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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.
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.
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
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
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.
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
Boat hydrodynamics ran on @dicehub with #incompressibleVoF #OF12 solver (#interFoam).
Case ran on 36 procs in 25mins, mesh 650k cells, simulated time 10 seconds.
#incompressibleVoF #OF12 (previously #interFoam) ran on @dicehub - 100 mins runtime, 36 cpu, 650k cells










