Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration

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 sy...

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Bibliographic Details
Published in:Physics of fluids (1994) 2021-10, Vol.33 (10)
Main Authors: Siddani, B., Balachandar, S., Fang, R.
Format: Article
Language:English
Subjects:
Artificial neural networks
Equilibrium flow
Euclidean geometry
Fluid dynamics
Fluid flow
Multiphase flow
Neural networks
Reynolds number
Symmetry
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Summary: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.
ISSN:1070-6631
1089-7666
DOI:10.1063/5.0066049