The adjoint double layer potential on smooth surfaces in \(\mathbb {R}^3\) and the Neumann problem

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
J. Thomas Beale, Michael Storm, Svetlana Tlupova
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Abstract

We present a simple yet accurate method to compute the adjoint double layer potential, which is used to solve the Neumann boundary value problem for Laplace’s equation in three dimensions. An expansion in curvilinear coordinates leads us to modify the expression for the adjoint double layer so that the singularity is reduced when evaluating the integral on the surface. Then, to regularize the integral, we multiply the Green’s function by a radial function with length parameter \(\delta \) chosen so that the product is smooth. We show that a natural regularization has error \(O(\delta ^3)\), and a simple modification improves the error to \(O(\delta ^5)\). The integral is evaluated numerically without the need of special coordinates. We use this treatment of the adjoint double layer to solve the classical integral equation for the interior Neumann problem, altered to account for the solvability condition, and evaluate the solution on the boundary. Choosing \(\delta = ch^{4/5}\), we find about \(O(h^4)\) convergence in our examples, where h is the spacing in a background grid.

$$\mathbb{R}^3$$中光滑表面上的邻接双层势和诺伊曼问题
我们提出了一种简单而精确的计算邻接双层势的方法,用于解决三维拉普拉斯方程的诺伊曼边界值问题。通过在曲线坐标中展开,我们修改了邻接双层的表达式,从而减少了在曲面上求取积分时的奇异性。然后,为了正则化积分,我们将格林函数乘以长度参数为 \(\delta \)的径向函数,使乘积平滑。我们证明,自然正则化的误差为(O(\delta ^3)\),而简单的修改可以将误差提高到(O(\delta ^5)\)。无需特殊坐标就能对积分进行数值计算。我们使用这种对邻接双层的处理方法来求解内部诺依曼问题的经典积分方程,并考虑到可解条件,对边界上的解进行评估。选择 \(\delta = ch^{4/5}\),我们发现在我们的例子中大约 \(O(h^4)\) 收敛,其中 h 是背景网格的间距。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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