“There are several things to consider here and the first is collaboration,” says Julien Simon, senior product manager of NX Performance Predictor at Siemens Digital Industries Software. “People need to collaborate during the development phase of a product. I think the unique challenge is how can we continue to improve our work and design.”

Processes like simulation-led design, via tools like CAE in-CAD, may sound like they only benefit simulation experts and design engineers. But according to Julien Simon, its benefits ripple throughout an organization. He shared his thoughts on how simulation-led design and CAE in-CAD benefit design engineers, reporting managers and business leaders. He also explains how simulation experts fit into this new design paradigm.

How design engineers benefit from CAE in-CAD — and the role of experts

In the engineering profession there can still be siloes. For instance, the gap of knowledge between designers and simulation experts, or the gap between engineers from one discipline to another.

“To progress in your design workflow, you need to get feedback from each one,” says Julien Simon. “This takes time because you need to exchange information.” This information will often need to be tailored to the workflows of the individual receiving it. Transforming the data this way can be tedious, costly and time consuming. And the person receiving it can’t instantly return feedback. They will need to perform their own analysis, which again, takes time. Meanwhile, feedback is easier to implement into a design when it’s received as soon as possible — so the model is fresh in the designer’s mind.

Consider the workflows between the designer and a simulation expert. Traditionally, the designer would send a CAD model to a simulation expert who will then need to:

• Import the file into CAE software.
• Clean up the geometry to ensure that it is compatible with the study (such as making it airtight for a fluid simulation).
• Cut any small geometry that could overly delay results by increasing computations.

Only then could the simulation expert run their analysis which, like the above steps, could take days to complete.

“For an automotive company to produce a part, they may need five to six iterations between design and simulation,” notes Julien Simon. “The design can take two to three days to iterate, and the simulation can take close to a full work week to complete. So, if you make five iterations, that is about 30 days spent iterating on one part. With simulation-led design, you can go down to just one or two iterations, just 10 days.”

Implementing simulation-led design, by embedding CAE tools within a CAD environment, can significantly reduce development times. Now imagine the same individuals are working on producing a new part. Only this time, the designer has access to CAE in-CAD tools. This brings simulation further to the left side of the product development workflow.

Most CAE in-CAD tools are designed to simplify and speed up the simulation workflow, so that most designers with a typical engineer’s understanding of physics can produce results — without an expert. The expert can still validate the work of the designer, but in the meantime they can focus on more complex simulations.

“There are trends of a lot of moving workflows [expanding the scope of traditional engineers] and a lot of engineering newcomers,” says Julien Simon. “So, to provide CAE tools for a beginner to gain knowledge and confidence, this will improve their work and performance.”

These results won’t be as in-depth as what a simulation expert can produce, so they should still be included in the iterative process, but it should be close enough to guide the designer towards a semi-optimized design. Meanwhile, the simulation expert can focus on more in-depth simulations in parallel to the CAD process. This shrinking of the product development Gant chart, via parallel work, should also reduce the total number of iterations to develop or validate a design.

How do reporting managers benefit from all of this?

It’s the reporting manager’s job to synchronize teams and product development processes. Hence, their fundamental duty is to ensure that what is reported between teams is correct and tailored to their needs. Julien Simon explains that with many simulation-led design tools, a part of this data transfer and knowledge sharing is streamlined.

“I saw that for reporting managers, the challenge was they need to understand more of the design,” said Julien Simon. “They then need to take care of, gain and share that knowledge. With simulation-led design they see more productivity and team achievements. They have more KPIs they can use to validate workflows and simulations. They have more things to overlook, and this gives them a way they can validate the data. That gives them more confidence about the project they are discussing.” From Julien Simon’s point of view, it’s about breaking the siloes between teams, engineers and stakeholders by improving communication.

Julien Simon adds that the experience of some stakeholders can also help others on different teams. For instance, after reviewing a simulation, a plant manager may notice there could be an issue producing the part. This can help guide the design team and avoid mistakes later in development. All this communication can then be facilitated by the reporting manager and the design tools.

Why should business leaders care about simulation-led design?

Julien Simon was very upfront about how to discuss the benefits of CAE in-CAD and simulation-led design to business leaders and C-level management. He explained, “from what I’ve discussed with business leaders, simulation moving left isn’t what they are interested in. They are interested in ‘how my team can become more performant.’”

Thus, it is important to show business leaders how simulations can facilitate communication and support initiatives like environmentalism, cost reduction, faster time to market, fewer product delays and reduced manufacturing downtime. In other words, the goal is to show top management that there is a return on investment when shifting simulation to the left.

In this sense, the argument about how simulation-led design can effectively shrink a product development’s Gant chart — by enabling designers and simulation experts to work in parallel — is something business leaders will pay attention to, not the CAE in-CAD tools themselves.

“We are trying to show them that today, development workflows are seen as a line. You start from the beginning with design, then simulation, then manufacturing and so on,” says Julien Simon. “But in reality, the workflow can be more like parallel tracks. By bringing simulation earlier into the process, it exchanges information at an early stage between these tracks, improving the communications between these tracks and decrease the number of iterations.”

