Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration
Abstract
This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles in a dispersed multiphase setup using convolutional neural network (CNN). The considered problem involves rotational symmetry about the mean velocity (streamwise) direction. Thus, this work enforces this symmetry using SE(3)-equivariant, special Euclidean group of dimension 3, CNN architecture, which is translation and three-dimensional rotation equivariant. This study mainly explores the generalization capabilities and benefits of a SE(3)-equivariant network. Accurate synthetic flow fields for Reynolds number and particle volume fraction combinations spanning over a range of [86.22, 172.96] and [0.11, 0.45], respectively, are produced with careful application of symmetry-aware data-driven approach.
- Publication:
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Physics of Fluids
- Pub Date:
- October 2021
- DOI:
- 10.1063/5.0066049
- arXiv:
- arXiv:2108.03494
- Bibcode:
- 2021PhFl...33j3323S
- Keywords:
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- Physics - Fluid Dynamics;
- Physics - Computational Physics
- E-Print:
- Main change: The acronym CNN in title of previous version has been changed to Convolutional Neural Network