Physics-based corrosion reliability analysis of miter gates using multi-scale simulations and adaptive surrogate modeling

Abstract

Stress corrosion cracking (SCC) initiation is usually simulated at the mesoscale, and these computations are usually expensive. This is made more computationally challenging or impossible when such simulations are coupled with a macroscale structural model required for reliability analysis, due to the sources of uncertainty from both scales. This paper tackles this computational barrier to perform physics-based corrosion reliability analysis of large structures using mesoscale simulations via a novel, adaptive surrogate modeling framework. A global surrogate model of the structure is first constructed from a finite element (FE) mechanical model to propagate various sources of input uncertainty at the macroscale to the local stress responses. After that, a mesoscale surrogate model is constructed from phase-field (PF) simulations to predict the failure probability of a given location by accounting for uncertainty in both the macroscale and mesoscale models. In order to guarantee the accuracy of the mesoscale surrogate model and reduce the number of PF simulations, an adaptive surrogate modeling method is proposed using importance sampling (IS) and active learning to refine iteratively the surrogate model in critical regions. Corrosion reliability analysis of a miter gate structure is adopted to demonstrate the efficacy of the proposed method. The result shows that the proposed framework can efficiently and accurately generate a failure probability map for a large structure like a miter gate based on computationally expensive mesoscale PF simulations. In addition, the proposed method is more accurate and converges faster than existing surrogate model-based reliability analysis algorithms.