NANAJan 12, 2018

Robust error estimation for lowest-order approximation of nearly incompressible elasticity

arXiv:1801.041223 citationsh-index: 30
Originality Synthesis-oriented
AI Analysis

Provides robust error estimators for mixed finite element methods in nearly incompressible elasticity, addressing a known bottleneck in computational mechanics.

The paper analyzes error estimation for lowest-order finite element approximations of nearly incompressible elasticity, proving robust a priori and a posteriori error bounds independent of Lamé coefficients, validated by numerical experiments.

We consider so-called Herrmann and Hydrostatic mixed formulations of classical linear elasticity and analyse the error associated with locally stabilised $P_1-P_0$ finite element approximation. First, we prove a stability estimate for the discrete problem and establish an a priori estimate for the associated energy error. Second, we consider a residual-based a posteriori error estimator as well as a local Poisson problem estimator. We establish bounds for the energy error that are independent of the Lamé coefficients and prove that the estimators are robust in the incompressible limit. A key issue to be addressed is the requirement for pressure stabilisation. Numerical results are presented that validate the theory. The software used is available online.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes