Engineering Simulation and Modeling

How do engineers simulate turbulence? Let’s explain it like you’re 5. Imagine you’re watching a big football match. The players are running everywhere, passing the ball, the crowd is cheering. It's total chaos sometimes. Now, you have three ways to understand what’s happening on the field: 1. DNS (Direct Numerical Simulation): You follow every single player, every step, every pass, every move of the ball, every bounce. You know exactly what’s happening. But it's exhausting and takes a super powerful camera and endless storage! ➡️ In fluid flow, DNS resolves every little swirl and eddy (smallest to largest). Super accurate but takes huge computing power and storage. 2. LES (Large Eddy Simulation): You follow only the star and big plays, like goals and key passes. For the small moves and background players, you just make a smart guess. You still understand most of the game, and it's easier to watch. You might miss some tiny details. ➡️ In fluid flow, LES resolves the big turbulent eddies and models the small ones. A balance between detail and effort. 3. RANS (Reynolds-Averaged Navier-Stokes): You don’t watch every move. You just say, “On average, this team had more possession and scored more goals.” Fast and easy to understand the overall result. But you miss all the exciting plays and details. ➡️ In flow, RANS gives the average effect of turbulence on the mean flow, without tracking all the chaos. It all depends on what you need. The full match analysis? (DNS) The highlights? (LES) Or just the final score? (RANS) That’s how engineers balance accuracy and computing power in turbulence modeling. #mechanical #aerospace #turbulence #automotive #cfd #aerodynamics

A few years ago, I learned the hard way that jumping straight into hardware, sensors, motors, and wiring can lead to costly mistakes and late-night headaches. That’s when I discovered the true importance of #simulation in robotics and engineering. During the early phase of my final-year thesis, I spent weeks recreating our school cafeteria with Iman Tokosi in Blender, exporting it as an SDF model and loading it into Gazebo using #ROS2. Suddenly, I could drive a virtual robot through aisles and around tables without the fear of damaging anything real. It was challenging and eye-opening, and it saved me countless hours and resources. Then came the moment that changed everything: integrating #SLAM so the robot could build its own map while moving, and setting up #Nav2 to let it plan and follow paths autonomously. Watching it navigate the environment with precision and independence was a powerful confirmation that the system worked. Now, imagine a world where every structure, product, and system is simulated down to the smallest detail. The result? Reduced costs, faster development, increased reliability, enhanced safety, and stronger adherence to standards. Some may still view simulation as “just for show,” but I’ve experienced firsthand that it’s the foundation of true innovation. Are you leveraging simulation in your next robotics or engineering project? Let’s connect and exchange ideas!

Do you understand the "why" from the engineering simulations you conduct? My greatest technical mentors always instilled a strong foundation in first principles, or "the first basis from which a thing is known." Unless you already know the answer (know what to expect or how to interpret what you see), how can you confirm the result validity from a simulation? When dealing with complex, multi-variable systems, I think of this as the "why" of what is being observed. With the increasing penetration of power electronic converters and the rapid change of the power system, the need for modeling and simulation engineers continues to increase. Access to modeling tools has made everyone capable of installing software and appearing as an expert. The most talented engineers I know are lifelong learners. I'd encourage everyone to keep their sense of curiosity. Ask questions and strive for a deeper understanding of what the simulation tools seem to be telling us. Don't assume the output is correct, or even the input.   We should always employ good engineering judgement and make ethical decisions about how we treat assumptions. Situations such as those in the comment below likely occur to make a simulation result look "nicer" or are misunderstandings of physics. A poor looking result doesn't always mean an inaccurate result. When best practices and sound engineering judgement have been used, care should be taken when making adjustments to ensure validity. Otherwise, it's not the simulation result that's in error, but the input data or assumptions (think garbage in equals garbage out). Loading input files and hitting run is not a valid approach to performing a technical study. Similar to the mantra of "measure twice and cut once", the testing and evaluation of sub-systems or components should ensure the quality of what's being used for the task at hand (be it harmonic analysis, dynamic stability, small-signal stability, or any other analysis type). Take your time to understand or run sensitivities to determine root cause of behavior observed, know how things are modeled and “why”, then research the control theory, electromagnetic response, or physical machine properties. Ask questions to those who can help shed some light on what's being observed.   Keeping our focus on the "why" will continue to make for more informed decisions and incredible engineers. #PowerSystems #PowerElectronics #ControlSystems #Modeling #SystemStudies #RenewableEnergy

