High-order finite volume method for linear elasticity on unstructured meshes

Autor: Pablo Castrillo, Alfredo Canelas, Eugenio Schillaci, Joaquim Rigola, Asensio Oliva
Přispěvatelé: Universitat Politècnica de Catalunya. Doctorat en Enginyeria Tèrmica, Universitat Politècnica de Catalunya. Departament de Màquines i Motors Tèrmics, Universitat Politècnica de Catalunya. CTTC - Centre Tecnològic de la Transferència de Calor
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Popis: 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. P. Castrillo gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of his predoctoral grant FPI-UPC (109 FPI-UPC 2018). A. Canelas thanks the Uruguayan research councils ANII and CSIC for the financial support.
Databáze: OpenAIRE