#computationalfluiddynamics — Public Fediverse posts

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

#FluidMechanics #FluidDynamics #AskFedi #DuckDuckFedi hey, I'm wondering which is better for cooling ? The desk isn't a perfect seal if you're wondering, air can vent, but slowly.

This computer is in a pull-push configuration, the GPU at the back is pulling fresh air in and the radiator is pushing hot air out as drawn by arrows. The radiator "pushing" fans are stronger than the GPU's "pulling" fans, so there is negative pressure, which means some air gets sucked in from surrounding case intakes (above, below etc).

#ComputationalFluidDynamics #FluidSimulation

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)

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)

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)

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)

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)

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)

📣 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

37x Speedup in Lattice Boltzmann Cylinder Flow

https://github.com/alikamp/Parks-KPBM-Scaling

#HackerNews #LatticeBoltzmann #Speedup #ComputationalFluidDynamics #Research #Innovation

37x Speedup in Lattice Boltzmann Cylinder Flow

https://github.com/alikamp/Parks-KPBM-Scaling

#HackerNews #LatticeBoltzmann #Speedup #ComputationalFluidDynamics #Research #Innovation

📣 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: https://shorturl.at/CYUzi

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

Funded PhD - multiscale electrochemical modeling
University College Dublin

See the full job description on jobRxiv: https://jobrxiv.org/job/university-college-dublin-27778-funded-phd-multiscale-electrochemical-modeling/

#battery #computationalfluiddynamics #electrochemistry #moleculardynamics #quantumchemistry #ScienceJobs #hiring #research
https://jobrxiv.org/job/university-college-dublin-27778-funded-phd-multiscale-electrochemical-modeling/?fsp_sid=10273

Funded PhD - multiscale electrochemical modeling
University College Dublin

See the full job description on jobRxiv: https://jobrxiv.org/job/university-college-dublin-27778-funded-phd-multiscale-electrochemical-modeling/

#battery #computationalfluiddynamics #electrochemistry #moleculardynamics #quantumchemistry #ScienceJobs #hiring #research
https://jobrxiv.org/job/university-college-dublin-27778-funded-phd-multiscale-electrochemical-modeling/?fsp_sid=10273

Richtmyer-Meshkov Instability

If you send a shock wave through a magnetized plasma–something that happens in both supernova explosions and inertial confinement fusion–it can trigger an instability known as the Richtmyer-Meshkov instability. The image above shows a form of this, taken from a simulation. Rather than treating the plasma as a single idealized fluid, the researchers represented it as two fluids: an ion fluid and an electron fluid. This allowed them to better capture what happens when certain components of the plasma react to changes faster than others do.

The image itself shows the electron number density across the fluid, where darker colors represent higher electron number density. The interface between high and low-densities shows a roll-up instability that resembles the Kelvin-Helmholtz instability, but there are also regions of mushroom-like plumes that more closely resemble Rayleigh-Taylor instabilities.

The authors note that these structures don’t appear in simulations that represent a plasma as a single fluid; you need the two-fluid representation to see them. (Image and research credit: O. Thompson et al.)

Richtmyer-Meshkov Instability

If you send a shock wave through a magnetized plasma–something that happens in both supernova explosions and inertial confinement fusion–it can trigger an instability known as the Richtmyer-Meshkov instability. The image above shows a form of this, taken from a simulation. Rather than treating the plasma as a single idealized fluid, the researchers represented it as two fluids: an ion fluid and an electron fluid. This allowed them to better capture what happens when certain components of the plasma react to changes faster than others do.

The image itself shows the electron number density across the fluid, where darker colors represent higher electron number density. The interface between high and low-densities shows a roll-up instability that resembles the Kelvin-Helmholtz instability, but there are also regions of mushroom-like plumes that more closely resemble Rayleigh-Taylor instabilities.

