Engineering Simulation Tools Overview

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data.  2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro  -> RoboCasa produces N (varying visuals)  -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are creating tools to enable everyone in the ecosystem to scale up with us: - RoboCasa: our generative simulation framework (Yuke Zhu). It's fully open-source! Here you go: http://robocasa.ai - MimicGen: our generative action framework (Ajay Mandlekar). The code is open-source for robot arms, but we will have another version for humanoid and 5-finger hands: https://lnkd.in/gsRArQXy - We are building a state-of-the-art Apple Vision Pro -> humanoid robot "Avatar" stack. Xiaolong Wang group’s open-source libraries laid the foundation: https://lnkd.in/gUYye7yt - Watch Jensen's keynote yesterday. He cannot hide his excitement about Project GR00T and robot foundation models! https://lnkd.in/g3hZteCG Finally, GEAR lab is hiring! We want the best roboticists in the world to join us on this moon-landing mission to solve physical AGI: https://lnkd.in/gTancpNK

Have you ever wondered how the performance of an underground metro station is measured? In this simulation, we look at pedestrian density (ped/m²) as a key indicator of how well the station space accommodates the number of people moving through it. The entire ecosystem is modelled in Viswalk software, a pedestrian simulation tool based on the social force model. These advanced modelling techniques allow us not only to understand existing conditions, but also to test future scenarios. For example: 🔹 What happens if train frequency increases? 🔹 How does the station perform with higher passenger demand? 🔹 If there’s a signalling fault and passengers wait longer on the platform, is the space still safe and comfortable? 🔹 How does a sudden surge in demand during major events impact flow and safety? By exploring these questions, we can design safer, more efficient, and more resilient stations for the future of urban mobility.

🔹 New Resource: Inductor Guide 🔹 I'm excited to share my detailed and practical Inductor Guide, which covers everything from basic principles to advanced selection and simulation techniques. ✅ Topics Covered: - Inductor Fundamentals (Core, Frequency, and Phase Behavior) - DC-DC Converter Design with Inductor Selection Examples - Real-World Calculations (Time Constant, Current Ripple) - Inductor Types and Core Selection Strategy - LTSpice Simulation Results - Practical Selection Checklist for Power Supplies and Filters This guide is designed to help engineers, students, and hardware designers understand inductor behavior beyond textbook definitions. 📥 Download the PDF attached and let me know your feedback! I’d love to hear your experience with inductor selection or EMI challenges in your projects. #HardwareDesign #PowerElectronics #Inductors #PCBDesign #DCtoDCConverter #SignalIntegrity #ComponentSelection #PowerDesign #ElectronicsEngineering #EngineeringResources

Assessing Aeroacoustics of Fan Noise in CFD by ENGYS 🚗 Reducing automotive cooling fan noise is critical - with levels reaching up to 85dBA, manufacturers seek efficient CFD solutions. Automotive cooling fans are a major noise source, reaching up to 85dBA at certain frequencies. To tackle this, Johnson Electric partnered with Engys to simulate and reduce fan noise using advanced CFD techniques. The project combined two computational approaches: an unsteady RANS (uRANS) simulation to analyze tonal noise within a 12-hour CPU time and a detached eddy simulation (DES) to assess broadband noise, validated against experimental results. Simulating turbulent flows is challenging, requiring accurate modeling of both the noise source and acoustic wave propagation. Engys leveraged its Helxy software, using an acoustic analogy approach to balance accuracy and computational efficiency. A CAD model of the fan, including the anechoic chamber, was analyzed to optimize mesh, time steps, and numerical schemes. Their extrude meshing algorithm improved boundary layer resolution while maintaining smooth transitions, cutting turnaround times by 20-30%. More on tackling CFD aeroacoustic challenges here: https://lnkd.in/eJxNhuAX #CFD #Aeroacoustics #NoiseReduction #AutomotiveEngineering #Simulation