There are also unexpected ways that simulation-led design can benefit business leaders. Julien Simon talks of a time he heard a marketing team discussing a product with designers and simulation experts. He said, “In the end, the marketing team put that simulation in the company’s flier because they saw how impressive it was in the meeting. They wanted to share the company’s confidence in its products directly with the consumer.”

NX Performance Predictor and the future of simulation-led design

Julien Simon explains that Siemens Digital Industries Software offers NX Performance Predictor as its specific CAE in-CAD solution. “NX Performance Predictor helps the designer become more performant by helping them use simulation to evaluate a component. With NX Performance Predictor, the idea is to get instantaneous feedback on a part design.”

What sets NX Performance Predictor apart from others is that its environment is optimized to guide simulation newcomers to quickly learn the tool and produce results. “We are making a lot of effort to make the interface and workflows straightforward,” he says. “It’s naturally embedded in the CAD environment so it’s easy to deploy. They don’t need to train on new software, and they don’t need to transfer data from one tool to another. It’s tools they are used to using daily.”

NX Performance Predictor is also optimized to transfer data and geometry to other tools within the Siemens Teamcenter platform, making it easier to share knowledge and communicate with teams. “We see connections between all the data in the enterprise coming,” said Julien Simon. “And we still want to push people to go a step forward to be more performant in their process.” In addition, the NX Performance Predictor simulation data model can be reused in Simcenter for the expert to validate or enrich the simulation.

Julien Simon also hinted towards new features coming to NX Performance Predictor and other Siemens Teamcenter products. For instance, he explained that AI can help democratize simulation — or at least help the user to be more performant — even earlier in the design process. It can, for instance, guide the user during the definition process. He adds that these AI tools won’t replace engineers; rather, they will act as an assistant, help guide users and offer a starting point that then needs to be optimized.

“AI will never replace humans. Human creativity is an important stage,” Julien Simon says. However, “the path to becoming a better designer is not finished today. I see plenty of improvement coming soon.”

To learn more about the role simulation-led design plays in the day-to-day work of a design engineer, read the Performance Predictor installment in the NX Tips and Tricks series.

The post See how simulation-led design adds value to the whole team appeared first on Engineering.com.

Today’s top story is Nemetschek Group’s new AI Assistant, a chatbot which will debut in both Allplan and Graphisoft Archicad.

In Archicad, the AI Assistant will be able to interact with BIM models in limited ways. For example, you could ask the chatbot to render your model in some particular style (such as with a wooden façade), and it will return an image generated with Nemetschek’s “AI Visualizer” powered by Stable Diffusion. You could also ask the AI Assistant to reveal some specific elements of your model, such as “the wall section at the East entry,” and it will bring up the proper view.

In Allplan, the assistant connects to the internet to help users find industry knowledge such as the minimum width of emergency exits in London.

You can see a brief demo of these capabilities in this video from Nemetschek:

This is the first manifestation of Nemetschek’s plan to launch an “artificial intelligence layer” across its portfolio this year, a plan which wasn’t so much a roadmap as a signpost declaring that Nemetschek has, in fact, heard of AI and does, in fact, plan to do something with it.

Well, this is something. The AI Assistant could prove to be a nifty feature for users of Allplan and Archicad, but by now chatbots are basically the “Hello World” of AI applications—the first step everyone takes when trying to figure out a new language. The real question is how far Nemetschek can go from here.

CAD in point: Acquisitions and updates

Here are some quick hits for your news radar:

• Software reseller GoEngineer announced that it’s acquired Canadian reseller CAD MicroSolutions, effective as of January 3, 2025. CAD MicroSolutions customers will retain access to their current software licenses and annual maintenance plans, and can call the same support line as before, according to an FAQ posted by GoEngineer.
• Jetcam released an update for CAD Viewer, its free software for viewing 2D CAD files. The update adds folder and file count display, window position and size memory, and other quality of life improvements.
• Hexagon has acquired CAD Service, an Italian developer of visualization tools. Effective January 21, 2025, CAD Service will join Hexagon’s Asset Lifecycle Intelligence division.
• Datakit announced version 2025.1 of its data exchange software, which includes enhanced support for 2D and 3D B-Rep geometry alongside other updates.

One last link

You have to love it when CAD marketers get catty. Piggybacking on the popularity of Peter Brinkhuis’ blog post 37 things that confuse me about 3DEXPERIENCE, Onshape posted a blog of their own: 37 Ways Onshape Simplifies What 3DEXPERIENCE Overcomplicates.

Got news, tips, comments, or complaints? Send them my way: malba@wtwhmedia.com.

The post Nemetschek Group’s new AI Assistant is a start—but a small one appeared first on Engineering.com.

For our first item, some spice.

I recently reported on the unexpected genre of CAD esports in The fastest 3D CAD modelers in the world. That story is about TooTallToby.com, where a dedicated community of 3D modelers, spanning many countries and software platforms, compete in CAD speed competitions. (Belated congratulations to RamBros, an Autodesk Fusion user from India, for winning the 2024 World Championship).

CAD speedrunning is now expanding to the next generation. TooTallToby.com has launched a tournament for CAD design students at Le Grand High School in Le Grand, California, that will play out through January (click here for the kickoff livestream from Friday, January 10).

I doubt any of my readers are eligible to compete, but I bring this up to share two thoughts.