The authors note that these structures don’t appear in simulations that represent a plasma as a single fluid; you need the two-fluid representation to see them. (Image and research credit: O. Thompson et al.)

Improving Turbulence Models

Calculating turbulent flows like those found in the ocean and atmosphere is extremely expensive computationally. That’s why forecasting models use techniques like Large Eddy Simulation (LES), where large physical scales are calculated according to the governing physical equations while smaller scales are approximated with mathematical models. Researchers are always looking for ways to improve these models–making them more physically accurate, easier to compute, and more computationally stable.

In a new study, researchers used an equation-discovery tool to find new improvements to these models for the smaller turbulent scales. They started by doing a full, computationally expensive calculation of the turbulent flow. The equation-discovery tool then analyzed these results, looking to match them to a library of over 900 possible equations. When it found a form that fit the data, the researchers were then able to show analytically how to derive that equation from the underlying physics. The result is a new equation that models these smaller scales in a way that’s physically accurate and computationally stable, offering possibilities for better LES. (Image credit: CasSa Paintings; research credit: K. Jakhar et al.; via APS)

Improving Turbulence Models

Calculating turbulent flows like those found in the ocean and atmosphere is extremely expensive computationally. That’s why forecasting models use techniques like Large Eddy Simulation (LES), where large physical scales are calculated according to the governing physical equations while smaller scales are approximated with mathematical models. Researchers are always looking for ways to improve these models–making them more physically accurate, easier to compute, and more computationally stable.

In a new study, researchers used an equation-discovery tool to find new improvements to these models for the smaller turbulent scales. They started by doing a full, computationally expensive calculation of the turbulent flow. The equation-discovery tool then analyzed these results, looking to match them to a library of over 900 possible equations. When it found a form that fit the data, the researchers were then able to show analytically how to derive that equation from the underlying physics. The result is a new equation that models these smaller scales in a way that’s physically accurate and computationally stable, offering possibilities for better LES. (Image credit: CasSa Paintings; research credit: K. Jakhar et al.; via APS)

Transport and settling of suspended particles in a simulated estuary: particle-laden freshwater enters a basin filled with seawater. The white iso-surface indicates 50% of the original particle density. Kelvin-Helmholtz instabilities evolve in the shear flow and drive the turbulent mixing. Rayleigh–Taylor instabilities can be observed in the initial settling phase. Based on Direct #Numerical #Simulation.

#sedimentation #estuary #fluiddynamics #turbulence #CFD #computationalfluiddynamics

Transport and settling of suspended particles in a simulated estuary: particle-laden freshwater enters a basin filled with seawater. The white iso-surface indicates 50% of the original particle density. Kelvin-Helmholtz instabilities evolve in the shear flow and drive the turbulent mixing. Rayleigh–Taylor instabilities can be observed in the initial settling phase. Based on Direct #Numerical #Simulation.

#sedimentation #estuary #fluiddynamics #turbulence #CFD #computationalfluiddynamics

Another one from the archive: turbulent mixing of sediment-laden freshwater and seawater (black). The white iso-surface indicates a 50/50 mix. The freshwater enters the basin at the bottom left. Direct #Numerical #Simulation.

#sedimentation #estuary #fluiddynamics #turbulence #CFD #computationalfluiddynamics

Another one from the archive: turbulent mixing of sediment-laden freshwater and seawater (black). The white iso-surface indicates a 50/50 mix. The freshwater enters the basin at the bottom left. Direct #Numerical #Simulation.

#sedimentation #estuary #fluiddynamics #turbulence #CFD #computationalfluiddynamics

Snapshot of the turbulent mixing of sediment-laden freshwater (black) and seawater (white / grey) in a modelled estuary. The scene is seen from the top. The freshwater enters from the bottom of the picture.

The results were obtained from a Direct #Numerical #Simulation. Only half of the domain is simulated (the other half is mirrored). Only the interesting part is shown (the simulated domain is actually a lot bigger).

