# Gradient recovery for elliptic interface problem: III. Nitsche's method

**Authors:** Hailong Guo, Xu Yang

arXiv: 1705.03617 · 2018-01-17

## TL;DR

This paper introduces a Nitsche's method-based gradient recovery technique for elliptic interface problems that maintains high accuracy near interfaces, overcoming issues caused by skinny triangles in unfitted meshes.

## Contribution

It proposes a novel gradient recovery method using Nitsche's method, ensuring superconvergence and improved gradient accuracy near interfaces in unfitted mesh scenarios.

## Key findings

- Achieves superconvergence of gradient recovery
- Maintains accuracy near interfaces with skinny triangles
- Validated by numerical examples

## Abstract

This is the third paper on the study of gradient recovery for elliptic interface problem. In our previous works [H. Guo and X. Yang, 2016, arXiv:1607.05898 and {\it J. Comput. Phys.}, 338 (2017), 606--619], we developed {gradient recovery methods} for elliptic interface problem based on body-fitted meshes and immersed finite element methods. Despite the efficiency and accuracy that these methods bring to recover the gradient, there are still some cases in unfitted meshes where skinny triangles appear in the generated local body-fitted triangulation that destroy the accuracy of recovered gradient near the interface. In this paper, we propose a gradient recovery technique based on Nitsche's method for elliptic interface problem, which avoids the loss of accuracy of gradient near the interface caused by skinny triangles. We analyze the supercloseness between the gradient of the numerical solution by the Nitsche's method and the gradient of interpolation of the exact solution, which leads to the superconvergence of the proposed gradient recovery method. We also present several numerical examples to validate the theoretical results.

## Full text

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## Figures

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## References

63 references — full list in the complete paper: https://tomesphere.com/paper/1705.03617/full.md

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Source: https://tomesphere.com/paper/1705.03617