Extrapolated regularization of nearly singular integrals on surfaces

TL;DR

This paper introduces a regularization and extrapolation method for accurately computing nearly singular surface integrals in harmonic potentials and Stokes flow, achieving high-order error reduction near surfaces.

Contribution

It develops a regularization and extrapolation technique that reduces errors in nearly singular integrals to high order, improving accuracy for surface integral evaluations.

Findings

Achieves $O(h^5)$ total error near the surface with constant $rac{\delta}{h}$

Provides a method for error reduction to $O(h^4)$ by choosing $\delta$ proportional to $h^{4/5}$

Extends approach to harmonic potentials with $O(\delta^7)$ regularization, offering smaller errors

Abstract

We present a method for computing nearly singular integrals that occur when single or double layer surface integrals, for harmonic potentials or Stokes flow, are evaluated at points nearby. Such values could be needed in solving an integral equation when one surface is close to another or to obtain values at grid points. We replace the singular kernel with a regularized version having a length parameter $\delta$ in order to control discretization error. Analysis near the singularity leads to an expression for the error due to regularization which has terms with unknown coefficients multiplying known quantities. By computing the integral with three choices of $\delta$ we can solve for an extrapolated value that has regularization error reduced to $O(\delta^5)$, uniformly for target points on or near the surface. In examples with $\delta/h$ constant and moderate resolution we observe total error about $O(h^5)$ close to the surface. For convergence as $h \to 0$ we can choose $\delta$ proportional to $h^q$ with $q < 1$ to ensure the discretization error is dominated by the regularization error. With $q = 4/5$ we find errors about $O(h^4)$. For harmonic potentials we extend the approach to a version with $O(\delta^7)$ regularization; it typically has smaller errors but the order of accuracy is less predictable.