Multiscale modeling and optimization of thermal barrier coatings for interface conditions

Abstract

Thermal barrier coatings (TBCs) are crucial in protecting components exposed to high-temperature environments; however, current design methods often rely on fragmented, single-scale models that lack predictive accuracy. This study presents an integrated multiscale modeling framework that combines molecular dynamics (MD), continuum mechanics, and finite element analysis (FEA) with AI-enhanced optimization to simulate and optimize the thermal, mechanical, and chemical performance of thermal barrier coatings (TBCs). Atomic-scale interactions were evaluated using molecular dynamics (MD) simulations, whose outputs informed continuum-scale stress-strain relationships, which were subsequently applied in finite element analysis (FEA) to model real-world thermal gradients and loading. AI algorithms were employed to refine parameter selection and accelerate convergence analysis. The approach demonstrated a 30 % improvement in thermal insulation efficiency, a 40 % increase in scratch resistance, and a 50 % enhancement in corrosion resistance compared to conventional models. Mesh refinement reduced FEM simulation error margins from 10 % to under 2 %, confirming the model's reliability. Results were validated against benchmark experimental data from peer-reviewed sources, aligning within ±5 % of reported values. This multiscale framework provides a powerful tool for predictive coating design, offering manufacturers a cost-effective and accurate means to develop next-generation thermal barrier coatings (TBCs) that can withstand extreme interface conditions across aerospace, energy, and automotive applications.