Direct point-cloud-based numerical analysis using octree meshes

Abstract

Point-cloud-based geometry acquisition techniques have seen an increased deployment in recent years and can be used in many branches of science and engineering. Different from conventional computer aided design (CAD) models, point-cloud data can be directly obtained by applying surveying technology, such as laser scanning devices. This opens a pathway to an efficient and highly automatic acquisition of the geometric model. However, in order to perform numerical analysis on point-cloud models using conventional finite element approaches, a surface reconstruction is required, which is both time consuming and error-prone. Therefore, a direct numerical analysis framework based on point-cloud data in conjunction with polyhedral element techniques is developed in this contribution. To this end, a robust and efficient meshing paradigm based on the direct use of point-cloud data (without surface reconstruction) is proposed. From the geometric model, a trimmed octree mesh containing only a limited number of master elements (element patterns) is directly generated, which is highly suitable for pre-computation techniques and parallelization (high-performance computing–HPC). By computing the element stiffness and mass matrices from pre-computed master elements via scaling, a fully automatic HPC framework is developed. By means of six numerical examples, we demonstrate the robustness, versatility, and efficiency of the developed methodology in handling (geometrically) complex engineering problems.