#sedimentation #estuary #fluiddynamics #CFD #computationalfluiddynamics

Snapshot of the turbulent mixing of sediment-laden freshwater (black) and seawater (white / grey) in a modelled estuary. The scene is seen from the top. The freshwater enters from the bottom of the picture.

The results were obtained from a Direct #Numerical #Simulation. Only half of the domain is simulated (the other half is mirrored). Only the interesting part is shown (the simulated domain is actually a lot bigger).

#sedimentation #estuary #fluiddynamics #CFD #computationalfluiddynamics

Inside Cepheid Variable Stars

Cepheid variable stars pulsate in brightness over regular periods. That’s one reason astronomers use them as a standard candle to judge distances–even for stars well outside our galaxy. In this image, researchers display a simulation of convection inside a Cepheid eight times more massive than our sun. The colors represent vorticity, with zero vorticity in white.(Image credit: M. Stuck and J. Pratt)

Inside Cepheid Variable Stars

Cepheid variable stars pulsate in brightness over regular periods. That’s one reason astronomers use them as a standard candle to judge distances–even for stars well outside our galaxy. In this image, researchers display a simulation of convection inside a Cepheid eight times more massive than our sun. The colors represent vorticity, with zero vorticity in white.(Image credit: M. Stuck and J. Pratt)

ExaWind Simulation

Large-scale computational fluid dynamics simulations face many challenges. Among them is the need to capture both large physical scales–like those of Earth’s atmospheric boundary layer–and small scales–like those of tiny eddies moving around a wind-turbine blade. Capturing all of these scales for a problem like four wind turbines in a wind farm requires using the full computing power of every processor in a large supercomputer. That’s the level of power behind the simulation visualized in this video. The results, however, are stunning. (Video and image credit: M. da Frahan et al.)

ExaWind Simulation

Large-scale computational fluid dynamics simulations face many challenges. Among them is the need to capture both large physical scales–like those of Earth’s atmospheric boundary layer–and small scales–like those of tiny eddies moving around a wind-turbine blade. Capturing all of these scales for a problem like four wind turbines in a wind farm requires using the full computing power of every processor in a large supercomputer. That’s the level of power behind the simulation visualized in this video. The results, however, are stunning. (Video and image credit: M. da Frahan et al.)

Having Just Realized™ that I can use Sphere Glyphs to render my particles in #ParaView, I am now Very Happy™ because my low-resolution #SPH simulations can be made to appear so much nicer.

#SmoothedParticleHydrodynamics #CFD #ComputationalFluidDynamics #rendering #visualization

Having Just Realized™ that I can use Sphere Glyphs to render my particles in #ParaView, I am now Very Happy™ because my low-resolution #SPH simulations can be made to appear so much nicer.

#SmoothedParticleHydrodynamics #CFD #ComputationalFluidDynamics #rendering #visualization

Oceans Could “Burp” Out Absorbed Heat

Earth’s atmosphere and oceans form a complicated and interconnected system. Water, carbon, nutrients, and heat move back and forth between them. As humanity pumps more carbon and heat into the atmosphere, the oceans–and particularly the Southern Ocean–have been absorbing both. A new study looks ahead at what the long-term consequences of that could be.

The team modeled a scenario where, after decades of carbon emissions, the world instead sees a net decrease in carbon–which could be achieved by combining green energy production with carbon uptake technologies. They found that, after centuries of carbon reduction and gradual cooling, the Southern Ocean could release some of its pent-up heat in a “burp” that would raise global temperatures by tenths of a degree for decades to a century. The burp would not raise carbon levels, though.

