Optimization of corrosion cellular automaton model and multiphysics coupling research based on TRIZ innovation theory
Hong Qin, Zihao Tang, Pengyu Wen, Kui Liang, Shaohai Ma, Jing Guo, Yingxue TengPurpose
This study aims to address the critical deficiencies of traditional corrosion cellular automaton (CA) models – excessive reliance on empirical probability-driven mechanisms and insufficient coupling of electrochemical and mechanical effects – by developing an improved multiphysics coupling model based on TRIZ innovation theory.
Design/methodology/approach
TRIZ theory was systematically applied to reconstruct the conventional CA model. Functional and contradiction analyses identified key technical contradictions, which were resolved through innovative principle mapping. The substance-field model and 76 standard solutions optimized the model structure. Corrosion potential replaced empirical probability as the core driving mechanism. The improved model integrates stress, strain, grain structure and local corrosion environment and was validated against experimental data from Cr–Mn–N–Mo austenitic stainless steels in 6% FeCl3 solution.
Findings
The improved model successfully transforms corrosion simulation from probability-driven to mechanism-driven logic. Corrosion potential as the core criterion aligns the triggering mechanism with electrochemical principles. Multiphysics coupling enables accurate simulation of microstructural changes during corrosion. Model predictions show good agreement with experimental results across three different steel compositions.
Originality/value
This study pioneers the systematic application of TRIZ to optimize corrosion CA models. Replacing empirical probability with corrosion potential represents a fundamental shift in simulation logic. Integrating stress–strain fields, grain boundaries and local environmental feedback into a unified CA framework provides a novel approach for multiphysics corrosion simulation.