Zhiyuan Liu
(, ), Yiqi Xiao
(, ), Li Yang
(, ), Wei Liu
(, ), Gang Yan
(, ), Yu Sun
(, ), Yichun Zhou
(, )
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引用次数: 0
Abstract
Failure of thermal barrier coatings (TBCs) can reduce the safety of aero-engines. Predicting the lifetime of TBCs on turbine blades under real service conditions is challenging due to the complex multiscale computation required and the chemo-thermo-mechanically coupled mechanisms involved. This paper proposes a multiscale deep-learning method for TBC failure prediction under typical thermal shock conditions involving calcium-magnesium-alumina-silicate (CMAS) corrosion. A micro-scale model is used to describe local stress and damage with consideration of the TBC microstructure and CMAS infiltration and corrosion mechanisms. A deep learning network is developed to reveal the effect of microscale corrosion on TBC lifetime. The modeled spalling mechanism and area are consistent with the experimental results, with the predicted lifetime being within 20% of that observed. This work provides an effective method for predicting the lifetime of TBCs under real service conditions.
期刊介绍:
Acta Mechanica Sinica, sponsored by the Chinese Society of Theoretical and Applied Mechanics, promotes scientific exchanges and collaboration among Chinese scientists in China and abroad. It features high quality, original papers in all aspects of mechanics and mechanical sciences.
Not only does the journal explore the classical subdivisions of theoretical and applied mechanics such as solid and fluid mechanics, it also explores recently emerging areas such as biomechanics and nanomechanics. In addition, the journal investigates analytical, computational, and experimental progresses in all areas of mechanics. Lastly, it encourages research in interdisciplinary subjects, serving as a bridge between mechanics and other branches of engineering and the sciences.
In addition to research papers, Acta Mechanica Sinica publishes reviews, notes, experimental techniques, scientific events, and other special topics of interest.
Related subjects » Classical Continuum Physics - Computational Intelligence and Complexity - Mechanics