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Optimizing anode location in impressed current cathodic protection system to minimize underwater electric field using multiple linear regression analysis and artificial neural network methods

机译:使用多重线性回归分析和人工神经网络方法优化外加电流阴极保护系统中的阳极位置,以最小化水下电场

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摘要

Design of impressed current cathodic protection (ICCP)-anode locations to minimize underwater electric field is important to increase the survivability of naval ships. However, the evaluation of all ICCP-anode location scenarios is time-consuming process even though it is conducted by numerical simulation, especially boundary element method (BEM). To solve this problem, the randomly selected cases of ICCP-anode location in 3, 4 and 5-pair, which were produced by the beta distribution model, were simulated using BEM simulations. Then, the optimized ICCP-anode location scenario from the cases was verified by statistical methods (multiple linear regression and artificial neural network). Predictions of ICCP-anode location and underwater electric field were more correlated with the artificial neural network than the multiple linear regression analysis. Also, the selected ICCP-anode locations were verified by the artificial neural network considering data interactions. Thus, the application of an artificial neural network can be more useful for designing ICCP-anode location that minimizes underwater electric fields.
机译:设计外加电流阴极保护(ICCP)阳极位置以最小化水下电场对提高海军舰艇的生存能力很重要。但是,所有ICCP阳极位置方案的评估都是耗时的过程,即使它是通过数值模拟(尤其是边界元方法(BEM))进行的。为了解决这个问题,使用BEM模拟了由beta分布模型产生的3、4和5对ICCP阳极位置随机选择的情况。然后,通过统计方法(多元线性回归和人工神经网络)验证了案例中优化的ICCP阳极位置方案。与多元线性回归分析相比,ICCP阳极位置和水下电场的预测与人工神经网络的相关性更高。此外,考虑到数据交互作用,通过人工神经网络验证了选定的ICCP阳极位置。因此,人工神经网络的应用对于设计可最大程度降低水下电场的ICCP阳极位置可能更为有用。

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