How a nonconvergent recovered Hessian works in mesh adaptation

TL;DR

This paper explains how nonconvergent Hessian recovery methods can still effectively guide mesh adaptation in finite element analysis, leading to optimal error despite their lack of convergence.

Contribution

It develops an error bound for finite element solutions using a recovered Hessian and demonstrates the practical effectiveness of nonconvergent Hessians in mesh adaptation.

Findings

Recovered Hessian is often nonconvergent but still effective in mesh adaptation.

Finite element error depends gradually on the closeness of the recovered Hessian to the exact Hessian.

Numerical results confirm the theoretical error bounds and effectiveness of common Hessian recovery methods.

Abstract

Hessian recovery has been commonly used in mesh adaptation for obtaining the required magnitude and direction information of the solution error. Unfortunately, a recovered Hessian from a linear finite element approximation is nonconvergent in general as the mesh is refined. It has been observed numerically that adaptive meshes based on such a nonconvergent recovered Hessian can nevertheless lead to an optimal error in the finite element approximation. This also explains why Hessian recovery is still widely used despite its nonconvergence. In this paper we develop an error bound for the linear finite element solution of a general boundary value problem under a mild assumption on the closeness of the recovered Hessian to the exact one. Numerical results show that this closeness assumption is satisfied by the recovered Hessian obtained with commonly used Hessian recovery methods. Moreover, it is shown that the finite element error changes gradually with the closeness of the recovered Hessian. This provides an explanation on how a nonconvergent recovered Hessian works in mesh adaptation.