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

Abstract

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

Phase‐resolving direct numerical simulations of particle transport in liquids—From microfluidics to sediment

“…A second major role of these simulations is to develop closures for meso-scale and macro-scale simulations with several examples highlighted in the sections above. For such purposes techniques of machine learning may be employed with benefit, as done by Tajfirooz et al [128], for example, and many similar studies.…”

Accurate Hydrodynamic Force and Torque Correlations for Prolate Spheroids from Stokes Regime to High Reynolds Numbers

“…The authors propose correlations for force and torque based on the numerical simulations. An interesting study to predict hydrodynamic drag, lift, and torque for an aspect ratio 10 oblate spheroid is performed by Tajfirooz et al [19]. Rather than conventional correlations, the authors utilize a deep neural network (DNN) to compute the force and torque coefficients as a function of 𝑅𝑒 and 𝜙. Jiang et al [20] performed simulations of prolate and oblate spheroids settling in a quiescent fluid.…”