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Fast finite element solver for incompressible Navier-Stokes equation by parallel Gram-Schmidt process based GMRES and HSS

机译:基于GMRES和HSS的并行Gram-Schmidt过程的不可压缩Navier-Stokes方程快速有限元求解器

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After finite element discretization of incompressible Navier-Stokes equation, a sparse linear system is obtained in every iteration, and GMRES provides an efficient approach to solve this system. However, if the size of the original PDE model is large, the solution speed of the incompressible Navier-Stokes equation is still slow. Since the linear system from the incompressible Navier-Stokes equation is saddle point problem, we find that large portion of computational efforts for solving the linear system is occupied by the vector projection in GMRES. In this paper, by our parallel Gram-Schmidt process based GMRES and newly developed preconditioner Hermitian/Skew-Hermitian Separation (HSS), we develop a fast solver HSS-pGMRES for the saddle point problem from incompressible Navier-Stokes Equation. Theoretical analysis shows that, HSS-pGMRES decreases the computational complexity of finite element solver for incompressible Navier-Stokes equation from O( m 2 n ) to O( mn ), where m is the grid size. Computational experiments show that, the fast solver HSS-pGMRES significantly increases the solution speed for the saddle point problem of incompressible Navier-Stokes equation than the conventional solvers.
机译:在对不可压缩的Navier-Stokes方程进行有限元离散化之后,每次迭代都会获得一个稀疏线性系统,而GMRES提供了一种有效的方法来求解该系统。但是,如果原始PDE模型的大小较大,则不可压缩的Navier-Stokes方程的求解速度仍然很慢。由于不可压缩的Navier-Stokes方程中的线性系统是鞍点问题,因此我们发现GMRES中的向量投影占据了求解线性系统的大部分计算工作。在本文中,通过基于GMRES的并行Gram-Schmidt过程和新开发的预处理器Hermitian / Skew-Hermitian分离(HSS),我们针对不可压缩的Navier-Stokes方程开发了鞍点问题的快速求解器HSS-pGMRES。理论分析表明,对于不可压缩的Navier-Stokes方程,HSS-pGMRES从O(m 2 n)到O(mn)降低了有限元求解器的计算复杂度,其中m为网格大小。计算实验表明,与常规求解器相比,快速求解器HSS-pGMRES大大提高了不可压缩Navier-Stokes方程鞍点问题的求解速度。

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