One: I think these students will crush it. The top seed for the top speed competition last year was a high schooler, and he made it all the way to the semifinals. Even if none of the Le Grand students are currently CAD masters, I can’t think of a better way to motivate them to level up their game (for more on the pedagogical value of CAD speed modeling, read my original article).

Now the spice: I can’t help but wonder how long it will matter.

When will CAD skills, as we currently know them, become obsolete?

I don’t mean to sound cynical. Like I said, I’m sure these students are passionate about CAD and are on track to master it. But it reminds me of my grade school lessons in cursive writing—a skill that was clearly fading in importance even as we spent hours perfecting it.

CAD isn’t fading, but it is ripe for disruption. CAD software—pretty much across the board—has a stale, unfriendly interface that does little to actually aid designers. CAD has hit a wall, and rather than climb it, developers are shuffling sideways, changing how the software is licensed and packaged rather than how it works.

At some point, AI will change that. I’m not just talking about generative AI that makes 3D models from text prompts, though developers are eagerly seeking that grail. Even a little AI implemented well could transform the very nature of CAD, making the software less of a digital drafting table and more of a virtual design assistant.

When will that happen? What will it look like? Those are questions for Nostradamus (and you! Send me your predictions at malba@wtwhmedia.com). In the meantime, it’s nice to see the next generation of CAD users having fun with it.

An upstanding start to Siemens for Startups

Siemens has launched a new program for engineering and manufacturing startups called, sensibly, Siemens for Startups (I have to imagine “Xcubator” was on the table at some point*). Companies that are accepted to the program will get discounted Siemens software and the opportunity to collaborate with Siemens on development, marketing and more.

No cynical take on this one. My main reaction is surprise that this didn’t already exist—many engineering software providers offer startup programs with similar benefits. (Okay, here’s the cynical take: it’s good business to hook ‘em while they’re young.)

One novel bit about Siemens for Startups is that it’s linked with AWS Startup, Amazon Web Service’s startup program, meaning eligible companies will also get access to AWS cloud infrastructure.

Interested? The application process is open now.

*In other Siemens news, Zel X has been renamed NX X Essentials. Xciting!

Stay gold, Nvidia

It seems I can’t go a week without mentioning Nvidia. The chipmaker’s latest news is that it’s launching a “personal AI supercomputer” called Project DIGITS.

Coming this May, Project DIGITS is a $3,000+ PC (or should that be PAISC?) featuring Nvidia’s GB10 Grace Blackwell Superchip, which combines the Arm-based Grace CPU with a Blackwell GPU. The system will have 128 GB of memory and up to 4 TB of storage on board. It will run Nvidia’s Linux-based DGX OS and come preconfigured with the company’s AI software stack.

All that means users will be able to run large language models of up to 200 billion parameters, according to Nvidia. In true Nvidia fashion, you’ll also be able to link up two Project DIGITSes to crank that number up to 405 billion (I don’t know where the extra 5 billion parameters come from).

Learn about the latest BIM trends with me

Building. Information. Modeling. These aren’t just ordered excerpts from Merriam Webster. Together they describe the software tools behind modern design, engineering and construction workflows: BIM.

As with CAD, BIM is also in the midst of major changes. I want to learn more about them, and if you do too, I know just the place.

Sign up for Engineering.com’s upcoming webinar Design: Trends in BIM on Tuesday, January 21 at 12:00 PM EST. I’ll be there interviewing BIM expert Jennifer Schmitz of Short Elliott Hendrickson Inc. (SEH) about all the ways BIM is evolving alongside AI, digital twins, sustainability imperatives, and much more. Plus, you’ll get a chance to ask her any questions I don’t.

See you there!

One last link

Last week I left you with a link to 37 things that confuse me about 3DEXPERIENCE, written by Peter Brinkhuis of CAD Booster.

I guess I’m not the only one who enjoyed that blog post—Manish Kumar, CEO of Solidworks, acknowledged it on a recent LinkedIn post. An excerpt:

“We are humbled every day by the 7.5M+ users around the world who use and love our products and solutions. We are especially grateful to have true friends like Peter Brinkhuis, who challenge us to be even simpler. We take feedback like yours with humility and will continue to simplify our solutions further—always. Your feedback is deeply respected, and we will address it with a sense of urgency.”

Got news, tips, comments, or complaints? Send them my way: malba@wtwhmedia.com.

The post Fun times before the CAD revolution appeared first on Engineering.com.

Traditionally, simulation was used primarily as a validation tool in later stages of product development to verify that a product would meet performance and safety requirements. This approach minimized the risk of costly redesigns and failures post-manufacturing. However, placing simulation at the end of the design process limits its impact on early-stage innovation and conceptualization.

Additionally, well before today’s advanced software and computing technologies, simulation studies had limited computational power, making high-fidelity simulations time-consuming and less feasible, especially for complex systems. High costs and the need for specialized expertise restricted their use to larger organizations with significant resources. Consequently, specialized teams often conducted simulations in isolation, leading to less integration with the overall design and development process.

Modern practices integrate simulation early in the design phase, allowing for rapid prototyping and iterative improvements. Advances in computational power and software capabilities enable automated optimization that reduces iteration time and effort. Today’s software can also handle multiphysics problems, integrating tightly coupled physical phenomena for a more comprehensive analysis. Plus, as simulation tools become more user-friendly, more organizations can adopt modern simulation-driven design approaches.