Your plan to become a BIM specialist: 1) Learn Revit Take online courses or tutorials (e.g., Udemy, LinkedIn Learning, or Autodesk’s official training). Practice creating models for different disciplines (architectural, structural, MEP). Focus on: 1- Creating walls, floors, roofs, and structural elements. 2- Adding families (parametric components like doors, windows, etc.). 3- Generating construction documents (plans, sections, schedules). 2) Learn Navisworks 1- Use Navisworks for clash detection and project coordination. 2- Learn to merge models from different disciplines and run clash tests. 3) Explore Dynamo (Optional but Recommended) Dynamo is a visual programming tool for Revit that automates repetitive tasks. Learn to create scripts for tasks like placing families, generating geometry, or extracting data. 4) Gain Knowledge of BIM Standards and Processes 1- Study BIM Level 2 standards (common in many countries). 2- Understand the COBie (Construction Operations Building Information Exchange) format for data delivery. 3- Learn about Common Data Environments (CDEs) like BIM 360 or Aconex for collaboration. 5) Build a Portfolio Create sample projects showcasing your BIM skills. Include: 1- 3D models of buildings or structures. 2- Construction documentation (plans, sections, schedules). 3- Examples of clash detection and coordination. 4- Any automation scripts (if you’ve learned Dynamo). 6) Get Certified (Optional but Helpful) Certifications can boost your credibility: 1- Autodesk Certified Professional (ACP) in Revit. 2- Certified BIM Manager (from organizations like AGC or RICS). 3- ISO 19650 Certification for BIM standards. 7) Gain Practical Experience 1- Internships: Look for internships or entry-level roles in AEC firms. 2- Freelancing: Take on small BIM modeling projects on platforms like Upwork or Fiverr. 3- Networking: Join BIM communities (e.g., LinkedIn groups, forums like Revit Forum) to connect with professionals. 8) Stay Updated 1- Follow industry trends like BIM Level 3, Digital Twins, and AI in BIM. 2- Attend webinars, conferences, and workshops on BIM. 3- Keep learning new tools and techniques.

NASA Aeroacoustic Landing Gear Simulation using SIMULIA PowerFLOW. NASA simulation of a full-scale, realistic aircraft landing gear using PowerFLOW technology on NASA’s supercomputer. The high fidelity CFD simulation helps understand the flow features and sources contributing to overall airframe noise. During landing, when the engines are operating at reduced power, noise from the airframe, including landing gear, can be equal to or greater than the engine noise. This visualization, from a collaboration between NASA and Boeing about airframe noise prediction, shows the simulated air flow field around the nose landing gear of a Boeing 777, representing the complex unsteady flow generated by the gear components. The visualization is colored by speed, from slower green to faster red air velocities. A strong vortex appears coming off the edge of the landing gear doors. Source: NASA Ames Research Center Check out: https://lnkd.in/gSBCUR58 #mechanicalengineering #mechanical #aerospace #aerodynamics #automotive #cfd #turbulence

A month ago, I shared a simulation video of the 𝐃𝐢𝐫𝐞𝐜𝐭𝐞𝐝 𝐄𝐧𝐞𝐫𝐠𝐲 𝐃𝐞𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 (𝐃𝐄𝐃) process of a titanium wire. Since then, we've added 𝐆𝐏𝐔 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 to our simulation software, significantly reducing simulation time and enabling more complex and detailed studies. The following video demonstrates the deposition process of a titanium wire (1 mm radius) on a (4 x 4) cm² substrate across 𝐟𝐨𝐮𝐫 𝐥𝐚𝐲𝐞𝐫𝐬. The wire is melted using 𝐭𝐡𝐫𝐞𝐞 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐥𝐚𝐬𝐞𝐫 𝐛𝐞𝐚𝐦𝐬, each's power is individually controlled to maximize the deposition rate. The breakage of the liquid bridge connecting the wire and substrate can be observed during the deposition of the last track in the fourth layer. We employ a 𝐫𝐚𝐲 𝐭𝐫𝐚𝐜𝐢𝐧𝐠 algorithm to model the laser-material interaction, where the total laser power is distributed among numerous rays. The laser energy absorbed by the material surface is computed based on ray intersections with the material surface, considering surface temperature, angle of incidence, and polarization. In the video, the upper section displays the temperature field, while the lower section shows the number of ray intersections with the material surface throughout the simulation. Simulated on a Ryzen 7950x3D and an RTX4070. The video is rendered using Blender. Get in touch with us at blank-simulations if you see potential application scenarios. #SPH #multiphysics #raytracing #additivemanufacturing 𝐌𝐞𝐭𝐡𝐨𝐝: - Smoothed Particle Hydrodynamics (SPH) - MPI-OpenMP parallelization - GPU-acceleration - Dynamic workload balancing - Adaptive particle refinement 𝐏𝐡𝐲𝐬𝐢𝐜𝐬: - Ray tracing to model laser-material interaction - Temperature-dependent material properties - Latent heat of fusion and crystallization - Evaporation and recoil pressure - Surface tension and wetting

