Statistical-learning method for predicting hydrodynamic drag, lift, and pitching torque on spheroidal particles

A statistical learning approach is presented to predict the dependency of steady hydrodynamic interactions thin oblate spheroidal particles on particle orientation and Reynolds number. The conventional empirical correlations that approximate such dependencies are replaced by a neural-network-based correlation which can provide accurate predictions for high-dimensional input spaces occurring in flows with nonspherical particles. By performing resolved simulations uniform flow at 1≤Re≤120 around 1:10 body, database consisting number- orientation-dependent drag, lift, pitching torque acting collected. multilayer perceptron trained validated generated database. performance neural network tested point-particle simulation buoyancy-driven motion disk. Our outperforms existing terms accuracy. agreement between numerical results experimental observations prove potential method.