This work presents a hierarchical adaptive mesh generation strategy for parametric surfaces based on tree structures. The error measured between the analytical and discrete curvatures guides the adaptive process. While the analytical curvature is an mathematical representation that models the domain, the discrete curvature is an approximation of that curvature and depends directly on the used mesh configuration. The presented strategy possess the following properties: it is able to refine and coarsen regions of the mesh; it ensures compatibility between neighbouring regions; it considers the contribution of the local error measures to ensure good global mesh quality and it works with any type of parametric surface geometry, since the process is performed in the parametric space.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.