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Locking Free and Gradient Robust H(div)-conforming HDG Methods for Linear Elasticity

Guosheng FuChristoph LehrenfeldAlexander LinkeTimo Streckenbach

出版	Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2020
URL	http://books.google.com.hk/books?id=oXCwzQEACAAJ&hl=&source=gbs_api

註釋

Robust discretization methods for (nearly-incompressible) linear elasticity are free of volumelocking and gradient-robust. While volume-locking is a well-known problem that can be dealt with in many different discretization approaches, the concept of gradient-robustness for linear elasticity is new. We discuss both aspects and propose novel Hybrid Discontinuous Galerkin (HDG) methods for linear elasticity. The starting point for these methods is a divergence-conforming discretization. As a consequence of its well-behaved Stokes limit the method is gradient-robust and free of volume-locking. To improve computational efficiency, we additionally consider discretizations with relaxed divergence-conformity and a modification which re-enables gradient-robustness, yielding a robust and quasi-optimal discretization also in the sense of HDG superconvergence.