1. Node's residual descent method for linear elastic boundary value problems.
- Author
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Dong, Tailang and Cui, Yuhong
- Subjects
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BOUNDARY value problems , *PARTIAL differential equations , *FINITE differences , *SPARSE matrices , *MATRIX multiplications - Abstract
• Node's residual descent method (NRDM) for linear elasticity boundary value problem. • Solved the second-order PDE boundary value problem with the first-order GFD scheme. • The NRDM eliminates the task of assembling large global sparse matrices. • The computational accuracy and convergence rate are greatly improved in the NRDM. • The NRDM retain all the advantages of GFDM. This study proposes a node's residual descent method (NRDM) for the linear elasticity boundary value problem. In this method, the boundary information and deformation states are attached to the nodes, and the partial differential equations (PDEs) are then transformed into a generalized difference form with the first-order generalized finite difference (GFD) scheme. Furthermore, this study thoroughly describes the NRDM, confirms the accuracy and convergence by numerical examples, performs an error analysis, and discusses the influence of computational parameters. Results demonstrate that NRDM has high computational accuracy and superliner convergence rate. The double derivation is implemented to solve the second-order PDE boundary value problem with the first-order GFD scheme, thus simplifying the requirement for star connectivity and reducing the computational complexity. NRDM is a matrix-free iterative method that eliminates the task of assembling large global sparse matrices, thus avoiding the numerous matrix–matrix product operations. Rather than solving large systems of linear equations, many extremely small matrix multiplications are present during iterations, which are extremely friendly to parallel computing and can be adapted to the computation of ultra-multi-degree-of-freedom problems. The computational accuracy and convergence rate are considerably improved in NRDM compared to the classical GFD method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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