In this work, we present a new a posteriori error estimator based on the Variational Multiscale method for anisotropic adaptive fluid mechanics problems. The general idea is to combine the large scale error based on the solved part of the solution with the sub-mesh scale error based on the unresolved part of the solution. We compute the latter with two different methods: one using the stabilizing parameters and the other using bubble functions. We propose two different metric tensors H iso and H new aniso . They are both defined by the recovered Hessian matrix of the solution and rely on the sub-grid scale error estimator. Thus, we develop a new anisotropic local error indicator and we test it for mesh adaptation on convection-dominated benchmarks in 2D and 3D. The results show that the proposed error indicator leads to enhanced and accurate solutions while using a drastically reduced number of elements.
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.