Computational Aerodynamics Innovations

✈️ 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

What if you could reduce weeks of simulation time to just seconds? 🚗💨 A 10% improvement in aerodynamic performance can boost EV range by ~5% — and that’s exactly why AI in CFD is gaining serious traction in automotive design. Together with ENGYS, NAVASTO trained a Graph Neural Network (GNN) model on just 110 simulations of an AeroSUV. The result? ⚡ Near-instant drag and lift predictions 📊 R² = 0.912+ correlation with CFD data 🧠 Real-time design iteration without waiting for simulation queues What used to take 2 weeks and 117 simulations can now be predicted with seconds of inference time — using a single GPU. “AI won’t replace traditional CFD — but it amplifies it.” → Faster iterations → Better collaboration between aero and design teams → More accessible insights across departments 🔍 Full study: https://lnkd.in/eTqeWWsw

What if you could estimate the aerodynamic drag of a cyclist directly from a single image? This may sound like yet another inflated AI promise, but the pipeline outlined below only partially relies on AI, with most of the heavy lifting being done by numerical analysis. Let me elaborate. The field of 3D human reconstruction has seen a spectacular evolution in recent years. Methods such as ECON, 4DHumans and Meta’s SAM 3D Body fuse semantic segmentation (finding the human’s silhouette) and keypoint segmentation (resolving the skeletal structure) with parameterized 3D representations of digital humans (e.g. SMPL or MHR). By fitting these physics-informed priors to 2D observations, the otherwise ill-posed depth ambiguity of single-shot images is elegantly addressed. In the demo below, SAM 3D Body is used to recover a cyclist’s 3D human mesh, which is then imported into a CFD solver based on the Lattice Boltzmann Method (LBM). This class of solvers approximates fluid flow through the streaming and collision of particle distributions and forms a powerful alternative to conventional Navier-Stokes solvers. Its local, explicit formulation enables massive GPU parallelization, reducing computation times by ~2 orders of magnitude. Additionally, it avoids the need for conformal meshing, as the reconstructed 3D geometry can be directly immersed in the LBM solver via a simple voxelization step. Using an entry-level GPU, the full pipeline takes about 10 minutes from image to CdA: ~20 seconds for 3D reconstruction and voxelization, ~2 minutes for transient wake development, and ~8 minutes for averaging aerodynamic drag over 5 convective time scales. On a higher-end GPU (e.g. NVIDIA RTX 5090), this could be reduced to under 2 minutes. Further considerations: • While SAM 3D is good at reconstructing 3D objects, it lacks the physics-based foundation of 3D human recovery, which is why I use CAD data for objects. • SAM 3D Body recovers unclothed humans; if 3D reconstruction of clothing is desired, an approach like ECON can be used. • Meta’s MHR human model is parameterized by 321 parameters: 72 control facial expressions (irrelevant for CdA), 113 describe body shape and skeleton dimensions (fixed for a given athlete), and 136 control dynamic pose. With offline calibration, online reconstruction can be tailored to dynamic pose estimation only. • As a cyclist is a closed kinematic chain, all valid cycling poses live on a low-dimensional manifold within the 136-dimensional pose space. At ~2 minutes of GPU time per CdA evaluation, this opens the door to surrogate modeling and real-time applications. • Using multiple images from different viewpoints (e.g. front, left & right) can further reduce 3D reconstruction error, as this gives more data to fit the MHR parameters. A reduced-order model, a camera as a sensor, an identification framework enhanced by AI, and (the prospect of) real-time actionable insights – now thát is what I call a true Digital Twin 😊.

A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Field Apurba Sarker, Reza T. Batley, Darshan Sarojini, and Sourav Saha https://lnkd.in/dmPRwHSX This work addresses the challenge of building fast and accurate surrogate models for aerodynamic simulations, particularly when high-fidelity data is scarce and computational resources are limited. The core idea is to leverage a kernel-based neural surrogate called KHRONOS within a multi-fidelity framework. KHRONOS combines sparse high-fidelity data with readily available low-fidelity data to predict aerodynamic fields. Technically, KHRONOS distinguishes itself by its foundation in variational principles, interpolation theory, and tensor decomposition. This allows for aggressive pruning of the network, leading to a significantly smaller number of trainable parameters compared to dense neural networks like MLPs. The authors compare KHRONOS against MLPs, GNNs, and PINNs on the AirfRANS dataset, using NeuralFoil to generate low-fidelity data. They vary the amount of high-fidelity data (0%, 10%, 30%) and the complexity of the airfoil geometry parameterization. The key metric is the prediction of the surface pressure coefficient distribution. The results demonstrate that while all models eventually converge to similar accuracy levels, KHRONOS shines when resources are constrained. It achieves comparable accuracy with orders of magnitude fewer parameters and faster training/inference times. This work highlights the importance of architectures designed for resource efficiency in scientific applications. In many scientific domains, obtaining high-fidelity data is expensive and time-consuming. KHRONOS, by leveraging kernel methods and tensor decompositions, offers a promising path towards building accurate and efficient surrogate models in such scenarios. The ability to drastically reduce the computational cost of surrogate modeling can accelerate design optimization and uncertainty quantification workflows in aerodynamics and potentially other physics-based simulations.

High-Fidelity External Aerodynamics Simulation of the DrivAer Model using Large Eddy Simulation (LES) on GPUs with Simcenter STAR-CCM+. The DrivAer model, developed by TU Munich, has become a global benchmark for advancing automotive aerodynamic research and developing robust CAE methodologies. Large Eddy Simulation (LES) provides high-fidelity resolution of large-scale turbulent structures, offering significantly more detailed insight into complex flow fields compared to traditional RANS models. Shown here is the DrivAer hatchback configuration simulated according to the setup of the 3rd Automotive CFD Prediction Workshop (AutoCFD 3). Mesh Size: 130 million trimmed cells Physical Time Simulated: 4 seconds Time Steps: 16,000 This high-fidelity LES was executed on 4 NVIDIA A100 GPUs, achieving a runtime of approximately 20 hours. Scaling the case to 8 A100 GPUs could reduce the simulation time to around 12 hours, a remarkable performance boost. To match this GPU performance using CPUs, one would require approximately 2,000 Intel Xeon Gold cores, underlining the efficiency and scalability of GPU-accelerated CFD. Read the full article: https://lnkd.in/gkyFbHDG Learn more about the DrivAer model (TUM): https://lnkd.in/gD4NjJsC Article author: Liam McManus [SIEMENS Blog] #mechanical #aerospace #automotive #cfd #aerodynamics #cfd

Two researchers at San Diego State University have cracked a major puzzle in hypersonic flight—how fuel droplets and gas particles behave inside the violent chaos of detonation waves. Their new mathematical model, developed with Stanford and backed by the U.S. Air Force, delivers unprecedented insight into the particle dynamics inside scramjets and rocket engines, opening the door to faster, more stable, and more precise hypersonic aircraft. This breakthrough pushes the frontier of military aerospace tech into territory once considered pure guesswork. https://lnkd.in/gsvQB3cZ FuturistSpeaker.com