High-resolution fluid–particle interactions: a machine learning approach

DOI10.1017/jfm.2022.174OpenAlexW4221093241MaRDI QIDQ5064921

Tsimur Davydzenka, Pejman Tahmasebi

Publication date: 17 March 2022

Published in: Journal of Fluid Mechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/jfm.2022.174

zbMATH Keywords

river dynamicscoastal engineeringwet granular material

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Uses Software

• LIGGGHTS
• Adam
• CFDEMcoupling

Cites Work

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• Coupled DEM-SPH method for interaction between dilated polyhedral particles and fluid
• An integrated mechanistic-neural network modelling for granular systems
• Machine Learning for Fluid Mechanics
• Machine learning the kinematics of spherical particles in fluid flows
• Using machine learning to detect the turbulent region in flow past a circular cylinder
• Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
• Unsupervised deep learning for super-resolution reconstruction of turbulence
• The voidage function for fluid-particle interaction systems
• Deep learning in fluid dynamics
• Data-driven prediction of the equivalent sand-grain height in rough-wall turbulent flows

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