Advanced Simulation Tools for Flight Dynamics

Flight dynamics in Python with Archimedes! In a new series we walk through implementing 6dof flight dynamics using the subsonic F-16 benchmark. The implementation uses hierarchical, multi-fidelity modeling and the spatial mechanics primitives for rigid body dynamics. The trajectory in the gif has tabulated aerodynamics, NASA turbofan model, rate-limited control surfaces, USSA1976 atmosphere, and... constant gravity. No sensor models (yet).  It runs on a laptop at ~8000x realtime using the SUNDIALS interface for adaptive ODE solving. The whole thing is implemented in Archimedes + NumPy, so the entire model (plus controllers and filters, coming soon) is also compatible with C code generation for real-time simulation, HIL testing, and embedded deployment using CasADi's computational graphs. An RK4 step takes ~380 µs on a Cortex M7, enabling 1+ kHz hard real-time running on bare metal. Check out the tutorials and the source code on GitHub! https://lnkd.in/e_74JVU4 (tutorial series) https://lnkd.in/ecV6nHdk (source code) #Archimedes #CasADi #ControlSystems #EmbeddedSystems #Python #OpenSource #Aerospace

A Boeing 747 Aerodynamics Simulation at Re = 50,000 A very interesting CFD visualization of the Boeing 747 aerodynamics at Reynolds number 50,000. The simulation highlights complex vortex structures around the aircraft using velocity-colored Q-criterion. The simulation was performed using FluidX3D, based on the Lattice Boltzmann Method (LBM) with a D3Q19 lattice and BGK collision model. Mid-grid bounce-back boundaries were used for the geometry, while equilibrium boundaries were applied for the computational box walls. Simulation details: Grid resolution: 912 × 1824 × 456 Time steps: 100,000 Compute + render time: 6 h 34 min Hardware: Nvidia A100 40 GB GPU Flow structures were visualized using velocity-colored Q-criterion (Q = 0.00002), revealing the vortical structures forming around the aircraft. The implementation is highly memory-efficient using Esoteric-Pull streaming and FP16 memory compression, reducing memory demand to about 55 Bytes per node, roughly one-third of typical FP32 implementations. This enables extremely large simulations even on a single GPU. A great example of how modern GPU accelerated CFD and LBM methods can produce high-resolution flow simulations. Simulation Owner: Dr. Moritz Lehmann (FluidX3D YouTube channel) FLUIDX3D: https://lnkd.in/gDseuPKK FP32/FP16 mixed precision: https://lnkd.in/gtpUS7cA Esoteric-Pull: https://lnkd.in/gwPR9Cp4 #mechanical #aerospace #automotive #cfd #aerodynamics

✈️ 𝗡𝗔𝗦𝗔'𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗠𝗲𝘀𝗵𝗶𝗻𝗴 𝗳𝗼𝗿 𝗛𝗶𝗴𝗵-𝗙𝗶𝗱𝗲𝗹𝗶𝘁𝘆 𝗔𝗲𝗿𝗼𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 Advancements in 𝗪𝗮𝗹𝗹-𝗠𝗼𝗱𝗲𝗹𝗲𝗱 𝗟𝗮𝗿𝗴𝗲-𝗘𝗱𝗱𝘆 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 (𝗪𝗠𝗟𝗘𝗦) are revolutionizing aerodynamics in the aerospace industry. At NASA Ames Research Center, a 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘀𝗼𝗹𝘃𝗲𝗿-𝗺𝗲𝘀𝗵𝗲𝗿 𝗽𝗮𝗶𝗿 𝘁𝗼𝗼𝗹 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗲 𝗟𝗔𝗩𝗔 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 is addressing one of WMLES’s biggest hurdles: the 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗹𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲, 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗺𝗲𝘀𝗵𝗲𝘀 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗴𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗲𝘀. Traditional mesh generation for high-lift configurations can take weeks and a team of specialists. With 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗩𝗼𝗿𝗼𝗻𝗼𝗶 𝗴𝗿𝗶𝗱𝘀, it’s down to a couple of days—requiring just one person! These highly scalable, body-conforming grids are delivering accurate, engineering-relevant results while slashing costs and time. 🚀 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: ➡️ 𝗛𝗶𝗴𝗵 𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Simulated the flow around half of a 60-meter-wingspan aircraft, resolving 4 mm flow details. ➡️ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻: Ran on 100 AMD “Rome” nodes for two days on NASA’s Aitken supercomputer. ➡️ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Voronoi grids cut hands-on effort while matching the accuracy of custom-built meshes. ➡️ 𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀: Enhancing meshing via parallel load balancing and accelerating solvers with GPUs. ✒️ 𝗔𝘂𝘁𝗵𝗼𝗿 𝗰𝗿𝗲𝗱𝗶𝘁: Victor Sousa & Emre Sozer, NASA Ames Research Center 🎥 The 𝘃𝗶𝗱𝗲𝗼 shows "surface contours of the friction coefficient experienced by NASA High-Lift Common Research model at an angle-of-attack of 19.7 degrees, where small-turbulent fluctuations are visible. Dark regions highlight locations where flow separation is likely to occur." Enjoy! 📢 𝗖𝗙𝗗 𝗲𝘅𝗽𝗲𝗿𝘁𝘀, 𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗺𝗲𝘀𝗵 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀? 𝗦𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀 𝗯𝗲𝗹𝗼𝘄! #CFD #Simulation #Technology #WMLES #Aerodynamics #NASAResearch #NASA #HighPerformanceComputing #Automation #CAE #Engineering #SimulationExcellence #TurbulenceModeling

