The service life of thermal barrier coatings (TBCs) in calcium magnesium aluminosilicate (CMAS) and molten salt environments has become a key factor in determining engine reliability and safety. The effect of interface roughness (bond coating surface roughness) on the CM (CMAS + molten salt (Na 2 SO 4 + NaCl + NaVO 3 )) corrosion resistance of electron beam physical vapor deposition (EB-PVD) TBCs is investigated. The addition of molten salts lowers the melting point of CM, leading to more severe destructive effects compared to single CMAS corrosion. Moreover, the 1200# specimen (Ra = 2.0 μm) exhibits the best resistance to CM corrosion. The adjustment of interface roughness significantly alters the microstructure of the top-ceramic coating and induces changes in crack patterns after CM corrosion. Therefore, appropriately reducing interface roughness is key to improving the isothermal CM corrosion resistance and high-temperature CM corrosion-induced thermal shock performance of TBCs.
Chenyang Jing,Da Bi,Hao Yan,Yangyang Pan,Donald B. Dingwell,Wenjia Song
Airborne particles such as sand, dust, and volcanic ash pose a severe threat to the reliability of thermal barrier coatings (TBCs) in jet engines. Upon ingestion into engines, these silicate-rich particles melt and form deposits that undergo complex spreading and adhesion dynamics on coating surfaces, ultimately leading to coating degradation. To simplify the reproducibility and comparability of experimental studies synthetic calcium–magnesium–alumino–silicate (CMAS) particles are commonly employed as simulants. Here, high-temperature deposition experiments have revealed that partially molten droplets (containing solid inclusions), exhibit heterogeneous spreading morphologies beyond classical viscous flow. A compound droplet model integrating heat transfer and spreading dynamics has been developed to describe quantitatively the deposition behavior of such partially molten particles. A physics-informed machine learning model, trained on 105 experimental cases was developed and has demonstrated superior predictive performance, with support vector regression achieving the highest accuracy (R 2 ≈ 0.71, RMSE ≈ 0.13). This combined experimental–computational strategy establishes a robust predictive paradigm linking natural threats (sand, dust, volcanic ash) with CMAS simulants, providing mechanistic insights into and mitigation strategies for CMAS-related degradation to enhance the operational reliability of aeroengines.
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