The research suggests that we should continue working to understand the complex balance between the atmosphere and oceans–and how our changes will affect that balance not only now but in the future. (Image credit: J. Owens; research credit: I. Frenger et al.; via Eos)

#CFD #climateChange #computationalFluidDynamics #fluidDynamics #geophysics #heatTransfer #numericalSimulation #ocean #physics #science

Oceans Could “Burp” Out Absorbed Heat

Earth’s atmosphere and oceans form a complicated and interconnected system. Water, carbon, nutrients, and heat move back and forth between them. As humanity pumps more carbon and heat into the atmosphere, the oceans–and particularly the Southern Ocean–have been absorbing both. A new study looks ahead at what the long-term consequences of that could be.

The team modeled a scenario where, after decades of carbon emissions, the world instead sees a net decrease in carbon–which could be achieved by combining green energy production with carbon uptake technologies. They found that, after centuries of carbon reduction and gradual cooling, the Southern Ocean could release some of its pent-up heat in a “burp” that would raise global temperatures by tenths of a degree for decades to a century. The burp would not raise carbon levels, though.

The research suggests that we should continue working to understand the complex balance between the atmosphere and oceans–and how our changes will affect that balance not only now but in the future. (Image credit: J. Owens; research credit: I. Frenger et al.; via Eos)

#CFD #climateChange #computationalFluidDynamics #fluidDynamics #geophysics #heatTransfer #numericalSimulation #ocean #physics #science

Waves Over Sand Ripples

Look beneath the waves on a beach or in a bay, and you’ll find ripples in the sand. Passing waves shape these sandforms and can even build them to heights that require dredging to keep waterways passable to large ships. To better understand how the sand interacts with the flow, researchers build computer models that couple the flow of the water with the behavior of individual sand grains. One recent study found that sand grains experienced the most shear stress as the flow first accelerates and then again when a vortex forms near the crest of the ripple. (Image credit: D. Hall; research credit: S. DeVoe et al.; via Eos)

#CFD #computationalFluidDynamics #fluidDynamics #geophysics #granularMaterial #oceanWaves #physics #sandRipples #science #sedimentTransport #sedimentation

Waves Over Sand Ripples

Look beneath the waves on a beach or in a bay, and you’ll find ripples in the sand. Passing waves shape these sandforms and can even build them to heights that require dredging to keep waterways passable to large ships. To better understand how the sand interacts with the flow, researchers build computer models that couple the flow of the water with the behavior of individual sand grains. One recent study found that sand grains experienced the most shear stress as the flow first accelerates and then again when a vortex forms near the crest of the ripple. (Image credit: D. Hall; research credit: S. DeVoe et al.; via Eos)

#CFD #computationalFluidDynamics #fluidDynamics #geophysics #granularMaterial #oceanWaves #physics #sandRipples #science #sedimentTransport #sedimentation

The next issue of the #SPHERIC #newsletter is fresh out, go download it from https://www.spheric-sph.org/newsletters

#SPH #CFD #ComputationalFluidDynamics

The next issue of the #SPHERIC #newsletter is fresh out, go download it from https://www.spheric-sph.org/newsletters

#SPH #CFD #ComputationalFluidDynamics

Bow Shock Instability

There are few flows more violent than planetary re-entry. Crossing a shock wave is always violent; it forces a sudden jump in density, temperature, and pressure. But at re-entry speeds this shock wave is so strong the density can jump by a factor of 13 or more, and the temperature increase is high enough that it literally rips air molecules apart into plasma.

Here, researchers show a numerical simulation of flow around a space capsule moving at Mach 28. The transition through the capsule’s bow shock is so violent that within a few milliseconds, all of the flow behind the shock wave is turbulent. Because turbulence is so good at mixing, this carries hot plasma closer to the capsule’s surface, causing the high temperatures visible in reds and yellows in the image. Also shown — in shades of gray — is the vorticity magnitude of flow around the capsule. (Image credit: A. Álvarez and A. Lozano-Duran)

#2024gofm #CFD #computationalFluidDynamics #flowVisualization #fluidDynamics #hypersonic #instability #numericalSimulation #physics #science #shockWave #turbulence

Bow Shock Instability

There are few flows more violent than planetary re-entry. Crossing a shock wave is always violent; it forces a sudden jump in density, temperature, and pressure. But at re-entry speeds this shock wave is so strong the density can jump by a factor of 13 or more, and the temperature increase is high enough that it literally rips air molecules apart into plasma.