🌊 𝗡𝗲𝘄 𝗙𝗹𝗼𝗼𝗱 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗔𝗿𝗰𝗚𝗜𝗦 𝗣𝗿𝗼 𝟯.𝟱 🌍 Flood simulation in ArcGIS is crucial for risk assessment, disaster preparedness, and urban planning. It enables geospatial professionals to model flood scenarios based on real-world data, helping decision-makers understand potential impacts on infrastructure, communities, and ecosystems. With advanced tools in ArcGIS Pro 3.5, simulations can incorporate dynamic rainfall, terrain infiltration, terrain roughness and more to refine predictions and improve mitigation strategies. This enhances emergency response, reduces damage costs, and supports sustainable development. 🌎 And this tool just got great upgrades! With ArcGIS Pro 3.5, creating flood simulation scenarios is more intuitive, dynamic, faster and more precise. 🚀 My personal highlights: 🔹𝗦𝘂𝗿𝗳𝗮𝗰𝗲 𝗥𝗼𝘂𝗴𝗵𝗻𝗲𝘀𝘀 𝗥𝗮𝘀𝘁𝗲𝗿: Now it’s possible to define the roughness of the surface which influences water flow! 🔹𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 “𝗪𝗮𝘁𝗲𝗿 𝗦𝗽𝗲𝗲𝗱”: Water Speed now can be visualized in the Symbology pane! 🔹𝗜𝗻𝘀𝗲𝗿𝘁 “𝗦𝗶𝗻𝗸 𝗔𝗿𝗲𝗮𝘀”: Water Speed now can be visualized in the Symbology pane! 🔹𝗣𝗹𝗮𝘆𝗯𝗮𝗰𝗸 𝗥𝗮𝘁𝗲: Now you can define the playback rate in different fps. Flood simulation in ArcGIS is a powerful tool with diverse applications across industries. Here are some key use cases: 🌍 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 🔸 Predict flood-prone areas and develop evacuation plans for communities. 🔸 Optimize placement of rescue resources and improve response coordination. 🔸 ... 🏗️ 𝗨𝗿𝗯𝗮𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 🔸 Design flood-resistant transport networks and drainage systems. 🔸 Identify vulnerable buildings and assets to strengthen resilience. 🔸 ... 🌿 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗜𝗺𝗽𝗮𝗰𝘁 & 𝗖𝗼𝗻𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 🔸 Assess the effects of flooding on wetlands, rivers, and ecosystems. 🔸 Model sediment and pollutant transport to ensure water quality protection. 🔸  ... 🛡️ 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 & 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 🔸 Improve flood risk predictions for property insurance pricing. 🔸 Enhance data-driven decision-making for risk mitigation investments. 🔸 ... With ArcGIS Pro 3.5, flood simulation becomes even more precise and actionable, empowering industries to mitigate risks and make informed decisions. See the technical paper for more information ➡️ https://lnkd.in/d3u37-Ey 🌊💡 🤝♻️ Let's spark a conversation! How are you leveraging flood simulation tools in ArcGIS? Let’s connect and exchange ideas! Drop your insights below 👇 💡 🌟 #Esri #GIS #DigitalElevationModels #SpatialAnalysis #ArcGIS #remotesensing #flood #floodmodelling #rainfall #climatechange #FloodManagement #DisasterResponse #UrbanPlanning #Sustainability #EsriDeutschland #mapping #ArcGISPro #esrivoices🔍 🚀 🌱

