What is it about?
This work develops a 3D convolutional recurrent autoencoder network for real-time parametric flow predictions. Specifically, the present manuscript addresses the online speed-up of a complicated and realistic symmetry-breaking flow past a sphere to predict unsteady flows and instantaneous forces. More importantly, we introduce a novel mesh-to-mesh field transfer and load recovery (FTLR) process to select the best grid for CNNs from a point cloud domain of an unstructured finite-element-based CFD solver.
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Why is it important?
In this work, for the first time, we have shown that besides online speed-up of 1800 times, our 3D CRAN and FTLR framework has also significantly improved the training time/offline speed-up as well as reduced the CFD mesh complexity by nearly 3 times in the deep learning space. In summary, the authors strongly believe that the present contribution is a first attempt at scaling unsteady flow and force prediction for 3D bluff bodies involving physically rich vortex dynamics of low aspect ratio geometries.
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This page is a summary of: Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number, Physics of Fluids, March 2022, American Institute of Physics, DOI: 10.1063/5.0082741.
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