Qian Guofeng, Tantratian Karnpiwat, Chen Lei, Hu Zhen, Todd Michael D
Department of Structural Engineering, University of California, San Diego, CA, 92093-0085, USA.
Department of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI, 48128-1491, USA.
Sci Rep. 2022 Dec 3;12(1):20898. doi: 10.1038/s41598-022-25477-8.
Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework.
腐蚀会引发裂纹，从而导致结构完整性降低。定量腐蚀评估具有挑战性，而腐蚀诱导裂纹萌生的建模对于各种结构基于模型的腐蚀可靠性分析至关重要。本文通过将相场模型与机器学习和不确定性量化相结合，提出了一种用于腐蚀到裂纹转变的概率计算分析框架。对一个电化学机械相场模型进行了修改，以预测点蚀演化，其中应力被适当地耦合到电极化学势中。提出了一种基于形态学的裂纹萌生准则来量化点蚀到裂纹的转变。为此开发了一种时空代理建模方法，该方法由一个卷积神经网络（CNN）组成，用于将腐蚀形态映射到潜在空间，以及一个具有非线性自回归外源模型（NARX）架构的高斯过程回归模型，用于预测潜在空间中随时间的腐蚀动力学。它能够实时预测腐蚀形态和裂纹萌生行为（腐蚀损伤是否、何时以及何处引发裂纹萌生），从而使得进行概率分析并量化不确定性成为可能。给出了在各种应力和腐蚀条件下的示例，以证明所提出的计算框架。
