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High-order finite volume method for linear elasticity on unstructured meshes
被引:1
|作者:
Castrillo, Pablo
[1
,2
]
Canelas, Alfredo
[2
]
Schillaci, Eugenio
[1
,3
]
Rigola, Joaquim
[1
]
Oliva, Asensio
[1
]
机构:
[1] Univ Politecn Catalunya BARCELONA TECH UPC, Ctr Tecnol Transferencia Calor CTTC, Colom 11, Terrassa 08222, Spain
[2] Univ Republica, Fac Ingn, Inst Estruct & Transporte, Julio Herrera & Reissig 565, Montevideo 11300, Uruguay
[3] Termofluids, Ave Jacquard 97-E, Terrassa 08227, Barcelona, Spain
关键词:
Finite Volume Method;
Unstructured meshes;
High-order schemes;
Local Regression Estimators;
Linear elasticity;
Shear locking;
MOVING LEAST-SQUARES;
NAVIER-STOKES EQUATIONS;
ALGORITHM;
ELEMENT;
D O I:
10.1016/j.compstruc.2022.106829
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
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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