Unsupervised Learning Based Interaction Force Model for Nonspherical Particles in Incompressible Flows

Technical Report · Sat Sep 30 04:00:00 EDT 2023

DOI:https://doi.org/10.2172/2007744· OSTI ID:2007744

Fan, Liang-Shih ^[1]; Tong, Andrew ^[2]; Hwang, Soohwan ^[2]

---

1. The Ohio State Univ., Columbus, OH (United States); The Ohio State University
2. The Ohio State Univ., Columbus, OH (United States)

This project provides a neural network-based interaction force model for gas-solid flows from low to intermediate Reynolds numbers and concentration, which can be linked to MFiX-DEM. We have constructed a database of the interaction force between the irregular-shaped particles using a spherical harmonic method and the fluid phase based on the particle-resolved direct numerical simulation (PR-DNS) with immersed boundary-based gas kinetic scheme. Unsupervised learning method, i.e., variational auto-encoder (VAE) has been applied to extract the primitive shape factors determining the drag force, lifting forces, and torque. The interaction force model has been trained and validated with a simple but effective multi-layer feed-forward neural network: multi-layer perceptron (MLP), which will be concatenated after the encoder of the previously trained VAE for geometry feature extraction for single, irregular particles. We have trained transpose convolutional neural networks with the PR-DNS data to predict the velocity and pressure gradient of the single particle systems and utilized them to calculate drag force of multi-particle systems. This model can provide high computational efficiency because it does not require collecting multiparticle system data from PR-DNS.

• Research Organization: The Ohio State Univ., Columbus, OH (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: FE0031905
• OSTI ID: 2007744
• Report Number(s): DE--FE0031905
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS