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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2021
- Source ID
- 10.1063/5.0066049
Entities
People
- Bhargav Sriram Siddani
- Ruogu Fang
- Sivaramakrishnan Balachandar
Organizations
- National Science Foundation
- Office of Naval Research
- United States Department of Energy
- University of Florida