A hybrid point-particle force model that combines physical and data-driven approaches

DOI10.1016/j.jcp.2019.01.053zbMath1451.76139OpenAlexW2916075052WikidataQ128345612 ScholiaQ128345612MaRDI QIDQ2214673

W. C. Moore, G. Akiki, S. Raja Balachandar

Publication date: 10 December 2020

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2019.01.053

zbMATH Keywords

nonlinear regressionEuler-Lagrange methodpairwise interactionpoint-particle modeldrag law

Mathematics Subject Classification ID

General nonlinear regression (62J02) Suspensions (76T20)

Related Items (4)

A disturbance corrected point-particle approach for two-way coupled particle-laden flows on arbitrary shaped grids ⋮ Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows ⋮ Microstructure-informed probability-driven point-particle model for hydrodynamic forces and torques in particle-laden flows ⋮ Incorporating grain-scale processes in macroscopic sediment transport models. A review and perspectives for environmental and geophysical applications

Uses Software

• PMTK

Cites Work

• Unnamed Item
• Immersed boundary method with non-uniform distribution of Lagrangian markers for a non-uniform Eulerian mesh
• Self-induced velocity correction for improved drag estimation in Euler-Lagrange point-particle simulations
• Turbulent Dispersed Multiphase Flow
• Equation of motion for a small rigid sphere in a nonuniform flow
• Flow past a sphere with an oscillation in the free-stream velocity and unsteady drag at finite Reynolds number
• The Motion of High-Reynolds-Number Bubbles in Inhomogeneous Flows
• Pairwise interaction extended point-particle model for a random array of monodisperse spheres
• Random heterogeneous materials. Microstructure and macroscopic properties

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