🏎️💡 Continuing from my last post on F1 racing powertrain advances… The incredible jump to 350 kW MGU-K systems and 8.5 MJ energy recovery per lap in Formula 1 is not just a natural evolution. This is not by chance...it’s the result of relentless engineering by teams using cutting-edge simulation and Digital Twin technologies. Yes, it ties back to my past posts about Digital Twin (DT) ! 🧠 Five years ago, Prof. Huan X. Nguyen of the London Digital Twin Research Centre published a visionary piece on how DT is changing F1 decision-making (see link in first comment). Last year, Gabriel Loffredo of Globant wrote about how AI and cloud computing are now essential in DT deployment for motorsport (see also link in first comment). These ideas are no longer futuristic.... they are embedded in how F1 teams operate today. 🔁 What is a Digital Twin in F1? A Digital Twin is not just a 3D model. It is a real-time, adaptive, executable replica of the physical system, built from simulation, control algorithms, and telemetry. It combines: Multiphysics modeling (EM, thermal, structural) Real-time control logic Telemetry integration AI/ML for predictive behavior HPC and cloud co-simulation 🧪 How teams build and use a DT for an F1 MGU-K system Design & electromagnetic modeling Tools: JMAG, ANSYS Maxwell, Motor-CAD Thermal and structural simulation Tools: JMAG-Thermal, Flotherm, Abaqus Simulates behavior at 50,000 RPM and >150°C winding hotspots. Loss map generation & efficiency optimization Input to system-level simulation to assess real-world energy flow. System-level hybrid simulation Tools: GT-SUITE, Simulink, AMESim Includes inverter control, bus voltage stability, regen dynamics. HIL testing with real firmware Hardware-in-the-loop using dSPACE or OPAL-RT Validates control logic under dynamic conditions. Cloud integration + AI updates Real-time telemetry feeds improve accuracy. AI adjusts degradation models, performance maps, and MPC parameters. DT co-execution during test & race DT operates in parallel with physical system for live tuning, failure prediction, and thermal envelope tracking. 📊 Why it matters Cut development time by 30–40% Replace thousands of hardware prototypes Enable in-race predictive decision-making Integrate with OTA updates and telemetry feeds Accelerate innovation while controlling risk This is what modern engineering looks like. 350 kW boost, regen de folie, Rekuperation wie verrückt, rigenerazione da paura — I’m jumping partout, vor Freude, dalla gioia… ALL AT ONCE!! 🤯⚡🏎️💨

✈️ Would you trust engineers working with 1980s-era resolution to design a next-gen aircraft? In simulation, mesh cells are like pixels: the more you have, the more detail you capture. But until now, even advanced engineering teams were limited to coarse meshes—far from the fidelity needed to fully trust or iterate on complex systems. 🛠️ Simulations at industrial scale have long been the bottleneck in designing and operating complex systems—from aircraft to cars to energy infrastructure. For decades, most of the improvement has been coming from Moore's law - CPU speed improvements, rather than software (some of which still use Fortran, an insider admitted during one of my many reference calls...) Enters AI and an amazing team who've been obsessed with that problem for year. So far, most models have broken down beyond meshes with a few 100K cells on multiple GPUs. But yesterday, the Emmi AI team released AB-UPT, the first fluid dynamics model scaling beyond 150 million mesh cells, running on a single GPU, and delivering real-world physics accuracy. - 150 million mesh cells (no typo, that's 1000x more) - Real-world accuracy on a single GPU This is not just faster simulation—it’s AI-native simulation that finally bridges the gap between research demos and industrial-grade engineering. It means aircraft designers can explore concepts in minutes instead of weeks. It means complex systems can be simulated in real time as they operate. At Serena Data Ventures, together with Juliette, Matthieu, Floriane, Bertrand and Charline, we invest in foundational technologies that shift what’s possible in infrastructure and foundational software. Johannes, Dennis, Miks and their whole team are doing just that—and doing it fast. Hats off to all of them for this game-changing breakthrough! 📄 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗽𝗮𝗽𝗲𝗿: https://lnkd.in/deX_zQWx 🤗 𝗧𝗿𝘆 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹: https://lnkd.in/dmd-xtpR 💻 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲: https://lnkd.in/dZY6EW_P 🧪 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝗱𝗲𝗺𝗼: https://demo.emmi.ai/ #FoundationalAI #CFD

𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝗣𝗵𝘆𝘀𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗙𝗶𝗻𝗶𝘁𝗲 𝗘𝗹𝗲𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱 (𝗙𝗘𝗠) The Finite Element Method is a powerful framework for modeling and analyzing a wide range of physical systems. It follows a structured and repeatable workflow: 1. Define the Physical Phenomenon 2. Formulate the Mathematical Model 3. Convert to a Variational (Weak) Form 4. Discretize the Domain into Finite Elements 5. Assemble and Solve the Finite Element Equations 6. Postprocess: Compute Derived Quantities of Interest To illustrate this workflow in action, here are eight example applications from across engineering and science: 1. Solid Mechanics – Axial deformation of bars 2. Fluid Mechanics – Poiseuille flow and simplified Navier-Stokes 3. Thermal Analysis – Steady-state 1D heat conduction 4. Mass Transport – Diffusion via Fick’s Law 5. Electrical Conduction – Ohm’s Law in differential form 6. Solid Mechanics – Bending of beams 7. Heat Transfer – With distributed convection 8. Wave Propagation – Frequency response analysis     These examples will help engineers, researchers, and students see the unifying structure beneath seemingly diverse physical problems. 🔁 If this helped clarify the FEM process, feel free to share it with your network. P.S. Which of the eight examples would you like to see fully worked out in symbolic or numerical form?

