Corrosion-induced material deterioration poses a pervasive threat to structural integrity, necessitating an in-depth understanding of its intricate behaviors. Pitting corrosion, a critical concern in this context, accelerates the degradation of materials. The limitations of conventional models arise from their neglect of the subsurface electrode boundary layer dynamics during the dissolution process. In this study, we present a novel approach that combines Peridynamics (PD) diffusion framework with machine learning (ML) techniques to develop an efficient predictive model and computational efficiency. The proposed hybrid PD-ML model leverages the non-local effects inherent to Peridynamics and the pattern recognition capabilities of machine learning. It establishes an analytical connection between the concentration value at a specific material point and the concentrations exhibited by related constituents within its spatial horizon, considering the external mass flux applied. The adaptability of the model is achieved through the utilization of weighted regression coefficients, determined via multivariate linear regression. Validation against experiments and conventional PD model demonstrates the model's precision and efficiency using diverse micro-diffusivity scenarios. For 1D uniform and 2D pitting corrosion cases, our hybrid model yields precise concentration predictions while showcasing a remarkable improvement in computational speed compared to conventional approaches. Specifically, the hybrid model achieves an impressive speedup, approximately 4 times faster per time step and 2.5 times faster overall simulation. The study presents a promising tool for predicting corrosion-induced material deterioration in practical systems, offering accuracy, efficiency, and potential for broader applications.

中文翻译：

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协同方法：用于高效点蚀仿真的近场动力学和机器学习回归


腐蚀引起的材料劣化对结构完整性构成了普遍的威胁，需要深入了解其复杂的行为。点蚀是在这种情况下的一个关键问题，它加速了材料的降解。传统模型的局限性在于它们在溶解过程中忽视了地下电极边界层动力学。在这项研究中，我们提出了一种将近场动力学 （PD） 扩散框架与机器学习 （ML） 技术相结合的新方法，以开发高效的预测模型和计算效率。提出的混合 PD-ML 模型利用了 Peridynamics 固有的非局部效应和机器学习的模式识别能力。考虑到施加的外部质量通量，它在特定物质点的浓度值与相关成分在其空间视界内表现出的浓度之间建立了解析联系。模型的适应性是通过使用加权回归系数来实现的，加权回归系数是通过多元线性回归确定的。根据实验和传统 PD 模型进行的验证证明了该模型使用各种微扩散场景的精度和效率。对于 1D 均匀和 2D 点蚀情况，我们的混合模型可以产生精确的浓度预测，同时与传统方法相比，计算速度有了显著提高。具体来说，混合模型实现了令人印象深刻的加速，每个时间步长的速度提高了约 4 倍，整体仿真速度提高了 2.5 倍。 该研究为预测实际系统中腐蚀引起的材料劣化提供了一种很有前途的工具，为更广泛的应用提供了准确性、效率和潜力。