What is simulation-driven design?

Simulation-driven design moves simulation from the later stages of product development cycles toward the beginning and throughout to inform design decisions. This can accelerate the design phase by allowing for rapid iteration and testing in a virtual environment before producing physical prototypes. It also enables engineers to explore innovative and unconventional designs and materials that may be too risky or expensive to test physically. Integrating simulation with design also helps engineers identify flaws and failures earlier, reducing the risk of costly recalls and redesigns after product launch.

Though this approach makes sense conceptually, implementing it can be challenging. Teams accustomed to more traditional, linear design cycles, where models and files are exchanged between design and simulation engineers, must adopt new collaborative ways of working. Much like moving from a waterfall to an agile approach, teams must transform their culture, not just their technologies or processes.

Some software providers have made simulation-driven design adoption easier by integrating CAD and CAE capabilities into one platform. They also offer cloud-based services to support asynchronous design cycles and disparate teams. Plus, such platforms are becoming more accessible so that designers and engineers of varying skills can leverage the software with less technical experience. This is often referred to as the democratization of simulation, a paradigm which opens CAE capabilities to novices and individuals in various fields. However, anyone using simulation software still needs a fundamental understanding of the problems being solved and the ability to assess the results for feasibility.

Nonetheless, with integrated CAE platforms and a simulation-driven design approach, teams can accelerate designs, improve quality and manufacturability and make physical prototyping and final testing more efficient and cost-effective.

What is the difference between a digital twin and a simulation?

The terms “simulation” and “digital twin” are sometimes used interchangeably, yet they refer to different technologies and are used for different purposes. Distinctions among the terms and technologies are up for debate and will likely continue blurring over time.

Engineers often use simulation software to mathematically model and test designs before manufacturing and to understand post-production design failures. In contrast, digital twins are virtual models that replicate the status, operation and condition of a real-world asset, such as a production-line robot or compressed air system. This requires sensors and transmitters on the physical asset to send real-time data to the software.

Though different in function, simulation and digital twins can intersect to improve products and systems.

For example, engineers may create a digital twin of a real-world machine and then test it under certain conditions. With data continuously and accurately sent to the software, engineers can simulate how changes will affect the digital twin before adjusting settings or replacing components on the real-world machine.

From a data perspective, digital twins ideally have two-way communication with the physical assets they represent, while simulation typically only receives information. Also, digital twins continuously integrate real-time data, while simulation uses static data for model analysis. However, simulations can run parallel with digital twin data feeds to predict future states, optimize maintenance schedules, identify potential issues and suggest improvements.

How is artificial intelligence impacting simulation?

Every industry is exploring how artificial intelligence (AI) can improve technology and processes. From machine learning (ML) algorithms to large language models (LLMs), such as ChatGPT, capabilities abound to reduce costs and increase efficiency and quality.

In the simulation world, AI could be a gamechanger. It can automate tasks and streamline workflows so designers and engineers can focus on higher-value work that only humans can do. It also opens doors for non-experts to create designs and approximations with less technical skills.

For instance, AI algorithms can optimize computational processes and reduce run times. Simulation techniques such as reduced-order modeling (ROM) use AI to simplify complex models and speed up solving without compromising accuracy. ML algorithms can also improve validation by continuously learning from simulation results to detect errors and anomalies.

Some software providers are exploring a bottom-up approach to develop physics-based AI models that bypass the mathematical equations underpinning current solvers. Such software could analyze a CAD model’s behavior under loading conditions in a fraction of the time compared to traditional solvers. Simulation studies could be 100 times faster due to the AI algorithms alone and another 10 times faster using GPUs.

While a bottom-up approach attempts to create a general-purpose simulation AI trained on physics, top-down AI targets specific problems based on narrow training data. A top-down approach can be applied to any simulation problem, but as soon as the problem is tweaked, the simulation breaks down, and the AI must be retrained. Though much more limited than bottom-up simulation, it is also easier to develop, which is why many simulation companies have already begun commercializing it.

Of course, there are caveats. AI’s effectiveness relies on data quality and availability. Poor data can lead to inaccurate models and predictions. Also, AI often requires significant computing resources, especially for training complex models, which can seem more like a problem shift than a solution. Regardless, many engineers look forward to incorporating more AI capabilities into simulation software to solve bigger problems faster and more accurately.

The post How is simulation software used in product development cycles? appeared first on Engineering.com.

If you’ve gotten sick of the AI hype of the last couple years, I have bad news for you: it’s not going anywhere. Generative text and images may be old hat by now, but engineering software developers (and venture capitalists) are sprinting to bring AI into the third dimension.

That race got a little more crowded late last year when a startup called Backflip emerged from stealth with $30 million in funding from NEA and Andreessen Horowitz. Calling itself a “3D generative AI company,” Backflip offers a design platform that turns text prompts into 3D-printable models. The company was founded by the same duo that launched 3D printing company Markforged: Greg Mark, serving as Backflip’s CEO, and David Benhaim, CTO.

Backflip isn’t the first to try this. Last year I reported on Autodesk’s Project Bernini, a text-to-3D generator that’s theoretically impressive, but still far from being a practical design tool.