🔬 FEM vs. AEM: A New Frontier in Structural Simulation For decades, the Finite Element Method (FEM) has been the go-to for structural analysis. But as the challenges of our built environment become more complex, we need tools that go further. ➡️Enter the Applied Element Method (AEM), the technology at the heart of Extreme Loading® for Structures (ELS), a software developed by Applied Science International, LLC - ASI 🏗️ How Do They Compare? 🔍 FEM vs. AEM – Key Differences: 🔸Failure & Collapse: FEM – Limited, needs assumptions AEM – Native progressive collapse simulation 🔸Element Separation: FEM – Hard to simulate AEM – Natural detachment under load 🔸Debris Trajectories: FEM – Not modeled AEM – Realistic post-collapse behavior 🔸Non-linear Behavior: FEM – Requires advanced meshing AEM – Handled intrinsically 🔸Extreme Events: FEM – Manual setup AEM – Built-in support ⚙️ Why Choose ELS? With ELS, engineers, researchers, and forensic specialists can: ✅ Simulate real collapse mechanisms without artificial constraints ✅ Model blast, seismic, fire, and impact events in one platform ✅ Visualize structural damage and debris in true 3D ✅ Align simulations more closely with observed reality 📐 ELS doesn’t just analyze structures, it reveals how they behave under the most extreme conditions. In a world where understanding failure is key to preventing it, the method and the tool you choose matters. #StructuralEngineering #ProgressiveCollapse #ExtremeLoading #FEMvsAEM #SimulationTechnology #ForensicEngineering #AEM #ELS #EngineeringInnovation #CivilEngineeringTools

🚨 Big Advice Before We Dive Deep Into FEA 🚨 This is the opening message from my upcoming video in MechCADemy—but I couldn’t wait to share it. If it helps even one student or engineer improve their approach to simulation, then it’s already worth it. Also, I welcome any comments or suggestions to improve this list. FEA success starts before you hit "solve." It starts with how you think, how you define the problem, and how honest you are with your process. Here’s a preview of the advice I’ll be sharing: (Note that we will have a deep-dive into the items shown below!) 🧠 Learn Critical Thinking Ask better questions, challenge assumptions, and think like an engineer—not just a software user. ⚠️ Understand Cognitive Biases Biases like confirmation, anchoring, and the halo effect can mislead your judgment. Recognize them early. 📘 Know the Theory (but just Enough!) You don’t need every derivation, but you do need to understand the "why" behind what you’re modeling. 🛠 Explore the Tools Know your software’s strengths and limitations. Your efficiency depends on it. 📐 Review Statics & Free Body Diagrams Can you isolate a part and show all forces/moments? It’s a fundamental skill—don’t skip it. 🧱 Understand Mechanics of Materials FEA gives you numbers. You give them meaning. Know deformation, stress, strain, and failure behavior/criteria. 🚫 Simulation ≠ Judgment Software doesn’t replace engineering insight—it supports it. Never forget that. 👥 If possible, find a Mentor A mentor will help you learn faster, make fewer mistakes, and grow deeper understanding. 🎯 Focus on Problem Definition A well-defined problem beats fancy software tricks every time. 🧩 Keep It Simple (and Smart) Simplicity and accuracy aren’t opposites. Good modeling balances both. 🧭 Model with Integrity What you simulate could impact real lives. Treat it with the seriousness it deserves. 🎥 This is all part of my mission at MechCADemy—to offer free, high-quality engineering content for students and professionals alike. 💡 If you find this valuable, please support the channel! Your encouragement gives me the energy to keep producing more content that helps our engineering community. 👉 https://lnkd.in/gUyP9q9V #MechCADemy #FEA #MechanicalEngineering #Simulation #EngineeringEducation #CAD #CAM #FiniteElementAnalysis #EngineeringStudents #EngineeringMentorship #CriticalThinking #IntegrityInEngineering #EdTech #FreeEducation #DigitalLearning #ProblemSolving #SimulationEngineering #CivilEngineering #StructuralEngineering #CAE #CognetiveBiases #Statics #MechanicsofMaterials #FreeBodyDiagram

Modeling Extreme Flooding Scenarios Virtually Engineering vehicles to safely traverse flooded roads requires accounting for immensely complex hydrodynamic forces and multiphase interactions as waters rise chaotically into engine bays. Advanced simulation develops high-resolution digital twins replicating vehicles battling rapid flows, unpredictable spray, and partial submersion across thousands of topology-varying flood scenarios based on real-world datasets. By exploring aquatic extremes digitally first, automakers mitigate reliance on risky physical prototypes when designing beyond boundaries considered implausible just years ago. Simulation reshapes possibility frontiers. #simulation #multiphysics #cfd #fea #dynamics #fluiddynamics #automotive #aerodynamics #hydrodynamics #Durability #testing #CAE #PLM #systemsmodeling #electromagnetics #heattransfer #AI #optimization #design #NVH #manufacturing #innovation #engineering