Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows

Abstract

We present a physics-inspired neural network (PINN) model for direct prediction of hydrodynamic forces and torques experienced by individual particles in stationary beds of randomly distributed spheres. In line with our findings, it has recently been demonstrated that conventional fully connected neural networks (FCNN) are incapable of making accurate predictions of force variations in a static bed of spheres. The problem arises due to the large number of input variables (i.e., the locations of individual neighboring particles) leading to an overwhelmingly large number of training parameters in a fully connected architecture. Given the typically limited size of training datasets that can be generated by particle-resolved simulations, the NN becomes prone to missing true patterns in the data, ultimately leading to overfitting. Inspired by our observations in developing the microstructure-informed probability-driven point-particle (MPP) model, we incorporate two main features in the architecture of the present PINN model: 1) superposition of pairwise hydrodynamic interactions between particles, and 2) sharing training parameters between NN blocks that model neighbor influences. These strategies helps to substantially reduce the number of free parameters and thereby control the model complexity without compromising accuracy. We demonstrate that direct force and torque prediction using NNs is indeed possible, provided that the model structure corresponds to the underlying physics of the problem. For a Reynolds number range of 2 <= Re <= 150 and solid volume fractions of 0.1 <= phi <= 0.4, the PINN's predictions prove to be as accurate as those of other microstructure-informed models.

arXiv comment19 pages, 7 figures

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Machine Learning for Fluid Mechanics
• Turbulence Modeling in the Age of Data
• Super-resolution reconstruction of turbulent flows with machine learning
• A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
• Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
• Deep Learning of Vortex Induced Vibrations
• Deep Neural Networks for Data-Driven Turbulence Models
• Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning
• Drag correlation for dilute and moderately dense fluid-particle systems using the lattice Boltzmann method
• Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation

Cited by in corpus (3)

• Point-particle drag, lift, and torque closure models using machine learning: hierarchical approach and interpretability
• Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies
• A Data-Driven Approach for Predicting Hydrodynamic Forces on Spherical Particles Using Volume Fraction Representations