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  1. Are there any #CFD (Computational Fluid Dynamics) types on here? I'm involved in a project to build a flowbench and it's got me wondering about the limited CFD I did at Uni 25+ years ago.

    Particularly interested in software recommendations; I have Fusion 360 personal edition for building models (I tried FreeCAD and really did not get on with it) if that helps. Windows, because I need that fur data acquisition software - no Linux preaching please, I spent 11 years as a Linux sysadmin ;)

  2. Are there any #CFD (Computational Fluid Dynamics) types on here? I'm involved in a project to build a flowbench and it's got me wondering about the limited CFD I did at Uni 25+ years ago.

    Particularly interested in software recommendations; I have Fusion 360 personal edition for building models (I tried FreeCAD and really did not get on with it) if that helps. Windows, because I need that fur data acquisition software - no Linux preaching please, I spent 11 years as a Linux sysadmin ;)

  3. I have been porting kernels from OpenFOAM for CFD to FPGAs using HLS as part of an internal research and engineering project at my company.

    Spent 3 weeks debugging. The fix was awfully trivial.
    No LLM could help me. I know my job is safe.

    Also, Microchip have bad documentation. They can do better.

  4. I have been porting kernels from OpenFOAM for CFD to FPGAs using HLS as part of an internal research and engineering project at my company.

    Spent 3 weeks debugging. The fix was awfully trivial.
    No LLM could help me. I know my job is safe.

    Also, Microchip have bad documentation. They can do better.

    #cfd #openfoam #hpc #fpga #SoftwareDevelopment #SoftwareEngineering #hardware #computationalphysics

  5. Free online #CFD course: #OpenFOAM – Setting Up and Performing Fluid Dynamics Simulations on #HPC Systems

    📅 13 July 2026, 09:00–13:00 CEST

    Beginner level, hands-on on ASC’s HPC via #JupyterHub, no local install needed.

    Register: events.asc.ac.at/event/314/

    @[email protected] @[email protected]

  6. Free online #CFD course: #OpenFOAM – Setting Up and Performing Fluid Dynamics Simulations on #HPC Systems

    📅 13 July 2026, 09:00–13:00 CEST

    Beginner level, hands-on on ASC’s HPC via #JupyterHub, no local install needed.

    Register: events.asc.ac.at/event/314/

    @[email protected] @[email protected]

  7. Пишем CFD solver для симуляции потока воздуха (часть 1)

    Недавно передо мной встала задача расчетной гидро-газо динамики, надо было посчитать сопротивление воздуха, форму огибающего потока, все то, что требовалось, чтобы посчитать траекторию летящего тела. В этой статье я подробно написал и объяснил, как создать CFD solver.

    habr.com/ru/articles/1050440/

    #симуляция #python #python3 #c++ #поток #воздух #сопротивление_воздуха #cfd

  8. FIFA World Cup ball comparison
    Trionda (2026) vs Al Rihla (2022)

    This CFD comparison is showing two different FIFA World Cup ball designs spinning at the same rate (600 rpm) in the same airflow (30 m/s).

    The colours and wake structure reveal how the surface geometry affects the air around the ball.

    * Trionda (2026): Much more intricate surface pattern with many grooves, ridges and dimples.

    * Al Rihla (2022): Simpler panel layout with fewer aerodynamic features.

    players might notice the Trionda:

    * Grips the air more strongly
    * Curves more readily when spun
    * Feel slightly more stable aerodynamically

    The 2026 Trionda appears designed to give the airflow more to “hold onto”, creating stronger vortex structures and slightly larger aerodynamic forces than the smoother 2022 Al Rihla, which may translate into more pronounced curl and control for skilled players.

    #FIFAWorldCup #WorldCup2026 #WorldCupBall #Trionda #AlRihla #FootballScience #SoccerScience #SportsEngineering #Aerodynamics #CFD #ComputationalFluidDynamics #FluidDynamics #SportsTech #FootballTechnology #BallDesign #FootballEngineering #SportsInnovation #EngineeringVisualization #FlowSimulation #WakeStructure #VortexDynamics #AerodynamicForces #FootballAnalysis #SoccerBall #SportsResearch #EngineeringInsights #DataVisualization #FootballPerformance #SportsPhysics #FootballAerodynamics

  9. This Thursday’s office hours I will talk about multi-region convergence criteria and milti-region simulations.

    🗓 June 11
    🕒 15:00 Paris (CEST)
    💻 teams.microsoft.com/meet/32226

    Also happy to take general OpenFOAM/modelling questions

    🌍 14:00 London | 13:00 UTC | 09:00 NY | 06:00 SF | 21:00 SG | 22:00 JP

    #OpenFOAM #CFD

  10. Porting some OpenFOAM hotspots to FPGA using HLS. Starting with the dotInterpolate function.
    My first such project. Also, a first for my company.

    Will keep you all posted.

  11. Porting some OpenFOAM hotspots to FPGA using HLS. Starting with the dotInterpolate function.
    My first such project. Also, a first for my company.

    Will keep you all posted.

    #hpc #fpga #hls #software #SoftwareEngineering #cfd #openfoam #hardware #accelerators

  12. On Dolphin Turbulence

    Dolphins are such fast and agile swimmers that, naturally, scientists have long wanted to understand how they swim so well. A recent study draws on numerical simulation to analyze the flow a dolphin creates when flapping its tail.

    The resulting flow is highly turbulent–researchers were only able to simulate up to a fraction of a dolphin’s actual Reynolds number–with both large-scale vortices and a cascade of smaller ones. The largest vortices, shown here in white, form on the upper and lower surface of the dolphin’s tail, then slide off the tail in a vortex ring. It’s these vortex rings, the researchers found, that provide the bulk of a dolphin’s thrust.

    The smaller-scale vortices, in contrast, get formed by the large vortices, and they make little to no contribution to the dolphin’s propulsion. Interestingly, these results suggest that we might be able to describe the propulsion of dolphins and other highly turbulent swimmers by focusing only on the largest scales in the flow. (Video, image, and research credit: Y. Motoori et al.; via Ars Technica)

    Animation of the simulated flow from a swimming dolphin. #biology #CFD #computationalFluidDynamics #dolphins #fluidDynamics #numericalSimulation #physics #propulsion #science #swimming #turbulence
  13. On Dolphin Turbulence

    Dolphins are such fast and agile swimmers that, naturally, scientists have long wanted to understand how they swim so well. A recent study draws on numerical simulation to analyze the flow a dolphin creates when flapping its tail.

    The resulting flow is highly turbulent–researchers were only able to simulate up to a fraction of a dolphin’s actual Reynolds number–with both large-scale vortices and a cascade of smaller ones. The largest vortices, shown here in white, form on the upper and lower surface of the dolphin’s tail, then slide off the tail in a vortex ring. It’s these vortex rings, the researchers found, that provide the bulk of a dolphin’s thrust.

    The smaller-scale vortices, in contrast, get formed by the large vortices, and they make little to no contribution to the dolphin’s propulsion. Interestingly, these results suggest that we might be able to describe the propulsion of dolphins and other highly turbulent swimmers by focusing only on the largest scales in the flow. (Video, image, and research credit: Y. Motoori et al.; via Ars Technica)

    Animation of the simulated flow from a swimming dolphin. #biology #CFD #computationalFluidDynamics #dolphins #fluidDynamics #numericalSimulation #physics #propulsion #science #swimming #turbulence
  14. AI-Based Weather Forecasting Has Blind Spots

    Traditional weather forecasting models are physics-based and rely on supercomputers. Practically speaking, this means that they start from the basic governing equations (like the Navier-Stokes equations) and use approximations to model aspects of the problem in order to make the physics solvable, given constraints on time, computational power, spatial resolution, and so on.

    So-called AI models approach the problem differently, training a model on past weather conditions in order to predict future weather. In some respects, this approach is very successful; AI-based models require less computational infrastructure to run and, in recent years, have greatly improved their predictions of everyday weather.

    However, these AI models do poorly when predicting extreme weather events, because their training data contain relatively few examples of these events. They show limited ability to extrapolate their predictions to more extreme events. But these events–like the unprecedented 2021 heatwave in the Pacific Northwest or many of the Category 5 hurricanes we’ve seen in the last decade–are happening increasingly often due to climate change. Those events will keep happening, more frequently, as warming continues. Physics-based models can predict and forecast these events in ways that AI-based models fail to because they are limited by their trained experiences.

    Researchers are working to find ways to better equip AI-based models with more physical sense, but, as these models proliferate, it’s important for their users (and those of us using their forecasts) to know what their current weaknesses are. (Image credit: B. McGowan; research credit: Y. Sun et al.; see also S. Nath and T. Palmer; via Gizmodo)

    #CFD #computationalFluidDynamics #fluidDynamics #hurricane #hurricanes #meteorology #physics #science #weather
  15. AI-Based Weather Forecasting Has Blind Spots

    Traditional weather forecasting models are physics-based and rely on supercomputers. Practically speaking, this means that they start from the basic governing equations (like the Navier-Stokes equations) and use approximations to model aspects of the problem in order to make the physics solvable, given constraints on time, computational power, spatial resolution, and so on.

    So-called AI models approach the problem differently, training a model on past weather conditions in order to predict future weather. In some respects, this approach is very successful; AI-based models require less computational infrastructure to run and, in recent years, have greatly improved their predictions of everyday weather.

    However, these AI models do poorly when predicting extreme weather events, because their training data contain relatively few examples of these events. They show limited ability to extrapolate their predictions to more extreme events. But these events–like the unprecedented 2021 heatwave in the Pacific Northwest or many of the Category 5 hurricanes we’ve seen in the last decade–are happening increasingly often due to climate change. Those events will keep happening, more frequently, as warming continues. Physics-based models can predict and forecast these events in ways that AI-based models fail to because they are limited by their trained experiences.

    Researchers are working to find ways to better equip AI-based models with more physical sense, but, as these models proliferate, it’s important for their users (and those of us using their forecasts) to know what their current weaknesses are. (Image credit: B. McGowan; research credit: Y. Sun et al.; see also S. Nath and T. Palmer; via Gizmodo)

    #CFD #computationalFluidDynamics #fluidDynamics #hurricane #hurricanes #meteorology #physics #science #weather
  16. 10-minute Video: Top ten tips for understanding post-exertional malaise by Kate Herbert, Nurse Educator at Emerge Australia:

    vimeo.com/1191768719/03bf0f4425

    #PEM #MEcfs #PwME #CFD #LongCovid @mecfs @longcovid

  17. 10-minute Video: Top ten tips for understanding post-exertional malaise by Kate Herbert, Nurse Educator at Emerge Australia:

    vimeo.com/1191768719/03bf0f4425

    #PEM #MEcfs #PwME #CFD #LongCovid @mecfs @longcovid

  18. Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

    #biology #CFD #computationalFluidDynamics #fluidDynamics #numericalSimulation #physics #pollen #science #trees
  19. Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

    #biology #CFD #computationalFluidDynamics #fluidDynamics #numericalSimulation #physics #pollen #science #trees
  20. 📣 Registration is open for the Faculty Development Program on CFD using OpenFOAM by FOSSEE, IIT Bombay.

    This free online program is specially designed for faculty members using CFD in research and teaching.

    📅 2–5 June 2026
    💻 Online | Free of cost

    🔗 Register: shorturl.at/CYUzi

    📲 Scan QR code in poster for registration.

    #CFD #OpenFOAM #ComputationalFluidDynamics #EngineeringFaculty #FDP #FOSSEE #IITBombay #OpenSource #Research #EngineeringEducation #Simulation #OpenSource

  21. #FluidX3D #CFD v3.7 brings faster Q-criterion isosurface rendering with #OpenCL local memory optimization! 🖖🤠
    github.com/ProjectPhysX/FluidX

    Instead of 32 velocities for each #GPU thread, now an 8x8x8 workgroup loads & reuses 11x11x11 velocities in L1$, a 12x VRAM BW reduction.

    Fascinating insight: Which thread loads which cell from VRAM to L1$, and which thread renders which grid cell within the workgroup, can be very different!
    github.com/ProjectPhysX/FluidX

    PS: plugged X-wing Gif in #GitHub preview 🖖😜

  22. #FluidX3D #CFD v3.7 brings faster Q-criterion isosurface rendering with #OpenCL local memory optimization! 🖖🤠
    github.com/ProjectPhysX/FluidX

    Instead of 32 velocities for each #GPU thread, now an 8x8x8 workgroup loads & reuses 11x11x11 velocities in L1$, a 12x VRAM BW reduction.

    Fascinating insight: Which thread loads which cell from VRAM to L1$, and which thread renders which grid cell within the workgroup, can be very different!
    github.com/ProjectPhysX/FluidX

    PS: plugged X-wing Gif in #GitHub preview 🖖😜

  23. 📣 Faculty members working in CFD and simulation are invited to join the Faculty Development Program on CFD using OpenFOAM by FOSSEE, IIT Bombay.

    🗓 2–5 June 2026
    💻 Online Mode

    Learn OpenFOAM from basic to intermediate level with interactive sessions and receive a certificate upon fulfilling attendance criteria.

