Smart Cloud Collocation: Geometry-Aware Adaptivity Directly From CAD. (January 2023) - Jacquemin, Thibault; Suchde, Pratik; Bordas, Stéphane P.A.
Abstract: Computer Aided Design (CAD) is widely used in the creation and optimization of various industrial systems and processes. Transforming a CAD geometry into a computational discretization that be used to solve PDEs requires care and a deep knowledge of the selected computational method. In this article, we present a novel integrated collocation scheme based on smart clouds. It allows us to transform a CAD geometry into a complete point collocation model, aware of the base geometry, with minimum effort. For this process, only the geometry of the domain, in the form of a STEP file, and the boundary conditions are needed. We also introduce an adaptive refinement process for the resultant smart cloud using an a posteriori error indication. The scheme can be applied to any 2D or 3D geometry, to any PDE and can be applied to most point collocation approaches. We illustrate this with the meshfree Generalized Finite Difference (GFD) method applied to steady linear elasticity problems. We further show that each step of this process, from the initial discretization to the refinement strategy, is connected and is affected by the approach selected in the previous step, thus requiring an integrated scheme where the whole solution process should be considered at once. Highlights: CAD files are used to generate smart point clouds without any intermediate step. Exact normal vectors and boundary conditions are based on the CAD geometry. ZZ-type error indicators are the basis for model adaptivity and are quick to compute. Model adaptivity speeds up the convergence rate of the solution. Model adaptivity is based on the exact CAD geometry.