Here, researchers show a numerical simulation of flow around a space capsule moving at Mach 28. The transition through the capsule’s bow shock is so violent that within a few milliseconds, all of the flow behind the shock wave is turbulent. Because turbulence is so good at mixing, this carries hot plasma closer to the capsule’s surface, causing the high temperatures visible in reds and yellows in the image. Also shown — in shades of gray — is the vorticity magnitude of flow around the capsule. (Image credit: A. Álvarez and A. Lozano-Duran)

#2024gofm #CFD #computationalFluidDynamics #flowVisualization #fluidDynamics #hypersonic #instability #numericalSimulation #physics #science #shockWave #turbulence

Talking about dependencies: one thing we did *not* reimplement in #GPUSPH is rigid body motion. GPUSPH is intended to be code for #CFD, and while I do dream about making it a general-purpose code for #ContinuumMechanics, at the moment anything pertaining solids is “delegated”.

When a (solid) object is added to a test case in GPUSPH, it can be classified as either a “moving” or a “floating” object. The main difference is that a “moving” object is assumed to have a prescribed motion, which effectively means the user has to also define how the object moves, while a “floating” object is assumed to move according to the standard equations of motion, with the forces and torques exerted on the body by the fluid provided by GPUSPH.

For floating objects, we delegate the rigid body motion computation to the well-established simulation engine #ProjectChrono
https://projectchrono.org/

Chrono is a “soft dependency” of GPUSPH: you do not need it to build a generic test case, but you do need it if you want floating objects without having to write the entire rigid body solver yourself.

1/n

#SmoothedParticleHydrodynamics #SPH #ComputationalFluidDynamics

Talking about dependencies: one thing we did *not* reimplement in #GPUSPH is rigid body motion. GPUSPH is intended to be code for #CFD, and while I do dream about making it a general-purpose code for #ContinuumMechanics, at the moment anything pertaining solids is “delegated”.

When a (solid) object is added to a test case in GPUSPH, it can be classified as either a “moving” or a “floating” object. The main difference is that a “moving” object is assumed to have a prescribed motion, which effectively means the user has to also define how the object moves, while a “floating” object is assumed to move according to the standard equations of motion, with the forces and torques exerted on the body by the fluid provided by GPUSPH.

For floating objects, we delegate the rigid body motion computation to the well-established simulation engine #ProjectChrono
https://projectchrono.org/

Chrono is a “soft dependency” of GPUSPH: you do not need it to build a generic test case, but you do need it if you want floating objects without having to write the entire rigid body solver yourself.

1/n

#SmoothedParticleHydrodynamics #SPH #ComputationalFluidDynamics

This thread about writing games vs writing game engines <https://peoplemaking.games/@eniko/114137694102639793> by @eniko, and the comments within by many other people, is a fascinating read for me, particularly in relation to the similarities and the differences with our experience in the development of what is, for all intents and purposes, a #CFD engine, but also many of the test cases it has been used for.

For many #ComputationalFluidDynamics methods, it's actually pretty simple to write an implementation for a “trivial” test case (straight walls, right angles if any at all, periodic boundary conditions, etc). We did that for example in a couple of hours during the MODCLIM 2016 training school https://modclim.ulpgc.es/index.php/events/8-modclim-events

Things become quickly non-trivial as soon as you start needing

1. non-trivial geometries

and

2. more complex physics and/or more sophisticated methods.

1/

This thread about writing games vs writing game engines <https://peoplemaking.games/@eniko/114137694102639793> by @eniko, and the comments within by many other people, is a fascinating read for me, particularly in relation to the similarities and the differences with our experience in the development of what is, for all intents and purposes, a #CFD engine, but also many of the test cases it has been used for.

