This study developed a data-driven model for the prediction of fluid–particle dynamics

What is it about?

This study developed a data-driven model for the prediction of fluid–particle dynamics by coupling a flow surrogate model based on the deep convolutional neural network (CNN) and a Lagrangian particle tracking model based on the discrete phase model. The applicability of the model for the prediction of the single-fiber filtration efficiency (SFFE) for elliptical- and trilobal-shaped fibers was investigated. Details of fluid–particle dynamics parameters, including fluid and particle velocity vectors and contribution of Brownian and hydrodynamic forces, were examined to qualitatively and quantitatively evaluate the developed data-driven model. The CNN model with the U-net architecture provided highly accurate per-pixel predictions of velocity vectors and static pressure around the fibers with a speedup of more than three orders of magnitude compared with CFD simulations

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