Goal-oriented adaptivity using unconventional error representations

Abstract

In Goal-Oriented Adaptivity (GOA), the error in the Quantity of Interest (QoI) is represented using theerror functions of the direct and adjoint problems. This error representation is subsequently boundedabove by element-wise error indicators that are used to drive optimal refinements. In this work, wepropose to replace, in the error representation, the adjoint problem by an alternative operator. The mainadvantage of the proposed approach is that, when judiciously selecting such alternative operator, thecorresponding upper bound of the error representation becomes sharper, leading to a more efficientGOA.While the method can be applied to a variety of problems, we focused on one-, two- and threedimensionalHelmholtz and one- and two-dimensional convection-dominated diffusion problems. Weshow via extensive numerical experimentation that the upper bounds provided by the alternative errorrepresentations are sharper than the classical ones and lead to a more robust p-adaptive process. Weprovide guidelines for finding operators delivering sharp error representation upper bounds. We furtherextend the results to problems with discontinuous material coefficients. Finally, we consider a sonicLogging-While-Drilling (LWD) problem to illustrate the applicability of the proposed method.