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Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability

B. Siddani* and S. Balachandar†

  • Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, Florida 32611, USA

  • *siddanib@ufl.edu
  • †bala1s@ufl.edu
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Phys. Rev. Fluids 8, 014303 – Published 17 January, 2023

DOI: https://doi.org/10.1103/PhysRevFluids.8.014303

Abstract

Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest recently from the dispersed multiphase flow community. The robustness of neural models for this complex multibody problem is hindered by the availability of particle-resolved data. The present work addresses this unavoidable limitation of data paucity by implementing two strategies: (1) by using a rotation and reflection equivariant neural network and (2) by pursuing a physics-based hierarchical machine learning approach. The resulting machine-learned models are observed to achieve a maximum accuracy of 85% and 96% in the prediction of neighbor-induced force and torque fluctuations, respectively, for a wide range of Reynolds number and volume fraction conditions considered. Furthermore, we pursue force and torque network architectures that provide universal prediction spanning a wide range of Reynolds number (0.25≤Re≤250) and particle volume fraction (0≤ϕ≤0.4). The hierarchical nature of the approach enables improved prediction of quantities such as streamwise torque, by going beyond binary interactions to include trinary interactions.

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Phys. Rev. Fluids 8, 014303– Published 17 January, 2023
  • Received 18 July 2022
  • Accepted 23 December 2022
Article part of CHORUS
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