Omkar2118/datasets huggingface dataset:
3D Flow Shapes

The dataset consists of high-resolution three-dimensional (3D) turbulent flow simulations. It captures intricate vortex structures caused by a variety of shapes within a channel flow environment. The dataset is generated using OpenFOAM in large eddy simulation (LES) mode, ensuring the preservation of detailed turbulent characteristics across all spatial scales.

The dataset contains 45 simulations consisting of 5000 flow snapshots each at a temporal resolution of 0.1ms, for a total physical runtime of 0.5s per simulation. The channel geometry is 0.4x0.1x0.1m discretizted into a 192x48x84 regular grid. The inflow velocity is 20 meters per second. Each simulation contains a unique turbulent flow caused by the specific shape in the channel.

This dataset is ideally suited for machine learning researchers aiming to experiment with surrogate models, generative models, and applications in CFD. It can be used to train neural networks to predict turbulent flow predictions, simulate realistic flow fields efficiently, and evaluate models that aim to replicate the properties of turbulent kinetic energy distribution accurately. Moreover, it provides a rigorous testbed for developing generative models that can bypass computationally expensive CFD simulations, thereby prompting innovations in faster, model-driven fluid dynamics simulations for industrial and academic utilizations.

Introducing paper: From Zero to Turbulence: Generative Modeling for 3D Flow Simulation