A superconvergent HDG method for Stokes flow with strongly enforced symmetry of the stress tensor

Author (s): Giacomini, M., Karkoulias, A., Sevilla, R. and Huerta A.
Journal: Journal of Scientific Computing

Volume: 77, Issue 3
Pages: 1679 – 1702
Date: 2018

Abstract:
This work proposes a superconvergent hybridizable discontinuous Galerkin (HDG) method for the approximation of the Cauchy formulation of the Stokes equation using same degree of polynomials for the primal and mixed variables. The novel formulation relies on the well-known Voigt notation to strongly enforce the symmetry of the stress tensor. The proposed strategy introduces several advantages with respect to the existing HDG formulations. First, it remedies the suboptimal behavior experienced by the classical HDG method for formulations involving the symmetric part of the gradient of the primal variable. The optimal convergence of the mixed variable is retrieved and an element-by-element postprocess procedure leads to a superconvergent velocity field, even for low-order approximations. Second, no additional enrichment of the discrete spaces is required and a gain in computational efficiency follows from reducing the quantity of stored information and the size of the local problems. Eventually, the novel formulation naturally imposes physical tractions on the Neumann boundary. Numerical validation of the optimality of the method and its superconvergent properties is performed in 2D and 3D using meshes of different element types.

Journal paper (from the publisher)  
Download file (0 MBytes)  

Bibtex:

@article{MG-GKSH:18,
        Author = {Matteo Giacomini and Alexandros Karkoulias and 
                  Ruben Sevilla and Antonio Huerta},
        Title = {A superconvergent {HDG} method for {S}tokes flow 
                 with strongly enforced symmetry of the stress tensor},
        Fjournal = {Journal of Scientific Computing},
        Journal = {J. Sci. Comput.},
        Volume = {77},
        Number = {3},
        Pages = {1679--1702},
        Year = {2018},
        Doi = {10.1007/s10915-018-0855-y},
        }