Localized evaluation and fast summation in the extrapolated regularization method for integrals in Stokes flow

TL;DR

This paper enhances the extrapolated regularization method for Stokes flow integrals by introducing parallelization and a treecode to reduce computational costs, enabling efficient and accurate evaluation of nearly singular integrals in boundary integral methods.

Contribution

It applies parallelization, local evaluation, and a kernel-independent treecode to improve the efficiency of the extrapolated regularization method for Stokes flow integrals.

Findings

Achieved fifth-order accuracy in integral evaluation.

Reduced computational cost using parallelization and treecode.

Successfully computed flow around nearly touching spheres.

Abstract

Boundary integral equation methods are widely used in the solution of many partial differential equations. The kernels that appear in these surface integrals are nearly singular when evaluated near the boundary, and straightforward numerical integration produces inaccurate results. In Beale and Tlupova (Adv. Comput. Math, 2024), an extrapolated regularization method was proposed to accurately evaluate the nearly singular single and double-layer surface integrals for harmonic potentials or Stokes flow. The kernels are regularized using a smoothing parameter, and then a standard quadrature is applied. The integrals are computed for three choices of the smoothing parameter to find the extrapolated value to fifth order accuracy. In this work, we apply several techniques to reduce the computational cost of the extrapolated regularization method applied to the Stokes single and double layer integrals. First, we use a straightforward OpenMP parallelization over the target points. Second, we note that the effect of the regularization is local and evaluate only the local component of the sum for three values of the smoothing parameter. The non-local component of the sum is only evaluated once and reused in the other sums. This component is still the computational bottleneck as it is $O(N^2)$, where $N$ is the system size. We apply the kernel-independent treecode to these far-field interactions to reduce the CPU time. We carry out experiments to determine optimal parameters both in terms of accuracy and efficiency of the computations. We then use these techniques to compute Stokes flow around two spheres that are nearly touching.