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

Abstract

Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest in recent times from dispersed multiphase flow community. The robustness of neural models for this complex multi-body problem is hindered by the availability of particle-resolved data. The present work addresses this unavoidable limitation of data paucity by implementing two strategies: (i) by using a rotation and reflection equivariant neural network and (ii) 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 ($0.25\le Re\le 250$) and particle volume fraction ($0\le \varphi \le 0.4$). 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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Cited by in corpus (2)

• Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies
• $\mathcal{H}$-HIGNN: A Scalable Graph Neural Network Framework with Hierarchical Matrix Acceleration for Simulation of Large-Scale Particulate Suspensions