热障涂层的智能化设计研究进展

何健; 李宏峰; 康鹏; 郑蕾; 李圳; 郭洪波

doi:10.13700/j.bh.1001-5965.2025.0901

热障涂层的智能化设计研究进展

doi: 10.13700/j.bh.1001-5965.2025.0901

何健^{1, 2},
李宏峰^{1, E,},
康鹏^{1, 3},
郑蕾^{1, 3},
李圳¹,
郭洪波^{1, 2, 3, ,}

1.
北京航空航天大学材料科学与工程学院，北京 100191
2.
高温结构材料与涂层技术工信部重点实验室，北京 100191
3.
北京航空航天大学天目山实验室，杭州 311115

基金项目:

国家自然科学基金(U21B2052)

详细信息

通讯作者:
E-mail：guo.hongbo@buaa.edu.cn

中图分类号: V23；TG174.4
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- 文章访问数: 293
- HTML全文浏览量: 131
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- 被引次数: 0
出版历程
- 收稿日期: 2025-12-30
- 录用日期: 2026-05-08
- 网络出版日期: 2026-05-25
- 整期出版日期: 2026-10-01

Research progress on intelligent design of thermal barrier coatings

HE Jian^{1, 2},
LI Hongfeng^{1, E
,},
KANG Peng^{1, 3},
ZHENG Lei^{1, 3},
LI Zhen¹,
GUO Hongbo^{1, 2, 3
, ,}

1.
School of Materials Science and Engineering，Beihang University，Beijing 100191，China
2.
Key Laboratory of High-Temperature Structural Materials and Protective Coatings （Ministry of Industry and Information Technology），Beijing 100191，China
3.
Tianmushan Laboratory，Beihang University，Hangzhou 311115，China

Funds:

National Natural Science Foundation of China (U21B2052)

More Information

Corresponding author: E-mail：guo.hongbo@buaa.edu.cn

摘要

摘要:
随着航空发动机推重比与涡轮前进口温度的持续增高，热障涂层(TBC)已成为突破热端部件耐温极限、保障其长时服役可靠性的核心手段，但其服役行为涉及极度复杂的热-力-化(TMC)多场耦合与跨尺度损伤演化，使得传统单一尺度的实验与解决方法面临严峻挑战。基于此，系统梳理了TBC领域传统与先进建模、仿真及分析技术的研究进展，并拓展了人工智能(AI)技术的相关应用，旨在系统总结TBC服役行为的解析方法、涂层结构优化策略，并推动前瞻性维护策略的落地。涉及的技术方法包括TBC体系的热导率建模、热应力仿真、裂纹扩展分析及力学行为研究等，这些方法多依赖于多尺度建模和有限元分析等多样化手段。重点阐述了人工智能在TBC仿真中的应用，深入探究了支持向量机、随机森林、神经网络与深度学习等机器学习算法，以及数据驱动建模技术。研究结果表明，人工智能在多个方面具备革新TBC技术的潜力，涵盖材料与结构设计、性能优化与预测、状态监测、故障诊断与健康管理、剩余使用寿命(RUL)预测等方向，并能显著提升预测精度。随着智能化新材料设计技术的不断突破，新型TBC必将为下一代航空发动机突破耐温极限、实现更高效率与可靠性提供有力支撑。
- 热障涂层 /
- 智能化设计 /
- 结构设计 /
- 使用性能 /
- 机器学习
Abstract:
The sustained escalation in aero-engine thrust-to-weight ratios and turbine inlet temperatures has established thermal barrier coatings (TBCs) as an indispensable solution for surpassing the intrinsic temperature limits of hot-section components and ensuring their long-term service reliability. Nevertheless, the service response of TBCs involves exceedingly complex thermo-mechanical-chemical (TMC) multi-field coupling and cross-scale damage evolution, which poses severe fundamental challenges to conventional single-scale experimental and analytical methodologies. This review systematically summarizes the advances in conventional and advanced modeling, simulation, and analysis techniques for TBC and further extends the discussion to the application of artificial intelligence (AI) technologies. The study aims to provide a systematic overview of analysis methods for TBC service behavior and structural optimization strategies for coatings and to promote the implementation of proactive maintenance strategies. The technical methods covered include thermal conductivity modeling, thermal stress simulation, crack propagation analysis, and mechanical behavior studies of TBC systems, which predominantly rely on diverse techniques such as multiscale modeling and finite element analysis. The paper focuses on the use of AI in TBC simulation, including data-driven modeling approaches and machine learning algorithms such as support vector machines, random forests, neural networks, and deep learning. The findings indicate that AI holds the potential to revolutionize TBC technology in multiple respects, encompassing material and structural design, performance optimization and prediction, condition monitoring, fault diagnosis and health management, and remaining useful life (RUL) prediction, while significantly improving prediction accuracy. Novel TBCs are anticipated to offer robust support for next-generation aero-engines to transcend temperature limits and achieve improved efficiency and reliability with ongoing advancements in the intelligent design of new materials.
- thermal barrier coating /
- intelligent design /
- structural design /
- service performance /
- machine learning
注释:

1) E-mail：lihongfeng@buaa.edu.cn

HTML全文

图 1 TBC系统多层结构示意图^[12]

Figure 1. Schematic of multilayer of TBC system^[12]

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图 2 TBC在多种工业应用中的特定保护或功能需求^[13]

Figure 2. Specific protection or function needs of TBC in versatile industrial applications^[13]

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图 3 基于GA优化SVR的流程^[36]

Figure 3. Flow chart of GA optimization SVR^[36]

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图 4 新型双流太赫兹残差网络框架^[42]

Figure 4. Framework of dual-stream THz-ResNet^[42]

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图 5 SWT-BPNN的基本原理^[57]

Figure 5. Basic principle of SWT-BPNN^[57]

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图 6 利用机器学习改善YSZ涂层结合强度的流程^[65]

Figure 6. Flowchart of using machine learning to improve bonding strength of YSZ coating^[65]

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图 7 GPR算法流程^[74]

Figure 7. Flow chart of GPR algorithm^[74]

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图 8 图像与基于ANN的预测模型架构^[80]

Figure 8. Architecture of image and ANN-based prediction model^[80]

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