Electric Potential Due to a System of Conducting Spheres

TL;DR

This paper develops a comprehensive method for calculating the electric potential of conducting spheres using image charges, introduces a charge normalization technique, and optimizes charge configurations via gradient search for improved electrostatic modeling.

Contribution

It presents a novel normalization method for truncated image charge series and an optimization approach for charge placement, enhancing electrostatic solution accuracy.

Findings

Normalized charge series improve field approximation at low orders

Charge optimization yields better potential solutions than standard truncated series

Gradient search effectively refines charge configurations for multiple spheres

Abstract

Equations describing the complete series of image charges for a system of conducting spheres are presented. The method of image charges, originally described by J. C. Maxwell in 1873, has been and continues to be a useful method for solving many three dimensional electrostatic problems. Here we demonstrate that as expected when the series is truncated to any finite order N, the electric field resulting from the truncated series becomes qualitatively more similar to the correct field as N increases. A method of charge normalization is developed which provides significant improvement for truncated low order solutions. The formulation of the normalization technique and its solution via a matrix inversion has similarities to the method of moments, which is a numerical solution of Poisson's equation, using an integral equation for the unknown charge density with a known boundary potential. The last section of this paper presents a gradient search method to optimize a set of L point charges for M spheres. This method may use the image charge series to initialize the gradient search. We demonstrate quantitatively how the metric can be optimized by adjusting the locations and amounts of charge for the set of points, and that an optimized set of charges generally performs better than truncated normalized image charges, at the expense of gradient search iteration time.