For many #ComputationalFluidDynamics methods, it's actually pretty simple to write an implementation for a “trivial” test case (straight walls, right angles if any at all, periodic boundary conditions, etc). We did that for example in a couple of hours during the MODCLIM 2016 training school https://modclim.ulpgc.es/index.php/events/8-modclim-events

Things become quickly non-trivial as soon as you start needing

1. non-trivial geometries

and

2. more complex physics and/or more sophisticated methods.

1/

How CO2 Gets Into the Ocean

Our oceans absorb large amounts of atmospheric carbon dioxide. Liquid water is quite good at dissolving carbon dioxide gas, which is why we have seltzer, beer, sodas, and other carbonated drinks. The larger the surface area between the atmosphere and the ocean, the more quickly carbon dioxide gets dissolved. So breaking waves — which trap lots of bubbles — are a major factor in this carbon exchange.

This video shows off numerical simulations exploring how breaking waves and bubbly turbulence affect carbon getting into the ocean. The visualizations are gorgeous, and you can follow the problem from the large-scale (breaking waves) all the way down to the smallest scales (bubbles coalescing). (Video and image credit: S. Pirozzoli et al.)

#2024gfm #breakingWave #bubbles #carbonCycle #carbonDioxide #CFD #climateChange #computationalFluidDynamics #dissolution #flowVisualization #fluidDynamics #numericalSimulation #physics #science #turbulence

How CO2 Gets Into the Ocean

Our oceans absorb large amounts of atmospheric carbon dioxide. Liquid water is quite good at dissolving carbon dioxide gas, which is why we have seltzer, beer, sodas, and other carbonated drinks. The larger the surface area between the atmosphere and the ocean, the more quickly carbon dioxide gets dissolved. So breaking waves — which trap lots of bubbles — are a major factor in this carbon exchange.

This video shows off numerical simulations exploring how breaking waves and bubbly turbulence affect carbon getting into the ocean. The visualizations are gorgeous, and you can follow the problem from the large-scale (breaking waves) all the way down to the smallest scales (bubbles coalescing). (Video and image credit: S. Pirozzoli et al.)

#2024gfm #breakingWave #bubbles #carbonCycle #carbonDioxide #CFD #climateChange #computationalFluidDynamics #dissolution #flowVisualization #fluidDynamics #numericalSimulation #physics #science #turbulence

Hello all! This will be the official #GPUSPH account on the Fediverse going forward. What is GPUSPH, you ask? It's a software for #ComputationalFluidDynamics using the #SmoothedParticleHydrodynamics method, accelerated by running entirely* on GPU. In fact, it was the first to do so, leveraging the new GPGPU capabilities offered by NVIDIA CUDA.

(These days we have wider hardware support, but for a long time CUDA was all we supported.)

#introduction #newHere #CFD #HPC

*conditions apply

In recent years, Arctic permafrost has thawed at a surprisingly fast pace. Much of that is, of course, due to the rapid warming caused by climate change. But some of that phenomenon lives underground, where water’s unusual properties cause convection in gaps between rocks, sediment, and soil.

Water is densest not as ice but as water. This is why ice cubes float in your glass. Water’s densest form is actually a liquid at 4 degrees Celsius. For water-logged Arctic soils, this means that the densest layer is not at the frozen depth but at a higher, shallower depth. This places a dense liquid-infused layer over a lighter one, a recipe for unstable convection.