Most UAV simulation models make one simplifying assumption: treat the whole vehicle as a rigid body. For a fixed-wing or a standard multirotor, that's usually fine. For a morphing vehicle mid-transition, it's not. This paper from the University of Zagreb builds something different. The MetaMorpher is a UAV that physically folds its wings to switch between a spinning rotary-wing hover mode and a fixed-wing cruise. Each wing is split into eight spanwise segments, and each segment computes its own aerodynamic forces based on its own local velocity and angle of attack. The nonlinear model captures what a rigid-body approximation would average away. Aerospace Blockset is central to how this works in Simulink: 🔧 The 6-DoF rigid-body block forms the core of the flight dynamics model. All forces and moments from the 16 wing segments, thrusters, and gravity are summed and fed into this block, which returns the full vehicle state: velocities, angular rates, attitude, and position. 🔧 For each of the 16 segments, Aerospace Blockset computes the kinematic angle of attack from the local air-relative velocity vector. The model then applies a geometric correction for the wing rotation angle, with opposing signs on port and starboard to reflect the antisymmetric morphing kinematics, to get the effective aerodynamic angle of attack for that segment. 🔧 A MATLAB function block handles wing parametrization and discretization, making it straightforward to swap airfoils, adjust chord distributions, or change the number of segments without restructuring the model. Aerodynamic coefficients come from XFLR5 lookup tables, exported and embedded directly into the Simulink model for real-time interpolation during flight simulation. The model is open source and available now. The team's next step is experimental validation on a physical prototype, and honestly I can't wait to see this thing fly. Very interesting work from Anja Bosak, Dorian Eric, Ana Milas, and Stjepan Bogdan. https://lnkd.in/eaXw6U-R https://lnkd.in/eyDfDQif #MorphingWing #UAV #AerospaceBlockset #Simulink #MATLAB #FlightDynamics #Robotics #OpenSource

From Billion-Cell Solvers to Real-Time AI Surrogates. The New Architecture of CFD Engineering teams building the next generation of aircraft, vehicles, and data centers need the ability to make rapid, iterative design decisions. Waiting days for batch-processed fluid dynamics solvers completely breaks the development loop. To solve this, we are releasing the NVIDIA Omniverse Blueprint for Interactive Fluid Simulation. Check out the live blueprint: https://lnkd.in/gWZxX_ee For the CAE practitioners and enterprise architects scaling these workloads, here is the exact reference architecture to transition your CFD pipelines into real-time digital twins: 🟢 1. The Compute Engine (Blackwell & CUDA-X): We are accelerating traditional solvers by orders of magnitude. The proof is in the hardware: Cadence recently ran a 10-billion-cell large-eddy simulation (LES) of a complete aircraft on a single NVIDIA GB200-NVL72 system. It did the work of nearly 300,000 CPU cores at a 7x lower cost. 🟢 2. The AI Surrogate (PhysicsNeMo): To achieve real-time interactivity, developers are using the open-source PhysicsNeMo framework to embed governing equations (like Navier-Stokes) directly into machine learning models. Using tools like the DoMINO NIM microservice, these AI surrogates predict massive flow fields instantly. 🟢 3. The Digital Twin (OpenUSD & NVIDIA Omniverse): The unified pipeline—CAD → meshing → CFD solve → AI surrogate—is piped natively into Omniverse using OpenUSD. This gives engineers fully interactive, physically based RTX rendering of the fluid dynamics directly in their applications. You get the real-time design exploration of an AI surrogate, backed by the gold-standard accuracy of a high-fidelity solver. Incredible to see ecosystem leaders like Cadence, Siemens, Ansys, and Dassault Systèmes bringing these integrated capabilities to their customers. The interactive blueprint and reference architecture are live today. 🔗 Dive into the technical implementation here: https://lnkd.in/gc4qyj7s What is the biggest compute or data bottleneck your team faces when scaling multi-physics simulations? Let's discuss in the comments. 👇 #NVIDIA #Omniverse #CFD #DigitalTwins #OpenUSD #Blackwell #PhysicsNeMo #CAE #Engineering #DevRel