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Physics-informed Neural Network for Modelling Force and Torque Fluctuations in a Random Array of Bidisperse Spheres by Cheng, Zihao; Wachs, Anthony is a scholarly article available to read on EtoBox.

What is Physics-informed Neural Network for Modelling Force and Torque Fluctuations in a Random Array of Bidisperse Spheres about?

We present a physics-informed neural network (PINN) model to predict the hydrodynamic force and torque fluctuations in a random array of stationary bidisperse spheres. The PINN model is formulated based on two hypotheses: (i) pairwise interaction assumption that approximates the total force/torque exerted on a target sphere by linear superposition of individual contributions from a finite number of influential neighbors; (i) unified function representation that suggests a single functional form to describe the contribution from different neighbors based on the observation of probability distribution maps obtained with various binary interaction modes in bidisperse particle-laden flows. We accordingly establish a compact PINN architecture to evaluate individual force/torque contribution of influential neighbors through the same neural network block which tremendously reduces the number of unknown parameters, and ultimately compute the total force/torque exerted on target sphere by their weighted sum. We compare the model predictions to PR-DNS data of eight different cases in a range of Reynolds number $1\leq Re\leq100$, solid volume fraction $10\%\leq\phi\leq40\%$, sphere diameter r

Author

Cheng, Zihao; Wachs, Anthony

Published

2023

Language

EN