In a recent numerical simulation, researchers found that this underground convection caused permafrost to thaw much more quickly than it would due to heat conduction alone. In fact, the effects appeared in as little as one month, so in a single summer, this convection could have a big effect on the thaw depth. (Image credit: top – Florence D., figure – M. Magnani et al.; research credit: M. Magnani et al.)

https://fyfluiddynamics.com/2024/10/underground-convection-thaws-permafrost-faster/

#CFD #computationalFluidDynamics #convection #fluidDynamics #geophysics #instability #numericalSimulation #permafrost #physics #planetaryScience #science

The Gulf Stream current carries warm, salty water from the Gulf of Mexico northeastward. In the North Atlantic, this water cools and sinks and drifts southwestward, emerging centuries later in the Southern Ocean. Known as the Atlantic Meridional Overturning Circulation (AMOC), this circulation is critical, among other things, to Europe’s temperate climate. Since 1995, scientists have been warning that human-driven climate change is weakening the AMOC and may cause it to shut down entirely — which would have catastrophic consequences for our society.

A recent study re-examined the AMOC using both low- and high-resolution numerical simulations, combined with direct observations. Both simulations covered 1950 – 2100 and found the AMOC’s strength has declined since 1950. But the high-resolution simulation found significant regional variations in the AMOC’s behavior. Some regions saw localized strengthening, while other areas showed abrupt collapse. These sensitive shifts underscore the importance of driving toward higher resolutions in our next-generation climate models, if we want to better understand — and perhaps predict — what lies ahead as our climate changes. (Image credit: illustration – Atlantic Oceanographic and Meteorological Laboratory, simulations – R. Gou et al.; research credit: R. Gou et al.; via APS Physics)

https://fyfluiddynamics.com/2024/08/resolution-effects-on-ocean-circulation/

#CFD #circulation #climateChange #computationalFluidDynamics #flowVisualization #fluidDynamics #numericalSimulation #oceanCurrents #oceanography #physics #science

Growing up in northwest Arkansas, I spent my share of summer nights sheltering from tornadoes. Central North America — colloquially known as Tornado Alley — is especially prone to violent thunderstorms and accompanying tornadoes. That’s due, in part, to two geographical features: the Rocky Mountains and the Gulf of Mexico. Trade winds hitting the eastern slope of the Rockies get turned northward, imparting a counterclockwise vorticity. At the same time, warm moist air carried from the Gulf feeds into the atmosphere, creating perfect conditions for powerful thunderstorms. By this logic, though, South America should see lots of tornadoes, too, courtesy of the Andes Mountains and the moist environs of the Amazon Basin. To understand why South America doesn’t have a Tornado Alley, researchers used global weather models to investigate alternate North and South Americas.

They found that smoothness is a key ingredient for the upstream, moisture-generating region. Compared to the Amazon, the Gulf of Mexico is incredibly flat. With a flat Gulf, tornadoes abounded in North America, but their numbers dropped once that area was roughened to mimic the Amazon. The opposite held true, too: a smoothed-out Amazon Basin resulted in more simulated South American tornadoes.

For those in Tornado Alley, the results don’t offer much hope for mitigating our summer storms — we can’t exactly roughen the ocean. But the study does sound a word for warning for South America; the smoother the Amazon region becomes — due to mass deforestation — the more likely tornadoes become in parts of South America. (Image credit: G. Johnson; research credit: F. Li et al.; via Physics World)

https://fyfluiddynamics.com/2024/08/why-tornado-alley-is-north-american/

#atmosphericScience #CFD #computationalFluidDynamics #fluidDynamics #meteorology #physics #science #surfaceRoughness #thunderstorm #tornado #vorticity

Venus flower basket sponges have an elaborate, vase-like skeleton pocked with holes that allow water to pass through the organism. A recent numerical study looked at how the sponge’s shape deflects incoming (horizontal) ocean currents into a vertical flow the sponge can use to filter out food.

The sponges’ structure is porous and lined with helical structures. In their simulation, researchers reproduced a version of this structure (shown below) that used none of the real sponge’s active pumping mechanisms. The digital sponge was, instead, purely passive. Nevertheless, the simulation showed that, by their skeletal structure alone, sponges could redirect a significant fraction of incoming flow toward its filtering surfaces. Interestingly, the highest deflection fraction occurred at relatively low flow speeds, showing that the sponges are set up so that their structure is especially helpful for scavenging nutrients from nearly-still waters.

