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Neural vortex method: From finite Lagrangian particles to infinite dimensional Eulerian dynamics by Shiying Xiong; Xingzhe He; Yunjin Tong; Yitong Deng; Bo Zhu is a Engineering article available to read on EtoBox.

What is Neural vortex method: From finite Lagrangian particles to infinite dimensional Eulerian dynamics about?

In fluid analysis, there has been a long-standing problem: lacking a rigorous mathematical tool to map from a continuous flow field to finite discrete particles, hurdling the Lagrangian particles from inheriting the high resolution of a large-scale Eulerian solver. To tackle this challenge, we propose a novel learning-based framework, the neural vortex method (NVM). NVM builds a neural-network description of the Lagrangian vortex structures and their interaction dynamics to reconstruct the high-resolution Eulerian flow field in a physically-precise manner. The key components of our infrastructure consist of two networks: a vortex detection network to identify the Lagrangian vortices from a grid-based velocity field and a vortex dynamics network to learn the underlying governing interactions of these finite structures. By embedding these two networks with a vorticity-to-velocity Poisson solver and training its parameters using the fluid data obtained from grid-based numerical simulation, we can predict the accurate fluid dynamics on a precision level that was infeasible for all the previous conventional vortex methods. We demonstrate the efficacy of our method in generating highly a

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Author

Shiying Xiong; Xingzhe He; Yunjin Tong; Yitong Deng; Bo Zhu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)