Computational Fluid Dynamics (CFD) Applications

Urban Heat: Measure or Model? As cities face rising temperatures, understanding urban heat distribution becomes essential for sustainable planning and climate resilience. But how do we best capture this complex phenomenon? 🔴 Direct Thermal Sensing Thermal sensors mounted on aircraft or satellites provide high-resolution images of surface temperatures. This method is: ✅ Proven and reliable ✅ Fast in covering large areas ✅ Based on actual measurements at a specific point in time However, it comes with significant limitations: ⚠️ It only captures surface radiation – not the temperature people actually feel ⚠️ It’s a snapshot – no insight into daily or seasonal dynamics ⚠️ It’s relatively expensive and logistically demanding 🔵 Urban Climate Simulation (CFD-based) Computational Fluid Dynamics (CFD) models allow us to simulate the urban microclimate in 3D. With the right input data (e.g. DTM/DSM, land cover), we can: ✅ Simulate any time of day, season, or weather condition ✅ Calculate perceived temperature at pedestrian level – where it matters most ✅ Evaluate the impact of mitigation strategies like greening, shading, or urban design changes ✅ Scale analyses across multiple scenarios – quickly and cost-effectively Yes, it’s a simulation – but one that enables evidence-based decision-making. It shifts the focus from reactive measurement to proactive planning. 🧭 Conclusion If the goal is to understand how people experience heat in cities – and how we can improve it – simulation is not just an alternative, it’s the better tool. Flexible, scalable, and human-centered. Are you already using urban climate models in your projects? Or still relying on thermal imagery? Let’s discuss! 👇 💡 Comment | Like | Share 👉 Follow me (Dr. Uwe Bacher) for more insights on exciting topics from the world of geospatial #UrbanClimate #CFD #ThermalSimulation #SmartCities #ClimateAdaptation #UrbanPlanning #HeatMitigation #DigitalTwin #SustainableCities

🌟 Transforming Urban Wind Modeling with Physics-Informed AI 🌟 Traditional methods for predicting urban wind fields, like CFD simulations, are powerful but often time-consuming and computationally expensive. But what if we could predict wind dynamics faster without sacrificing accuracy? That's exactly what the authors Xuqiang Shao, Zhijian Liu, Siqi Zhang, Zijia Zhao, and Chenxing Hu achieved in their paper: "PIGNN-CFD: A Physics-Informed Graph Neural Network for Rapid Predicting Urban Wind Field Defined on Unstructured Mesh." 🔑 Key Highlights: 1️⃣ Faster Wind Field Prediction: The PIGNN-CFD model delivers predictions 1–2 orders of magnitude faster than traditional CFD simulations. 2️⃣ Physics-Informed Learning: By incorporating physical laws (RANS equations) directly into the training process, the model ensures accurate and reliable predictions. 3️⃣ Scalability: It generalizes well to large-scale urban environments, making it a promising tool for urban planning, air quality studies, and climate resilience efforts. 4️⃣ Real-World Validation:u The model leverages data from wind tunnel experiments (AIJ) and validated CFD simulations created using OpenFOAM. 💡 Why This Matters: Accurately modeling urban wind fields is critical for addressing environmental challenges like heat islands, air pollution, and pedestrian comfort. By integrating advanced graph neural networks and CFD-based data, this study paves the way for scalable and efficient urban climate solutions. 📈 Implications: The PIGNN-CFD framework offers a glimpse into the future of physics-informed machine learning, where simulation time is slashed, enabling rapid decision-making for urban designers, environmental scientists, and engineers. 💬 What are your thoughts on the application of machine learning in computational fluid dynamics? Let's discuss! #PhysicsInformedAI #CFD #MachineLearning #UrbanWindModeling #GraphNeuralNetworks #AIforClimateSolutions Link for the Paper https://lnkd.in/dD7Tbihp

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

Cavitation is the formation and collapse of vapor-filled cavities or bubbles in a liquid, occurring when the local pressure falls below the liquid's vapor pressure. This phenomenon is common in hydraulic machinery, such as pumps, propellers, and turbines. Cavitation starts when the liquid is subjected to rapid changes in pressure, causing vapor bubbles to form in low-pressure regions. As these bubbles move to higher-pressure areas, they collapse violently. The collapse generates intense shock waves, leading to noise, vibrations, and potential damage to the equipment. Over time, repeated cavitation can cause pitting and erosion of metal surfaces, significantly reducing the lifespan and efficiency of the machinery. In marine environments, cavitation can reduce the performance of propellers, leading to decreased vessel speed and increased fuel consumption. In pumps and turbines, it can cause significant operational disruptions and maintenance issues. Preventing cavitation involves careful design and operation, including controlling the fluid flow, pressure levels, and selecting appropriate materials resistant to cavitation damage. Advanced techniques like computational fluid dynamics (CFD) simulations are often employed to predict and mitigate cavitation effects in engineering systems.