🔌 Grid operators are implementing various strategies to manage the declining inertia caused by the increased penetration of variable generation (VG) resources, such as wind and solar. These strategies fall into three main categories: maintaining inertia, providing more response time, and enhancing fast frequency response. To maintain inertia, operators can ensure that a mix of synchronous generators is online to exceed critical inertia levels. Additionally, synchronous renewable energy sources and synchronous condensers can be deployed to provide inertia. To provide more response time, operators can reduce contingency sizes and adjust underfrequency load shedding (UFLS) settings. Finally, enhancing fast frequency response involves leveraging load resources, extracting wind kinetic energy, and dispatching inverter-based resources to improve the grid's ability to respond to frequency changes. 🍃 Extracted wind kinetic energy refers to the capability of wind turbines to provide fast frequency response (FFR) by utilising the kinetic energy stored in their rotating blades. This approach can be particularly effective in addressing the challenges posed by declining inertia in power systems with high wind penetration. By extracting kinetic energy, wind turbines can respond rapidly to frequency deviations, thereby helping to stabilise the grid. This method can be used in conjunction with other resources to enhance overall system reliability and maintain frequency within acceptable limits. 💡 High deployment of variable generation (VG) resources can be effectively managed by combining extracted kinetic energy from wind turbines and increasing output from curtailed wind plants. The figure below illustrates that when these two strategies are combined, they significantly mitigate frequency decline. The simulation shows that relying solely on extracted kinetic energy results in frequency falling below UFLS (underfrequency load shedding), while using only FFR barely avoids UFLS. However, when both methods are applied together, the frequency decline is minimal, demonstrating that these approaches can serve as viable alternatives to traditional inertia and primary frequency response from conventional generators. #gridmodernization #stability #gridforming #powerelectronics #renewables #cleanenergy #solidstate

A critical challenge in modern grid stability is that inverter-based resources (IBRs) are often “black boxes” to utilities and system operators. Inverter manufacturers and plant developers understandably hesitate to disclose proprietary control strategies, leaving operators with limited visibility into internal dynamics. The problem is further compounded by the fact that IBRs can switch among multiple control modes, which are typically unknown to operators yet can exhibit dramatically different dynamic behaviors. In the final days of 2025, we were excited to learn that our paper on black-box IBR modeling was accepted by IEEE Transactions on Smart Grid. In this work, we develop a comprehensive data-driven framework that uses only terminal measurements to discover unknown control modes and learn continuous-time models that accurately capture IBR dynamics under each mode. By leveraging physics-inspired deep learning, the proposed approach addresses four major challenges in a unified way: 🚀 High-Order Nonlinear Representation Using only terminal measurements, the framework provides a general learning approach for characterizing arbitrary high-order nonlinear dynamics of IBRs. It is not tied to any specific control paradigm and can cover anything from power/voltage/current control loops to virtual synchronous machines (VSMs) and phase-locked loops (PLLs). 🚀 Continuous-Time Modeling Unlike most data-driven methods built on discrete-time models (e.g., RNNs, LSTMs, Transformers), our approach learns continuous-time state-space models (differential-algebraic equations). This enables seamless integration of the learned IBR models into standard power-system time-domain simulations with arbitrary numerical integration schemes and step sizes. 🚀 Discovery of Unknown Control Modes A physics-inspired deep unsupervised learning mechanism automatically identifies distinct control modes from historical disturbance data and learns separate state-space models that represent the dynamics associated with each mode. 🚀 Robustness to Noise and Uncertainty Inspired by Kalman filtering, the learning architecture explicitly accounts for system uncertainties and measurement noise, both of which are ubiquitous in real-world grid systems and data. It ensures the method’s robust performance in practical settings. The examples in the paper demonstrate how the proposed framework can learn accurate time-domain models of fully black-box IBRs and deliver highly accurate long-horizon predictions of their responses to grid disturbances, e.g., subsynchronous oscillations caused by PLL interactions in weak grids. See details here: https://lnkd.in/eFd5CU4e #PowerSystem #SmartGrid #InverterBasedResources #RenewableEnergy #PowerElectronics #Control #PowerSystemStability #PowerSystemModeling #PowerSystemSimulation #SystemIdentification #DataDriven #MachineLearning #DeepLearning #ArtificialIntelligence #PhysicsInformed #IEEETransactionsOnSmartGrid