Is Backflip any better? I wish I could say. While the platform offers a free trial, there’s currently a waitlist due to “overwhelming demand.” I’ll weigh in when I can. (If you’ve tried it, let me know what you think at malba@wtwhmedia.com.)

AI for electrical engineers… maybe

I told you the AI fervor wasn’t going away. Another example comes from Cadstrom, a Canadian startup developing AI tools for PCB design validation that recently announced $6.8 million in seed funding. Cadstrom claims that their generative AI-based software will help electrical engineers avoid costly PCB redesigns and shorten design cycles by as much as 66 percent.

I’ll believe it when I see it. I can’t help but be reminded of SnapMagic, formerly SnapEDA, which announced in 2023 that it had developed a generative AI for circuit design. Fourteen months later, I’m still waiting to see anything other than provocative screenshots. Let’s hope Cadstrom can deliver quicker.

Speaking of quick, Nvidia won’t slow down

There’s got to be something to all this AI hype, right? Well there’s certainly something in it for Nvidia, which has ridden the AI wave to become the second most valuable company in the world. The chipmaker made a characteristically dense series of announcements today at CES in Las Vegas focusing largely on products and partnerships in industrial AI.

Among those products are three new Omniverse Blueprints, reference workflows for developing AI-connected digital twins in the company’s Omniverse platform (here’s more on one of the first Blueprints Nvidia announced a couple months ago for real-time simulation).

The new Blueprints are Mega, for testing and developing robot fleets; Autonomous Vehicle (AV) Simulation, for AV developers to review and generate data; and Omniverse Spatial Streaming to Apple Vision Pro, which helps developers create apps to visualize digital twins on Apple’s mixed reality headset.

The partnerships include the usual who’s who of engineering software developers: Altair, Ansys, Cadence, Siemens and quite a few more. They’re all using Omniverse or integrating it into their own software in some way. Siemens, for example, just launched the Teamcenter Digital Reality Viewer, an app for photorealistic visualization powered by Nvidia Omniverse libraries.

That’s just a taste of everything Nvidia announced at CES—for all the details, read the press release here. (Tip: you might want to keep the Nvidia glossary open in another tab.)

One last link

I’ll leave you with something that made me laugh recently: 37 things that confuse me about 3DEXPERIENCE, written by Peter Brinkhuis of CAD Booster.

Got news, tips, comments, or complaints? Send them my way: malba@wtwhmedia.com.

The post For engineers, AI anticipation continues in 2025 appeared first on Engineering.com.

Today we bring you some bonus questions and answers in which Sjodin muses on the biggest trends in simulation for 2025. Happy new year and happy simulating!

This interview has been edited for clarity and brevity.

Engineering.com: How do you see AI being used in simulation over the next few years?

Bjorn Sjodin: One thing is that simulation engineers, especially beginners at this stage, are using AI chatbots. It’s like having a tutor that you can ask basic simulation questions. ‘If I’m doing this heat transfer simulation, which type of boundary conditions do I have to choose between?’ And the chatbot can give you some answers that will lead you on the way. Sometimes it hallucinates, you know, gives the wrong answer. But if it’s something that is common simulation knowledge by experienced engineers, then the chatbot will probably know some of that. So I think right now it will be very helpful for beginner users.

[One of Sjodin’s favorite features in Comsol Multiphysics 6.3 is a new integration with ChatGPT. -Ed.]

Another area where you can go beyond beginners already is when it comes to API programming. Many of our users would like to automate simulation tasks in various ways, either by writing simulation apps or just to write Java code to automate common tasks that they are doing repeatedly. Our users can use their models to build simulation apps that can be used by those that are not simulation experts, necessarily, but consumers of simulation technology.

And to build those user interfaces, sometimes you would like to write some code. And the chatbots can help you in in programming tasks. They can help you debug your code. They can help you make your code more efficient. And that is something that we are seeing already. So using our new chatbot tool, for example, you can ask it, ‘how can I write a for loop that would automate this task?’ and the chatbot will answer back with code that you can paste into your Comsol model and run. Often it works—not always, sometimes it will give you the wrong answer—but very often it will give you very good answers. If you’re trained in how to guide it, then you can get very efficient and productive with those code snippets.

Do you think it’ll reach a point where it can build the app on its own?

Yes, I think so. That’s where everything is headed. The big question is how fast we will get to that point. No one can answer that, but yes, it is heading in that direction right now.

The biggest limitation that I see is that the current generation of chatbots, they don’t have particularly much spatial perception, so they don’t understand what a CAD model is. They don’t necessarily know the difference between a sphere and a cube and so on. But that will probably change. It can do some of that now, but not to any great extent. When that improves, then you will see more of this complete automation of modeling tasks. I think there’s still a lot of very difficult tasks that the simulation engineers will have to do. It might take decades before we get to full automation there.

In addition to AI, what are the other major simulation trends you’re seeing as we head into 2025?

There’s a lot of focus on reduced order modeling and what we call surrogate models, where you basically compactify your heavy simulations to get very lightweight models that can still give you the same results. They are precompiled, we should say. You precompile it for a wide range of parameters so that you don’t have to go to that full simulation that may take hours to run, and instead you get something that only takes seconds to run.

The reason for that trend is that people want to build digital twins. They want to build fast simulation apps. They want to use more complicated models in system simulations. So all of that requires that you can make your models faster to run. And, yeah, neural networks come into play there, but also other technologies, more traditional reduced order modeling technologies.

