Stress corrosion cracking (SCC) typically involves rupture of protective oxide film, growth of pits, pit-to-crack transition, and crack propagation. Furthermore, mechanical straining-induced film rupture followed by localized metal dissolution could be a possible explanation for pitting corrosion and SCC. Following every instance of oxide film rupture, there is the formation of a new film, which is often described as repassivation. In the presence of higher mechanical strain, this new film ruptures again, and the cycle of film rupture-dissolution-repassivation (FRDR) begins. In this complex and coupled problem, mechanical loading, properties at grain boundaries, and an aggressive corrosive environment strongly affect pit growth and crack propagation. Consequently, there is a dynamic relationship between mechanical strain and oxide films that results in intergranular SCC (IGSCC) and transgranular SCC (TGSCC) in a polycrystalline material, a subject of recent interest for researchers. In addition, the analysis of failure by SCC necessitates a detailed as well as specific modelling technique that takes into account scaling effects in both time and space, addresses the consequences of various events, and maintains coupled mechanistic interaction. Simulating such complex coupled processes is very expensive and limited by computational capabilities. In this work, a phase-field approach along with a partitioned multi-physics computational setup is presented, using two separate single physics solvers coupled by an open-source coupling library, preCICE [1]. The model includes the activation-controlled SCC with the FRDR mechanism and serves as an extension to the previous work [2]. In the proposed computational setup, two separate software environments are used with dedicated solver settings and different time steps to simulate mechanical fracture and dissolution-driven pitting corrosion under various loading and corrosion conditions. The proposed model is demonstrated through several numerical examples using a 2D polycrystalline model to predict the evolution of both IGSCC and TGSCC in an efficient manner.

References:

[1] Chourdakis et. al (2022): https://doi.org/10.12688/openreseurope.14445.2

[2] Kandekar et. al (2022): https://doi.org/10.1002/pamm.202200211