Streamlined engineering simulations for all

Mainz, Germany
Joined September 2014
dicehub retweeted
interesting to see the new AI developments in @dicehub
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That gust that almost steals your scarf next to a skyscraper? It's not bad luck. It's fluid dynamics. The tower pulls fast wind from up high down to the street, and squeezes it around the corners
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We will be at the @EXPOREAL_Munich next week.
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Accelerating Multiphysics Simulations in DICEHub with Agentic AI We still believe a clear, reproducible workflow cannot be replaced by an AI chatbot. However, AI assistants are valuable for answering questions, making suggestions, and handling specific coding tasks.
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dicehub retweeted
Okay, Opus 5.5 is amazing at motion design. This is my first attempt .. not bad.
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dicehub retweeted
I was successfully pressured into posting more and showing some of the projects and experiments I am working on. So here we go ... Highly experimental, first iteration: Generative AI studio in @dicehub, a template that generates customized applications based on user input and workflow requirements. The idea: you know what you need for your simulation and you already have your own custom workflow, you just talk to the chat and it generates everything based on already available components (3D visualization, storage, logs, HPC, etc.), attaches a domain-specific model to your problem and gives you a reproducible result that you can share. How is this different from just asking Claude to do it? You have an already prepared library of components and blocks that just work and you do not need to do everything from scratch. Writing the app was never the hard part anyway, the hard part is the solver coupling, the result readers, the visualization, finding the optimal settings, validating the result, etc. So the chat only has to work out your workflow instead of reinventing all of it every time. You run your custom but reproducible workflows, check the results with the domain-specific model and iterate. The goal is that the model and the workflow get better with every run. In the video you can see it generating simple apps, running workflows, etc. but there are still a lot of improvements to make. In my opinion this is probably where we are headed for specialized apps that still require a bit of detailed setup and a straightforward workflow setup, you do not want to prompt yourself to death when working on engineering problems. For most other applications the AI Chat will probably eat everything up, we can already see this happening.
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dicehub retweeted
Bring high-performance 3D visualization directly to the browser with #VTK-wasm. Join us September 3 to explore standalone web applications, client-side rendering for #trame, and browser-based volume rendering. Register: ow.ly/8B4q50ZCmT0
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dicehub retweeted
#UFOCFD (cartesian) 15M cells, ran on @dicehub T4 GPU in 3.5 minutes, Mach 0.7 case, cell size 2cm. Testing out the Roe upwind scheme implementation.
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Two design options, same site. One ruins the street corner in wind. Which one? Import both as STL in dicehub, same easy template setup for each, parallel cloud runs, a comfort map for each. Answer while the design can still change. #CFD #PedestrianWindComfort #UrbanDesign
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STL in. Pedestrian wind comfort map out. The PWC template in dicehub builds the simulation case: mesh, wind data, comfort criteria. The cloud runs it. You review where people can sit, stand, and walk. #CFD #PedestrianWindComfort
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dicehub retweeted
Typhoon geometry from thingiverse.com/thing:478598… at Mach 2 in #OpenFoam ran on @dicehub 8 procs, 2000 iterations, runtime 27 mins, mesh only 800k cells including layer mesh. Here showing isentropic Mach contours calculated from surface temperature. Ran in #OF12 "fluid" module.
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dicehub retweeted
ran generic supersonic missile at Mach 5 in #openfoam (#rhoSimpleFoam) in OF12 on @dicehub for 1000 its. Proper mesh with 3 layers, convergence bit wobbly after 600 its, maybe because of the freestream outer boundary.
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dicehub retweeted
#VAWT ran in #OpenFoam using @dicehub 'case run' feature - took 31 hours to run, then downloaded the results and did all post-processing on laptop using #Paraview.
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dicehub retweeted
Ran this #VAWT case on @dicehub recently - it took 31 hours, on just 4procs. More of an experimental run to scope out some of the issues/constraints. Model from thingiverse.com/thing:673454…. The rotation is 60rpm, wind speed 5m/sec. Ran as #incompressibleFluid in OF12 using #NCC
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Done with @dicehub, beautifully enhanced wit @GoogleLabs
🤖 Made with AI
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dicehub retweeted
#incompressibleVoF catamaran ran on @dicehub this time with CFL reduced to 1 (instead of 10) for better resultion of the timesteps, and more accurate solution.
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dicehub retweeted
Boat hydrodynamics ran on @dicehub with #incompressibleVoF #OF12 solver (#interFoam). Case ran on 36 procs in 25mins, mesh 650k cells, simulated time 10 seconds.
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dicehub retweeted
#incompressibleVoF #OF12 (previously #interFoam) ran on @dicehub - 100 mins runtime, 36 cpu, 650k cells
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dicehub retweeted
#interFoam case ran on @dicehub
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Did you know you can just setup your OpenFOAM case on @dicehub , generate the case and download it without the need to run it in the cloud?
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