Could you elaborate on some of those traditional technologies?

Yeah, the most classic technique is that you have maybe a structural mechanics simulation that requires you to solve for maybe a 10 million by 10 million matrix system. There are techniques based on eigenvalue, eigenfrequency analysis that will capture all of the most essential aspects of that model and bring it down from a 10 million by 10 million matrix to maybe a 100 by 100 matrix, and give you almost the same answer as the big model. And if you want to do a system simulation, then that’s all you need. You don’t need the full high fidelity model. You only need maybe some simple inputs and some simple outputs.

So there are traditional techniques for bringing down large system matrices to smaller ones by analyzing the matrix structure in various ways, and those are usually called reduced order models, or model order reduction, or model simplification. They are good for some cases. Neural networks are good for other cases. And there are other machine learning technologies that are useful, and hybrids of these as well.

How flexible are the neural network-based surrogate models? Do slight adjustments greatly impact their accuracy?

They are surprisingly good. You typically have a parametric model, maybe with five, six parameters, and you give it some range. So imagine that you have five, six parameters that are driving your simulation. These could be CAD parameters, material properties, boundary loads, etc. And they vary within max and min values. You feed that through the neural network, and the neural network starts sampling in this large parametric space and building up this neural network model.

As long as you’re staying within those parametric ranges that you pre-trained the model for, it is very good. It could be arbitrarily good. Actually, it could be just as good as the finite element model. The only question there is how much time you are willing to spend on training the model. Do you have five minutes or do you have 50 hours? The more time you have, the more accurate these models can be. So they are basically as good as you have time to train them.

You can give it to someone, and you don’t know which numbers they are typing in, but you know at least that they’re going to be in these intervals. Then they can get an answer in one second instead of one hour.

That’s a huge difference, especially if you have someone on the factory floor or in a production setting. They are not necessarily willing to wait for one hour for the simulation app to come back with an answer. They want an answer in five minutes, max—but probably seconds, and that’s what these surrogate models can provide you.

The post Simulation trends for 2025: Get ready for AI and surrogate models appeared first on Engineering.com.

For example, engineers may need to analyze the stress and deformation of an aircraft wing under aerodynamic loads or compute thermal deformation in engine components. FEA is also used when the fluid and structural response need to be analyzed simultaneously, such as interactions between blood flow and artery walls or dam deformations due to water pressure.

Computational fluid dynamics (CFD) simulation is used when the primary focus is on analyzing fluid flow, heat transfer and related phenomena. This includes the behavior of fluids in motion, such as airflow over a car body or water flow around ship hulls. CFD is also used when analyzing thermal phenomena involving fluids, such as convection cooling in electronic devices and combustion processes in engines.

In cases where fluid flow affects structural integrity and vice versa, an integrated approach using both FEA and CFD might be necessary. For example, aerospace engineers may use CFD to determine the pressure distribution over an aircraft wing and then use FEA to analyze the wing’s structural response to the pressure. Similarly, civil engineers may use CFD to simulate water flow around a pier and compute the hydrodynamic forces, and then use FEA to assess the pier’s structural integrity in response to such forces.

The general rule of thumb is to use CFD when the problem primarily involves fluid behavior, heat transfer within fluids or the interaction between multiple fluid phases, and to use FEA when the focus is on the structural responses to external forces, including those induced by fluid flows, thermal stress and FSIs.

Alternatively, engineers can use multiphysics simulation to solve FSI problems simultaneously.

Multiphysics software includes coupled solvers that can handle the simultaneous and dynamic interaction between fluids and solids and solve the governing equations for both in a synchronized manner. It can also handle moving and deforming meshes to model how fluid changes shape due to structural deformation.

Multiphysics simulation uses either a monolithic or partitioned approach to ensure convergence of the coupled FSI problems. The monolithic approach simultaneously solves the fluid and structural equations in a single system of equations, which can be more stable but computationally intensive. With the partitioned approach, the software solves the fluid and structural equations separately and iteratively exchanges boundary conditions until convergence is achieved.

Coupling methods for the partitioned approach are considered either one-way or two-way data exchanges depending on how the fluid flow and structural deformation affect each other. In a one-way data exchange, the simulation is performed in a particular order, and the results from the first analysis are inputs for the second analysis, which doesn’t affect the first. This is similar to running a CFD simulation to calculate aerodynamic pressure on an aircraft wing and then running an FEA to analyze the wing’s structural response. In a two-way data exchange, the fluid and structure affect each other dynamically, so the simulation runs iteratively and exchanges data back and forth to more accurately represent the coupled behavior.

Overall, the choice between using separate FEA and CFD software, multiphysics software and the simulation approaches and methods depends on the nature of the problem being studied, the required accuracy and the available time and computational resources.

The post When should FEA, CFD and multiphysics simulation be used for fluid-related problems? appeared first on Engineering.com.

To learn more about the latest update and why it’s a big deal for Comsol Multiphysics users, Engineering.com sat down with Bjorn Sjodin, senior vice president of product management at Comsol. Sjodin took us through his favorite features of the new release and shared his insight into the evolving simulation landscape.

The following interview has been edited for clarity and brevity.

Engineering.com: What are your favorite features of the new release?

