Journal
COMPUTERS & STRUCTURES
Volume 268, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compstruc.2022.106829
Keywords
Finite Volume Method; Unstructured meshes; High-order schemes; Local Regression Estimators; Linear elasticity; Shear locking
Funding
- Universitat Politecnicade Catalunya and Banco Santander [FPI-UPC (109 FPI-UPC 2018)]
- Uruguayan research councils ANII
- CSIC
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This paper presents a high-order finite volume method using Moving Least Squares (MLS) and Local Regression Estimators (LRE) for solving linear elasticity problems on two-dimensional unstructured meshes. The method effectively solves structural problems affected by shear locking and demonstrates accuracy and flexibility through canonical tests and analytical examples.
This paper presents a high-order finite volume method for solving linear elasticity problems on two-dimensional unstructured meshes. The method is designed to increase the effectiveness of finite volume methods in solving structural problems affected by shear locking. The particular feature of the proposed method is the use of Moving Least Squares (MLS) and Local Regression Estimators (LRE). Unlike other approaches proposed before, these interpolation schemes lead to a natural and simple extension of the classical finite volume method to arbitrary order. The unknowns of the problem are still the nodal values of the displacement which are obtained implicitly in a direct solution strategy. Some canonical tests are performed to demonstrate the accuracy of the method. An analytical example is considered to evaluate the sensitivity of the solution concerning the parameters of the algorithm. A thin curved beam and a crack problem are considered to show that the method can deal with the shear locking effect, stress concentrations, and geometries where unstructured meshes are required. An overall better behavior of the LRE is observed. A comparison between low and high-order schemes is presented, and a set of parameters for the interpolation method is found, delivering good results for the proposed cases. (c) 2022 Elsevier Ltd. All rights reserved.
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