Quadrillion Calculations: How the Super Heavy Exhaust Was Simulated Scientists at Georgia Tech did the impossible — they simulated the turbulent exhaust plumes from 33 rocket engines at the same time. This is the largest fluid dynamics simulation in history, exceeding one quadrillion degrees of freedom. To understand the scale: a quadrillion is a number with 15 zeros. That’s how many independent variables the El Capitan supercomputer computed simultaneously to model how the scorching exhaust streams interact. Why is this even needed? Modern rockets like SpaceX’s Super Heavy use dozens of smaller engines instead of a few large ones. They are easier to manufacture, have redundancy, and are simpler to transport. But when all of them fire together, their exhaust plumes at Mach 10 create a hellish mixture. Hot gases can reflect back toward the rocket’s base and simply destroy it. Testing this in a wind tunnel is impossible — the conditions are too extreme. Simulation is the only option. But traditional methods would require weeks of calculations even for a simplified model. The team came up with a trick. Instead of traditional shock-wave capturing, they developed the IGR method — Information Geometric Regularization. It sounds complicated, but the essence is simple: they reformulated how the computer processes shock waves. The result was an 80× speed-up and a 25× reduction in memory usage. El Capitan is not just a powerful computer. It has a unique architecture with unified memory using AMD MI300A chips. The CPU and GPU work with the same physical memory. The team used all 11,136 nodes of the machine — more than 44,500 AMD accelerators. A simulation that once took weeks finished in hours. Energy consumption was reduced by a factor of five. But the most interesting part is the applications. The technology works not only for rockets. Aircraft noise prediction, biomedical hydrodynamics, any high-speed flows — anywhere turbulence needs to be modeled without artificial viscosity. The work has been nominated for the Gordon Bell Prize — the highest award in supercomputing. The winner will be announced on November 20 in St. Louis. Ironically, El Capitan was created for nuclear weapons simulation. And its first public use — to help SpaceX avoid burning its own rocket with its own exhaust. https://lnkd.in/g-gxB4hR

As the hydrogen economy gains momentum, prioritizing safety is crucial. Computational Fluid Dynamics (CFD) plays a key role in minimizing the risks associated with unwanted flame propagation and explosions. In a recent CONVERGE simulation, a six-meter-long tube filled with a hydrogen and air mixture, with a 35% H2 volume fraction, underwent a fascinating transformation from deflagration to detonation. The simulation showcases intricate structures like shocks and Kelvin-Helmholtz type formations, evident in the temperature contours (middle view). Impressive work by Shuaishuai Liu, shedding light on the critical application of CFD in enhancing safety within the hydrogen industry. (Simulations are based on experiments from the paper: "Experimental study on combustion and explosion characteristics of hydrogen-air premixed gas in rectangular channels with large aspect ratio," Han et al., International Journal of Hydrogen Energy, 57, 2024.) #cfd #convergecfd #hydrogen

DNV - Engine-room fire simulations. This document provides further information in support of the conclusion in document SDC 12/11 that SOLAS regulations need not be revised in relation to requirements for escape from engine-rooms. It contains the report of computational fluid dynamics (CFD) simulations of fire scenarios in the engine-room of a container ship, aimed at assessing the risks associated with elevated access to emergency escape trunks and the potential benefit of lowering the access point. It is demonstrated that there is no measurable impact of an emergency escape trunk with inclined ladder on the crew's ability to evacuate safely and swiftly during a fire.

CFD that is more than pretty pictures! From the latest AIAA Journal of Spacecraft and Rockets. Database of Computational-Fluid-Dynamics-Based Buffet-Induced Forces for Artemis I Structural Response Evaluation https://lnkd.in/e-hW8SVk Francesco Soranna, Craig L. Streett, Patrick S. Heaney, Martin K. Sekula, David J. Piatak, Oleg O. Goushcha, and James M. Ramey NASA Langley Research Center, Hampton, Virginia 23681 Time-accurate FUN3D simulations are used to estimate buffet-induced unsteady forces experienced by the Space Launch System during the Artemis I (AR01) flight. A set of FUN3D simulations was developed that employed time-accurate mesh translations to simulate the changing velocity and attitude based on the AR01 best estimated trajectory. In these simulations, referred to as accelerating-flow simulations, the freestream Mach numbe rincreased from 0.80 to 1.92. Additional time-accurate simulations were obtained at constant freestream Mach number equal to 0.95, 1.18, and 1.70, thereby simulating stationary conditions experienced by the flow in a wind tunnel. Based on favorable comparisons between simulated and flight-measured environments, the FUN3D-basedsurface pressures were used to develop a buffet forcing function (BFF) database. This BFF database was analyzed to characterize the spatial distribution and frequency content of the buffet-induced forces during transonic and supersonic portions of the AR01 flight. The region of interest is located downstream of the forward attachment (FA) hardware between the core stage and the solid rocket boosters where vortex shedding off the FA protuberance produces significant unsteadiness. The analysis reveals that, at transonic and supersonic conditions, buffet forces that are based on constant freestream Mach number data are a good approximation of those based on accelerating-flow simulations.