Bjorn Sjodin: The new product is probably my favorite feature, the electric discharge module. It’s an add-on product to Comsol Multiphysics for simulating electric discharges.

For example, say you’re in your car and you get statically charged and you touch some of your control panels, and there’s a little spark that destroys electronics. Or it could be that someone is expanding equipment for next generation power grids for renewable energy and electric vehicles. Lots of new power system equipment is needed, and with that comes safety concerns. The electric discharge model can help in evaluating those new equipment designs for safety, so that people that service that equipment don’t get accidental discharges in their bodies.

How would users simulate that kind of thing prior to this release?

We have some very basic functionality for this in previous versions. You can use electromagnetic simulation to detect where there is risk for electric discharge by very simple means.

But with the electric discharge module, you can do it more efficiently. You can detect where there is risk for electric discharge, but you can now also simulate the actual discharge phenomena themselves for the first time. You couldn’t do that before, because the physics involved is very, very complicated, so it has to be pre-packaged for users to make it easy to use. And that’s what we have done in this new module. And we hide all the complicated physics behind the scenes so that the users get a friendly interface where they can enter their CAD geometries and their material parameters and what have you.

Is the electric discharge module something that users were requesting?

Yes they were. We have had users using our other tools for various discharge and discharge-like phenomena for 20 years, so this is something that people have requested for a long time.

The issue, though, has been with the speed of the computers. We could have done this many years ago, probably, but computers weren’t strong enough. Now they are. So now it is reasonable to do these types of simulations, because the phenomena that go into these are quite complicated. It’s a combination of electromagnetics, fluid flow and chemical reactions that requires a lot of computational power.

Is that generally how you plan your development roadmap, based on user requests and computing power?

Yes, those are certainly elements. How we decide products is kind of complicated. It depends on what are customers asking for? What is technically possible to do? Do we have the people on board that could produce such products? Those are usually the most critical factors. And of course, is there high enough demand?

Any other favorite updates in Multiphysics 6.3?

We have GPU support for the first time in Comsol in two different ways in 6.3. One is that we have GPU support for accelerating acoustic simulations. Transient phenomena in acoustics could be like you have some sound, some noise happening in a room or in a car, for example, and then this new technology makes it possible to see how the acoustic pressure rays are propagating through the room or the car over time. And it could be up to 25 times faster than previous versions without GPU, which is fantastic. That’s like between one and two orders of magnitude faster.

We also have GPU support for another application, and that is to train the neural network models that people have started to use. Essentially, you build a finite element simulation first, and then you train one of these neural network models to replace the finite element simulation. So it’s not a replacement for the simulation. You have to do the simulation first, but then you train your neural network, and you use that as a fast replacement for the finite element simulation.

That gives you instantaneous results if you need that for some reason. Maybe you’re doing a system simulation. Maybe you are creating one of our simulation apps and you want your users to get instantaneous results. So you then pre-compile these simulations by using neural networks. That now has GPU support, so you can train these neural networks much faster than previous versions—20, 25 times faster than before.

Is that a typical speedup or more of a best case scenario?

It’s a typical speed up. In general, it will depend highly on the CAD geometry. Is it a uniform geometry? Is it very elongated? All of those kinds of different modeling aspects will affect the speed. It could vary from case to case. Could be faster, could be slower, depending on the details of the model.

You do have to invest, though, in a dedicated GPU card. You can’t just use any card. If you’re interested in GPU acceleration, you should invest in a high end card. Otherwise you don’t get that dramatic performance boost. We support Nvidia cards.

Do you support AMD graphics cards as well?

We don’t support AMD yet, but we will look into all possibilities for the future, of course.

Is the new electric discharge module also GPU-accelerated?

It’s not running on GPU yet, but we certainly hope to have that running on GPUs in the future. Multiphysics simulations have always been difficult to accelerate on GPUs, but new technologies are emerging now that will we think will make this possible more and more moving forward.

If you look at the GPU support that is out there in the simulation world, it’s usually for single physics phenomena, because that’s where it was easy. It’s true for anything simulation. You start with one physics, and then when that is mature enough, you go to two, and then three and four and so on. So it’s the same thing with GPU, which is a relatively new technology.

Can you tell me about the automated geometry preparation tools in Multiphysics 6.3? Are they based on AI?

That is based on what we call heuristic rules. I wouldn’t call it AI. That is more based on the experience we have built up over the years on what type of simplifications customers would like to have with CAD models to make their meshing easier, to make their simulations faster.

So that was possible before, but with manual means. Now, we have made it an option to let the computer do all the work for you, all the simplifications for you, automatically. And this is the first time we have done that. It’s very exciting and extremely useful for all of our large industrial customers who have complicated CAD geometries.

How much time could users save with these automated tools, as opposed to the manual way?

Best case scenario, you could go from hours to minutes.

The final new feature I want to ask you about is the chatbot. Is that some sort of ChatGPT-like model?

Yes,exactly. It’s not even like ChatGPT, it is ChatGPT. We connect to ChatGPT, so we don’t ship a chatbot with Comsol Multiphysics.

