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Using the Gaussian function to simulate constant potential anodes in multiobjective optimization of cathodic protection systems

机译：在阴极保护系统的多目标优化中使用高斯函数模拟恒电位阳极

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摘要

The purpose of this work is to numerically find the optimum location of constant potential anodes to ensure complete structure surface protection using a cathodic protection technique. The existence of sacrificial anodes is originally introduced through the boundary conditions of the corresponding boundary value problem (BVP). However, if constant potential galvanic regions are introduced through its boundaries, then finding their optimal location is not an easy task due to the necessity of redefining boundary geometric nodes and the arrangement of virtual sources for the standard method of fundamental solutions (MFS) formulation. Therefore, in this work, the galvanic anodes are introduced as source terms using a Gaussian function. Hence, the boundary remains the same for different anode positions. The optimization process includes the identification of the following parameters characterizing the Gaussian function: the optimum coordinates of the centre of the anode, a factor that involves the inherent potential of the electrode and a proportionality factor for the electrode diameter. The MFS methodology coupled with a genetic algorithm presented good results for this multiobjective optimization procedure. This fact can be seen in the several results of applications that are discussed in this paper, considering numerical simulations in finite regions in R~2.
机译：这项工作的目的是从数值上找到恒电位阳极的最佳位置，以确保使用阴极保护技术对结构进行完整的表面保护。牺牲阳极的存在最初是通过相应边界值问题（BVP）的边界条件引入的。但是，如果通过边界引入恒定电位的电流区域，则由于需要重新定义边界几何节点以及为标准基本解法（MFS）公式化设置虚拟源的必要性，找到其最佳位置并不是一件容易的事。因此，在这项工作中，使用高斯函数将电阳极作为源项引入。因此，对于不同的阳极位置，边界保持相同。优化过程包括识别以下表征高斯函数的参数：阳极中心的最佳坐标，涉及电极固有电位的因子和电极直径的比例因子。 MFS方法与遗传算法相结合，为该多目标优化过程提供了良好的结果。考虑到R〜2中有限区域内的数值模拟，可以在本文讨论的几个应用程序结果中看到这一事实。

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