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

Sina Tajfirooz (Corresponding author), Max Hausmann, Jos C.H. Zeegers, J.G.M. (Hans) Kuerten, Jochen Fröhlich

Research output: Contribution to journalArticleAcademicpeer-review

12 Downloads (Pure)

Abstract

A statistical learning approach is presented to predict the dependency of steady hydrodynamic interactions of 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 of steady uniform flow at 1≤Re≤120 around a 1:10 spheroidal body, a database consisting of Reynolds number- and orientation-dependent drag, lift, and pitching torque acting on the particle is collected. A multilayer perceptron is trained and validated with the generated database. The performance of the neural network is tested in a point-particle simulation of the buoyancy-driven motion of a 1:10 disk. Our statistical approach outperforms existing empirical correlations in terms of accuracy. The agreement between the numerical results and the experimental observations prove the potential of the method.

Original languageEnglish
Article number023304
Number of pages17
JournalPhysical Review E
Volume103
Issue number2
DOIs
Publication statusPublished - 15 Feb 2021

Keywords

  • Statistical learning
  • Particulate flows
  • point-particle methods
  • Drag
  • Lift
  • Pitching torque

Fingerprint Dive into the research topics of 'Statistical-learning method for predicting hydrodynamic drag, lift, and pitching torque on spheroidal particles'. Together they form a unique fingerprint.

Cite this