Advances in thermal barrier coatings modeling, simulation, and analysis: A review

Abstract

This review presents an approach to traditional and advanced modeling, simulation, and analysis techniques in the context of thermal barrier coatings (TBCs), augmented by AI technology. The aim is to comprehensively comprehend TBC behavior, optimize designs, and facilitate proactive maintenance strategies. The discussed methods include thermal conductivity modeling, thermal stress simulation, crack propagation, and mechanical behavior analysis in TBC systems, employing diverse approaches such as effective medium theories, molecular dynamics simulations, computational fluid dynamics, multiscale modeling, and finite element analysis. Notably, the review underscores the burgeoning application of AI in TBC simulations, exploring various machine learning algorithms such as support vector machines, random forests, neural networks, deep learning methodologies, and data-driven modeling techniques. The findings posit that AI harbors significant potential to revolutionize TBCs across multiple domains, encompassing material and structural design, performance optimization and prediction, monitoring, prognostics and health management, and the enhancement of remaining useful life predictions.