    🔗 Register: shorturl.at/CYUzi

    #CFD #OpenFOAM #FDP #FOSSEE #IITBombay #OpenSource #EngineeringEducation #Simulation #ComputationalFluidDynamics

  24. #FluidX3D #CFD has reached ⭐ 5000 Stargazers on #GitHub! 🖖🥳
    Grid refinement update is still in development, I haven't forgotten... ⬜◻️◽▫️
    github.com/ProjectPhysX/FluidX

  25. #FluidX3D #CFD has reached ⭐ 5000 Stargazers on #GitHub! 🖖🥳
    Grid refinement update is still in development, I haven't forgotten... ⬜◻️◽▫️
    github.com/ProjectPhysX/FluidX

  26. High-pressure gas release forms vertical jets and explosive clouds. Pressure controls jet height, while wind expands hazard zones and alters dispersion paths. #openaccess at shortlink.uk/1sPZu
    #naturalgas #Pipeline #leakage #ProcessSafety #energyundercontrol #CFD

  27. My implementation of the GaussSeidel smoother using a Diagnol direct access scheme in OpenFOAM as compared to the default GS smoother LDU gives almost ~50% improvements in cache misses and hits for a structured 3D cavity tutorial. Profiled using the amazing LIKWID profiler. Will share a deep technical report soon. Check it out and use - github.com/amartyadav/DIAGauss

  28. My implementation of the GaussSeidel smoother using a Diagnol direct access scheme in OpenFOAM as compared to the default GS smoother LDU gives almost ~50% improvements in cache misses and hits for a structured 3D cavity tutorial. Profiled using the amazing LIKWID profiler. Will share a deep technical report soon. Check it out and use - github.com/amartyadav/DIAGauss
    #hpc #scientificcomputing #computationalphysics #cfd #openfoam #likwid #softwaredevelopment #cpp

  29. Released my DIA-format Gauss-Seidel smoother plugin for OpenFOAM v13. MIT licensed.

    Replaces the default LDU smoother on structured hex meshes — DIA stores diagonal bands contiguously, reducing pointer indirection and DRAM pressure. Expecting 10–20% wall-clock gains and better cache utilisation based on standalone profiling. Full OpenFOAM benchmarks incoming.

    github.com/amartyadav/DIAGauss

  30. Released my DIA-format Gauss-Seidel smoother plugin for OpenFOAM v13. MIT licensed.

    Replaces the default LDU smoother on structured hex meshes — DIA stores diagonal bands contiguously, reducing pointer indirection and DRAM pressure. Expecting 10–20% wall-clock gains and better cache utilisation based on standalone profiling. Full OpenFOAM benchmarks incoming.

    github.com/amartyadav/DIAGauss

    #OpenFOAM #HPC #CFD #NumericalMethods #FOSS #computerscience #physics

  31. Finally Intel #GPU support on Linux too. Watch all the metrics go brrr in multi-GPU #FluidX3D #CFD workload! Will #opensource soon™️

    Hardening against the myriads of broken counters in all those bugged APIs was a long shot. 🖖🫠

    ____________ | Windows | #Linux |
    CPU / RAM | ✅️️WinAPI | ✅️️/proc |
    #Nvidia GPU | ✅️️NVML | ✅️️NVML |
    #Intel GPU | ✅IGCL | ✅SYSMAN |
    #AMD GPU | ✅️️️️ADLX | ✅️️️️AMDSMI |

  32. Finally Intel #GPU support on Linux too. Watch all the metrics go brrr in multi-GPU #FluidX3D #CFD workload! Will #opensource soon™️

    Hardening against the myriads of broken counters in all those bugged APIs was a long shot. 🖖🫠

    ____________ | Windows | #Linux |
    CPU / RAM | ✅️️WinAPI | ✅️️/proc |
    #Nvidia GPU | ✅️️NVML | ✅️️NVML |
    #Intel GPU | ✅IGCL | ✅SYSMAN |
    #AMD GPU | ✅️️️️ADLX | ✅️️️️AMDSMI |

  33. FreeCAD: It IS Rocket Science!

    In full disclosure the author of this post is involved in model, amateur and high power rocketry so forgive their over excitement! This blogpost highlights a fabulous video from #NARCON around using #FreeCAD for #Rocket design analysis using #CFD #rocketry

    blog.freecad.org/2026/04/03/fr

  34. Throwback to April fools day 2022, when I solo-submitted a paper with the word "esoteric" in the title *twice*. The editor carefully asked me if that was a joke. 🖖😆

    Turns out I was totally serious, and that paper scored me the MDPI Computation 2022 Best Paper Award and ~500€ prize money.

    The paper is about two weird algorithms I found to cut VRAM footprint in half for #LBM #CFD simulations on #GPU. A topic that couldn't be more relevant today with the DRAM crisis. 💾🔥

    doi.org/10.3390/computation100

  35. #FluidX3D #CFD v3.6 is out! This release accumulates a number of small improvements over the last months. Most notably, better interactive graphics support on #macOS with XQuartz. Have fun! 🖖😎🌊🍏
    github.com/ProjectPhysX/FluidX