“Mixed” Meshless Time-Domain Adaptive Algorithm for Solving Elasto-Dynamics Equations

Maoxiong Liao, Tao Zhang and Jinggu Cao
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Maoxiong Liao: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Tao Zhang: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Jinggu Cao: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

Mathematics, 2022, vol. 10, issue 10, 1-24

Abstract: A time-domain adaptive algorithm was developed for solving elasto-dynamics problems through a mixed meshless local Petrov-Galerkin finite volume method (MLPG5). In this time-adaptive algorithm, each time-dependent variable is interpolated by a time series function of n-order, which is determined by a criterion in each step. The high-order series of expanded variables bring high accuracy in the time domain, especially for the elasto-dynamic equations, which are second-order PDE in the time domain. In the present mixed MLPG5 dynamic formulation, the strains are interpolated independently, as are displacements in the local weak form, which eliminates the expensive differential of the shape function. In the traditional MLPG5, both shape function and its derivative for each node need to be calculated. By taking the Heaviside function as the test function, the local domain integration of stiffness matrix is avoided. Several numerical examples, including the comparison of our method, the MLPG5–Newmark method and FEM (ANSYS) are given to demonstrate the advantages of the presented method: (1) a large time step can be used in solving a elasto-dynamics problem; (2) computational efficiency and accuracy are improved in both space and time; (3) smaller support sizes can be used in the mixed MLPG5.

Keywords: meshless local Petrov-Galerkin approach (MLPG); finite volume methods; mixed methods; adaptive algorithm; time-domain; moving least squares (MLS) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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