The idea here is that the user will connect to their own preferred chatbot. And in this first version of this, we allow connections with ChatGPT, but we hope to connect to other chatbots in the future as well. So the user will provide their subscription information in Comsol, and that will establish a link to ChatGPT. And we then prime the questions that you ask with context so ChatGPT understands that this question is coming from a Comsol Multphysics user from the Comsol Multiphysics user interface. So it knows what to send back. So what we have implemented is establishing the communication channel and the context by priming it with some prompts, basically.

Interested in more insight from Bjorn Sjodin on the latest trends in simulation? Stay tuned for a bonus Q&A (sign up for Engineering.com’s Simulation newsletter to make sure you don’t miss it).

The post The best updates in Comsol Multiphysics 6.3: “Let the computer do all the work for you” appeared first on Engineering.com.

SimScale announced that it will offer three free months of its cloud-based simulation software through the Onshape Startup Program. Eligible members of the program will have access to SimScale’s Professional tier, which provides structural, fluid, thermal and other simulations.

“At SimScale, we’re committed to democratizing access to powerful simulation technology, empowering engineers and designers to make informed, data-driven decisions starting at the earliest stages of product development,” said David Heiny, SimScale CEO, in the company’s press release. “Our relationship with Onshape exemplifies this mission and provides start-ups with access to a complete, cloud-based toolkit that is flexible, accessible, and scalable.”

Separately, SimScale also announced a partnership with Hexagon that will see Hexagon’s Marc nonlinear finite element solver available through the SimScale platform.

ABB and ESS to develop simulation tools for automotive paint shops

Engineering firm ABB announced that it will collaborate with Engineering Software Steyr (ESS) to develop simulation tools for automotive paint shop operations. Typical automotive paint processes require more than 20 highly variable steps, according to ABB, which hopes that making simulation tools accessible to more companies will help reduce the time and cost of the painting process.

“Delivering faster and more energy efficient solutions for the paint process is the final piece of the puzzle in digitalizing the manufacturing transition in the automotive industry,” said Marc Segura, president of ABB’s robotics division, in a press release. “The innovative solutions we are developing with ESS will cut vehicle development time by up to a month and generate cost savings of up to 30 percent, making manufacturers more competitive, efficient and resilient.”

ABB has also taken on a minority investment in ESS, but did not disclose financial details.

UniPlot now available as a Matlab add-on

Matlab and Simulink users can now access a connector for UniPlot, a data analysis tool that provides advanced visualization, data filtering, automated report creation and more. The connector, called UniPlot As PostProcessor in Matlab’s add-on explorer, opens after every Simulink simulation to give users quick access to their data in UniPlot. A UniPlot license, available in perpetual and subscription versions, is required.

Altair and Auburn University collaborate to advance vortex rocket engines

Altair says it will work with Auburn University’s Samuel Ginn College of Engineering on a $1.25 million AFWERX Phase II STTR contract to advance vortex rocket engines. The simulation developer, which is in the process of being acquired by Siemens, took over the collaboration from Research in Flight, its own acquisition from April 2024. Research in Flight’s technology is now a part of Altair’s HyperWorks platform and is called Altair FlightStream.

“FlightStream empowers users in unique ways, bridging the gap between high-fidelity CFD simulations and engineering demands to set industry standards for efficiency, accuracy, and speed,” said Pietro Cervellera, senior vice president of aerospace and defense at Altair, in the company’s press release.

The post Onshape startups to get SimScale free for 3 months appeared first on Engineering.com.

High-performance computing (HPC) is an advanced option for larger, highly complex problems that yield higher-fidelity results. HPCs resemble server racks with tens, hundreds or even thousands of CPUs working in parallel to divide and conquer large computational tasks. Such environments use resource management tools and job schedulers to allocate resources and manage job queues efficiently.

On-premises HPC units can require extensive upfront investments and ongoing maintenance, impractical for many startups and small to medium-sized businesses. Even larger companies may grimace at the initial and long-term costs and effort of managing such resources on-premises or offsite, even if maintenance is outsourced. Therefore, cloud computing offers a flexible alternative for accessing high-performance hardware without significant upfront investment. With cloud-based HPC, multiple engineers can run complex simulations in minutes or hours instead of days or weeks and pay only for their usage. Alternatively, SaaS tools relieve engineers and IT departments of setting up and maintaining complex HPC cloud environments so they can run simulations around the clock.

While planning a simulation study, engineers must consider their available time and computational resources to solve a given problem. Defeaturing, refining and mesh optimization strategies help reduce the number of equations needed to represent the problem, yet computing capabilities significantly influence the study’s reliability and total costs.

What coding languages do simulation engineers need to know?

Coding skills are not always necessary but can boost engineers’ capabilities for various simulation tasks. For example, when dealing with highly complex or custom simulation problems, engineers may need to write custom code to implement specific algorithms or models. They may also need to write scripts to automate repetitive tasks, integrate different software tools or customize workflows.

When using open-source simulation tools, engineers often need to modify or extend the existing code to fit their needs. However, many popular simulation software packages include GUIs for engineers to set up, run and analyze simulations with low or no code.

Nonetheless, it is never a bad idea for engineers to learn or brush up on coding skills. Commonly used languages in engineering simulation include Python, C, C++, Java, JavaScript and Matlab. The choice of programming languages in simulation depends on factors such as the engineering field, software used and available computing resources.

The post What computing resources and skills are required for engineering simulation? appeared first on Engineering.com.