In the real world, these sponges use a combination of passive filtering and active pumping to capture their food, but this study shows that the sponge’s clever structure helps it save energy, especially in tough flow conditions. (Image credit: sponges – NOAA, simulation – G. Falcucci et al.; research credit: G. Falcucci et al.; via APS Physics)

https://fyfluiddynamics.com/2024/06/venus-flower-basket-sponges/

#biology #CFD #computationalFluidDynamics #filterFeeding #fluidDynamics #numericalSimulation #physics #porousFlow #science

I also want to explore more #GeometricAlgebra applied to #physics and #ComputationalPhysics (not necessarily for #FluidDynamics and #CFD, but that would be preferable as it's obviously our primary topic of relevance). I can't seem to find anything that combines #SPH and GA, so it might even lead to some new interesting venues to explore.

3/n

#ComputationalFluidDynamics #SmoothedParticleHydrodynamics

I also want to explore more #GeometricAlgebra applied to #physics and #ComputationalPhysics (not necessarily for #FluidDynamics and #CFD, but that would be preferable as it's obviously our primary topic of relevance). I can't seem to find anything that combines #SPH and GA, so it might even lead to some new interesting venues to explore.

3/n

#ComputationalFluidDynamics #SmoothedParticleHydrodynamics

Does anybody know of #Lemmy communities/magazines centered around any of these topics:

#CFD aka #ComputationalFluidDynamics

#SPH aka #SmoothedParticleHydrodynamics

The closest I can find is @fluidmechanics

Does anybody know of #Lemmy communities/magazines centered around any of these topics:

#CFD aka #ComputationalFluidDynamics

#SPH aka #SmoothedParticleHydrodynamics

The closest I can find is @fluidmechanics

date: 2023-02-11 22:21:21
by: PhdScanner

✅ Passionate about #airquality and #health ?

✅ 3️⃣ year fully funded #phdposition on designing #sustainable methods for airborne particle spread @KTHuniversity

🗓️Deadline: 09 Mar '23

Details 👇

https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:591455/type:job/where:4/apply:1

#computationalfluiddynamics #Python #matlab #cfd #sweden

🐦🔗: https://twitter.com/twitter/statuses/1624534077968637956
#PhdPosition

Wow, this is pretty cool, history of #CFD #ComputationalFluidDynamics.

https://youtu.be/KWtX_E51gtU

Wow, this is pretty cool, history of #CFD #ComputationalFluidDynamics.

https://youtu.be/KWtX_E51gtU

You’ll shoot your eye out: Popped champagne cork ejects CO2 at supersonic speeds - Enlarge (credit: Andy Roberts/Getty Images)

The pop of a champ... - https://arstechnica.com/?p=1859317 #computationalfluiddynamics #champagnescience #fluiddynamics #supersonic #science #physics

Let's talk about #SmoothedParticleHydrodynamics (#SPH for short, even though you'll see #sph_ used on other sites, because #SPH also has a very common, very not-safe-for-work meaning …).

Why should we talk about it? Because it's relatively less known than other numerical methods, possibly undeservingly so, and because I love it.

So what is it? SPH is a Lagrangian meshless numerical method primarily used for #ComputationalFluidDynamics (and more recently also #ComputationalMechanics).

#introduction

I work at the Osservatorio Etneo, Catania section of the Italian National Institute for Geophysics and Volcanology #INGV.
Mathematician by formation, scientific software developer by necessity, I work on #lava flow #simulation, #hazard assessment, #risk mitigation.
Much of my work revolves around #ComputationalFluidDynamics (#CFD), w/ a preference for #SmoothedParticleHydrodynamics (#SPH).
I should probably mention my interest in #HPC and #GPGPU, but I ran out of characters …