Parallel Efficient Mesh Deformation Method Based On Support Vector Regression

Abstract

Mesh deformation method is widely used in unsteady numerical simulations involving moving boundaries. This kind of method redistributes the position of grid points in accordance with the movement of the computational domain without changing their connectivity relations. In this paper, we present a parallel mesh deformation method based on the support vector machine regression (SVR). In each time step, the proposed method first trains three SVRs by the coordinates of the boundary points and their known displacements in each direction, and then predicts the displacements of the internal points using the SVRs. After deforming the mesh, the dual-time step flow solver is used to solve the governing equations. To ensure the consistency of the method running in parallel, the training part of the method is executed with all global boundary points in each decomposed domain. Therefore, each CPU needs to maintain a copy of the entire boundary points via a point-to-point communication. The internal evaluation of the method is predicted separately in each decomposed domain without any data dependency. An oscillatory and transient pitching airfoil case is simulated to demonstrate the applicability of the proposed mesh deformation method, and its parallel efficiency is over 60% with 64 cores.