Investigation on the effect of density ratio on the convergence behavior of partitioned method for fluid-structure interaction simulation

被引:7
作者
Ha, Sang Truong [1 ]
Choi, Hyoung Gwon [2 ]
机构
[1] Seoul Natl Univ Sci & Technol, Dept Mech Engn, Seoul, South Korea
[2] Seoul Natl Univ Sci & Technol, Dept Mech & Automot Engn, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
Fluid-structure interaction; Strong-coupling partitioned algorithm; Finite element method; Density ratio; Mooney-Rivlin model; FINITE-ELEMENT FORMULATION; BLOOD-FLOW; COUPLING TECHNIQUE; ALGORITHMS; SOLVERS; MODEL;
D O I
10.1016/j.jfluidstructs.2020.103050
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In order to investigate the effect of density ratio of fluid and solid on the convergence behavior of partitioned FSI algorithm, three strong-coupling partitioned algorithms (fixed-point method with a constant under-relaxation parameter, Aitken's method and Quasi-Newton inverse least squares (QN-ILS) method) have been considered in the context of finite element method. We have employed the incompressible Navier-Stokes equations for a Newtonian fluid domain and the total Lagrangian formulation for a nonlinear motion of solid domain. Linear-elastic (hyper-elastic) model has been employed for solid material with small (large) deformation. A pulsatile inlet-flow interacting with a 2D circular channel of linear-elastic material and a pressure wave propagation in a 3D flexible vessel have been simulated. Both linear-elastic and hyper-elastic (Mooney-Rivlin) models have been adopted for the 3D flexible vessel. From the present numerical experiments, we have found that QN-ILS outperforms the others leading to a robust convergence regardless of the density ratio for both linear-elastic and hyper-elastic models. On the other hand, the performances of the fixed-point method with a constant under-relaxation parameter and the Aitken's method depend strongly on the density ratio, relaxation parameter selected for coupling iteration, and degree of deformation. Although the QN-ILS of this work is still slower than a monolithic method for serial computation, it has an advantage of easier parallelization due to the modularity of the partitioned FSI algorithm. (C) 2020 Published by Elsevier Ltd.
引用
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页数:20
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