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25 May 2023 Influence of implicit iterative method on convergence rate of SIMPLE algorithm
Hangbo Li
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Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120F (2023) https://doi.org/10.1117/12.2679045
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China
Abstract
In the application of SIMPLE algorithm, the generation and solving of linear equations with sparse coefficient matrix appear. Gauss-Seidel and ILU iteration are commonly used to solve the problem. Combined with the common characteristics of LU-SGS time advance method and ILU iterative method, the numerical iterative solutions of sparse matrix linear equations are studied. An iterative LU-SGS algorithm for solving general sparse matrix is proposed. Using C++ as programming language, a special equation solving library is constructed by using three-point data storage structure of multi-dimensional column vector and sparse matrix dot product algorithm. Combined with SIMPLE algorithm based on pressure, the transient NS equation in static grid is simulated numerically. Compared with Gauss-Seidel iterative method, this algorithm has the advantages of accurate calculation and fast convergence, so it has certain application value and reference value.
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Hangbo Li "Influence of implicit iterative method on convergence rate of SIMPLE algorithm", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120F (25 May 2023); https://doi.org/10.1117/12.2679045
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KEYWORDS
Matrices
Iterative methods
Error analysis
Data storage
Algorithm development
Data modeling
Stochastic processes
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