Implicit door-hinge analysis depicting the resulting von Mises stress This commonly executed mis-use load case in automotive industry. A computer simulation is challenging in various ways. For instance, on the one hand, a robust contact algorithim is necessary and, on the other hand, the solution procedure needs to be acurate and stable over longer simulated time periods. To cope with this, LS-DYNA comes with the Mortar contact algorithm providing accuracy and robustness when running implicit simulations. Moreover, LS-DYNA allows for an easy swtich from explicit crash simulation to implicit analysis, thus, saving a significant amount of conversion time.
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Discover new strategies for faster, more innovative machine designs. Get this white paper and find out how digital twin simulation enables engineers to explore new design possibilities and make decisions sooner. https://sie.ag/56gy8j
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Discover new strategies for faster, more innovative machine designs. Get this white paper and find out how digital twin simulation enables engineers to explore new design possibilities and make decisions sooner. https://sie.ag/56gy8j
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Discover new strategies for faster, more innovative machine designs. Get this white paper and find out how digital twin simulation enables engineers to explore new design possibilities and make decisions sooner. https://sie.ag/56gy8j
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Discover new strategies for faster, more innovative machine designs. Get this white paper and find out how digital twin simulation enables engineers to explore new design possibilities and make decisions sooner. https://sie.ag/56gy8j
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Discover new strategies for faster, more innovative machine designs. Get this white paper and find out how digital twin simulation enables engineers to explore new design possibilities and make decisions sooner. https://sie.ag/56gy8j
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Discover new strategies for faster, more innovative machine designs. Get this white paper and find out how digital twin simulation enables engineers to explore new design possibilities and make decisions sooner. https://sie.ag/56gy8j
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Recently set up a non-orthogonal 5-axis machine model. During Vericut simulation, C0° position is correct, but there is a noticeable shift at C-180°. Feels more like a kinematics definition issue rather than post processing, but still digging into it. Curious if anyone has run into similar behavior?
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Modern systems demand accurate current measurement—but ESL can quietly disrupt correlation between simulation, bench validation, and real-world performance. In our latest article we explore why traditional compensation methods may mask underlying errors instead of eliminating them and what engineers should consider when designing for high-speed current sensing. Read the full article here: https://lnkd.in/ed8SE8BT #CurrentMeasurement #PowerElectronics ##EngineeringInnovation #ESL #CurrentSensing #PowerSystems #InsideTFT
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One of the most frustrating problems in hydraulic simulation: Valves that never settle. If you’ve worked with PRVs, PSVs, or FCVs, you’ve probably seen it: - open → close → open → close - small changes → big swings - models that refuse to converge And the worst part? It’s rarely clear if the issue is your network, the control logic or the solver itself At WatDis, we’re currently working on something very focused: **Just making the solver more reliable.** We’re building a module that can **detect valve oscillation as it happens**, using solver telemetry like: - repeated state flips and residual patterns And instead of just failing, it tries to answer: *why is this happening?* The goal is simple: Turn this: “the model doesn’t converge” into this: “this valve is oscillating because X, try Y” Still early, but this is the kind of work that matters if we want hydraulic models to be: **predictable, stable, and trustworthy**
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How many design variants can your CFD team realistically evaluate before a deadline? For most automotive engineering teams, the answer is limited by turnaround time, compute cost, and the bottleneck of sequential simulation workflows. This webinar shows how ANSA and KOMVOS, from BETA CAE, embed AI natively into existing CAE tools, scaling design exploration without new toolchains, external environments, or data movement. 🔧 What you'll learn: • How AI integrates into CFD pre-processing and optimization workflows • How past simulations become reusable intelligence • How turnaround drops from days to hours, without sacrificing physics fidelity If you work in automotive CFD and want to do more with the tools and data you already have, this one is worth your time. 👇 Registration link in the comments.
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