, the Ansys is the leading authority in the simulation of 3D physics. So if you have a 3D physics model and you want to reuse that in a system simulation perspective, what you can do is that you can simplify that model using this reduced order modeling technology, without losing essential accuracy. Most of these 3D physics, it takes a lot of time to simulate, so which that is not very feasible if you want to generate these insights in the real time or near real time perspective. So you can use these 3D physics model, you can simplify them without losing those accuracy. And you can use them in real time or near real time for generating those insights.

Michael Alba: And is that a process of reduction something that I would do in Twin Builder or do I reduce my model and then bring that into Twin Builder? Could you take me a bit about how it works that way?

Manzoor Tiwana: Yes, sure. So the process of generating these reduced order models is in the way that what you have to do, you can use either Ansys physics, but it’s not limited to Ansys physics, you can also use any third party simulation software. And you generate inputs and outputs, and you generate that data, how your system is behaving. And then you bring that data inside Twin Builder, and then you can create those reduced order model inside Twin Builder. But once you have created those model inside Twin Builder, you can export them as a twin, what we call a twin file, and you can then deploy those reduced order models with other system simulation capabilities. And then you can deploy them on the cloud or on the edge or on any IoT platform.

Michael Alba: What is a typical journey for your customers in order to implement a digital twin, to get started from zero, what do they usually do?

Manzoor Tiwana: We recommend a crawl-walk-run approach to our customer. The very first step is to identify an application that is a low-hanging fruit. That means that it has some economic impact, so it could be in two ways, either the asset itself is very expensive, so any wear or damage, it can be very costly. For example, a wind turbine, right, so wind turbine is extremely costly. And any damage or wear happened to that can be really expensive. Or a turbine, a water turbine, they are some few examples where the asset could be very costly and any wear or damage to that asset could be very costly. Another aspect or economic impact could be that the asset might not be very expensive, but the downtime is very costly. For example, if you have a pump that is pumping oil from undersea, if that fails, the economic impact of the downtime that is very huge.

The asset might be few hundred thousand dollars, I don’t know, but the downtime could be millions of dollar. So first step is to identify an asset or process that has an economic impact, a large economic impact either by the downtime or in terms of its own cost. And the second step is to do a quick POC. That means to work with Ansys to create a quick proof of concept that takes four to six weeks. And after this proof of concept, present that to management and also gather the lesson learned. And in the last, we would recommend that after those lesson learned, you scale that to other applications, to the whole department or other department in the company.

Michael Alba: Now, when you talk about this low-hanging fruit that the company should start with and you look broadly around at all the possible applications of digital twins, is there anything you see as really ripe for digital twins that nobody seems to have really taken advantage of yet?

Manzoor Tiwana: As I said, we have seen a lot of application in all the industries. One application which is catching my attention or I have seen a lot of adoption in the industry is around heating and cooling application, like the refrigeration, HVAC application, zero carbon and carbon capture. Navantia is one of our customer that have adopted this simulation-based digital twin that help them monitor the performance and also manage the maintenance of their HVAC system on big ships. And with the help of this digital twin, they’re increasing their productivity, they are reducing the downtime, and it’s also helped them with the maintenance of their equipment.

Michael Alba: Now Ansys launched Twin Builder back in 2018, so it’s been live for a few years now, and I’m sure you’ve refined it over those years. Could you tell us how Twin Builder has evolved since it’s launch and what lessons you’ve learned along the way?

Manzoor Tiwana: Yeah, so Ansys has, as you said, we launched in 2018 and Ansys has established its position as leader in this space in the past three, four years. One key aspect of our go-to-market was to establish a key partnership with the IoT leaders in the industry, the people like Microsoft, PTC, SAP, Rockwell Automation, to build those relationship with the infrastructure provider in terms of cloud infrastructure or asset infrastructure and develop those connector, those out of the box implementation that user can take these digital twins and deploy on the IoT platform what they already have invested in.

We are also working to develop those design patterns and best practices. Another thing what we have evolved to adopt is the data analytics. So in the upcoming release, we are calling this the hybrid data analytics or hybrid analytics that allows you to combine physics with the data. That means that you can get the best of the both worlds. With the help of these simulation models, you can combine the test results and you can tune and calibrate your model to match the outputs to the real world outputs.

Michael Alba: Manzoor, I have one more question for you, and I’m going to ask you to put on your future looking hat for this one. But if you look into the future with this concept of digital twins in hand, how far do you think it’s going to go? For instance, if I buy a car 10 years from now, am I going to get a file as well that has a digital twin of that car that I can keep an eye on or a house or something like that? Will digital twins start to accompany most or all of the physical things that we make in the world, in your opinion?

Manzoor Tiwana: The immediate application or immediate, as I said, the low-hanging fruit, is on the side of, with the things which have greater economic impact. For example, the asset is expensive, downtime is expensive, so those are the low-hanging fruits or those are the things which are first to adopt these technologies. But as we are going to evolve, also, as you mentioned, the cars, as the cars going to evolve to become more EV centered, more getting the batteries inside, so you have to monitor those batteries, you have to monitor how is your asset is performing. So as we going to evolve, all these technologies is going to be infiltrate also into the secondary application, secondary assets. And you will see that you will have digital twins of almost everything of interest, so you can monitor how your asset is performing in the field.

Michael Alba: And just a quick add on to that. What about people? I know Ansys isn’t focusing on this, but some companies have explored this area. Do you think there’ll be a digital twin of you and I, at some point in the future?

Manzoor Tiwana: So actually, we do explore these kinds of things, where we have some of the medical applications of the heart model and things like that, where we are on the experimental side, we are working with some bio companies too, to explore this, so to model how your heart is pumping, to model how your arteries are functioning. So all these things could be very, very interesting, in the future, before a surgeon can do a surgery, so he can do a surgery on the digital twin side. So it’s still in the future, but I’m looking forward to that.

Michael Alba: Me too. I can’t wait to meet my digital twin someday. Manzoor, thanks so much for coming on the show. It was great speaking with you today.

Manzoor Tiwana: Yeah. Thanks very much for having me. It was really great talking to you.

Michael Alba: And thanks to you for tuning in. We’ll see you next time.

The post Finding Success With Digital Twins appeared first on Engineering.com.