Defining metric-aware size-shape measures to validate and optimize curved high-order meshes

We define a regularized size-shape distortion (quality) measure for curved high-order elements on a Riemannian space. To this end, we measure the deviation of a given element, straight-sided or curved, from the stretching, alignment, and sizing determined by a target metric. The defined distortion (...

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Detalles Bibliográficos
Autores: Aparicio Estrems, Guillermo|||0000-0002-1956-0409, Gargallo Peiró, Abel|||0000-0003-3742-2197, Roca Navarro, Francisco Javier
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/409814
Acceso en línea:https://hdl.handle.net/2117/409814
https://dx.doi.org/10.1016/j.cad.2023.103667
Access Level:acceso abierto
Palabra clave:Computational geometry
Mesh optimization
r-adaptation
Curved high-order meshes
Geometria computacional
Àrees temàtiques de la UPC::Matemàtiques i estadística::Geometria::Geometria computacional
Descripción
Sumario:We define a regularized size-shape distortion (quality) measure for curved high-order elements on a Riemannian space. To this end, we measure the deviation of a given element, straight-sided or curved, from the stretching, alignment, and sizing determined by a target metric. The defined distortion (quality) is suitable to check the validity and the quality of straight-sided and curved elements on Riemannian spaces determined by constant and point-wise varying metrics. The examples illustrate that the distortion can be minimized to curve (deform) the elements of a given high-order (linear) mesh and try to match with curved (linear) elements the point-wise stretching, alignment, and sizing of a discrete target metric tensor. In addition, the resulting meshes simultaneously match the curved features of the target metric and boundary. Finally, to verify if the minimization of the metric-aware size-shape distortion leads to meshes approximating the target metric, we compute the Riemannian measures for the element edges, faces, and cells. The results show that, when compared to anisotropic straight-sided meshes, the Riemannian measures of the curved high-order mesh entities are closer to unit. Furthermore, the optimized meshes illustrate the potential of curved ¿-adaptation to improve the accuracy of a function representation.