西安交通大学复杂服役环境重大装备结构强度与寿命全国重点实验室,710049,西安
空军工程大学航空工程学院,710038,西安
暨南大学公共管理学院/应急管理学院,510632,广州
陕西省特种设备检验检测研究院,710048,西安
中国飞机强度研究所强度与结构完整性全国重点实验室,710065,西安
作者简介:周运来(1986—),男,副教授,博士生导师。
收稿:2025-09-23,
纸质出版:2026-06-10
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周运来, 温生林, 董鑫宇, 等. 航空发动机高温结构损伤检测研究与应用进展[J]. 西安交通大学学报, 2026,60(6):86-96. DOI: 10.7652/xjtuxb202606007.
ZHOU Yunlai, WEN Shenglin, DONG Xinyu, et al. Research and Application Progress on Damage Detection for High-Temperature Structures of the Aero-Engines[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 86-96. DOI: 10.7652/xjtuxb202606007.
航空发动机作为航空装备的“动力心脏”,其高温结构(如涡轮叶片、燃烧室)在高温、高压、强振动等极端环境下的损伤失效直接威胁着航空装备安全,是制约航空装备向隐身化、超音速化、长寿命化发展的核心瓶颈之一。首先,从航空发动机高温结构损伤智能感知、检测评估、先进测试等层面出发,综述了航空发动机高温结构损伤检测领域的研究与应用进展。接着,针对航空发动机高温结构损伤检测,分别总结了基于航空发动机定期检测和基于飞参总线数据判读两方面的研究进展,介绍了新型检测方法在航空发动机高温结构中的创新应用实践,并总结了现有的采用数据驱动方式实现损伤早期识别和趋势预测的应用研究。然后,在模型驱动方面,阐述了集成物理机理模型和采用智能算法提升预测性能的可靠性,以及机器学习与深度学习算法同步处理多源数据,从而实现航空发动机高温结构损伤快速检测的研究现状。最后,梳理了相关检测技术和判读技术发展历程,并结合航空装备应用需求展望了未来发展趋势。
The aero-engine is the “power heart”of aero-equipment.Its high-temperature structures(e.g.,turbine blades and combustors)may suffer damage or failure when exposed to extreme conditions such as high temperature,high pressure,and intense vibration,which will directly threaten the operational safety of aero-equipment.These structures constitute one of the key bottlenecks that constrain the evolution of aero-equipment towards stealth,hypersonic capability,and extended service life.First,from the perspectives of intelligent sensing,detection and evaluation,and advanced testing,the research and application progress on damage detection for high-temperature structures of aero-engines is reviewed.Next,research advances on the damage detection of high-temperature structures of aero-engines are summarized in terms of periodic aeroengine inspection and interpretation of flight-parameter bus data,and innovative applications of novel detection methods to high-temperature structures of aero-engines are introduced;furthermore,existing application studies that employ data-driven approaches for early damage identification and trend prediction are also summarized.Subsequently,in the model-driven domain,the integration of physics-based mechanism models and the use of intelligent algorithms to improve prediction reliability are described,and the current state of research in which machine learning and deep learning algorithms are used to synchronously process multi-source data for rapid detection of damage to high-temperature structures of aero-engines is presented.Finally,the development history of related detection and interpretation technologies is outlined,and future development trends are forecasted in light of aero-equipment application requirements.
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