航空发动机 CMAS 的本征特性分析及黏度预测

鄢浩; 王哲; 蒋成洋; 毛海荣; 阮宏泽; 梁磊; 宋文佳

doi:10.7527/S1000-6893.2026.33728

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DOI: https://doi.org/10.7527/S1000-6893.2026.33728

航空发动机 CMAS 的本征特性分析及黏度预测

鄢浩 ,
王哲 ,
蒋成洋 ,
毛海荣 ,
阮宏泽 ,
梁磊 ,
宋文佳

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1. 北京航空航天大学材料学院
2. 北京航空航天大学
3. 北京航空航天大学国际创新学院
4. 中国商飞集团公司上海飞机设计研究院

收稿日期: 2026-04-15

修回日期: 2026-06-23

网络出版日期: 2026-08-31

基金资助

浙江省尖兵领雁研发攻关计划;浙江省自然科学基金项目

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Analysis of the intrinsic characteristics and viscosity prediction of aeroengine CMAS

YAN Hao ,
WANG Zhe ,
JIANG Cheng-Yang ,
MAO Hai-Rong ,
RUAN Hong-Ze ,
LIANG Lei ,
SONG Wen-Jia

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Received date: 2026-04-15

Revised date: 2026-06-23

Online published: 2026-08-31

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摘要

高温热障/环境障涂层是航空发动机热端部件的关键防护材料，但其在服役过程中易受环境沉积物（CMAS，主要成分为沙石、灰尘和火山灰）在高温下的腐蚀损害，严重威胁发动机安全运行。由于CMAS的理化特性高度依赖地理来源，基于特定环境建立的腐蚀防护模型在我国复杂地貌条件下适用性有限，但目前尚缺乏对我国全域CMAS常/高温特性的系统性研究。针对这一问题，本研究采集了我国内陆典型地区的90种CMAS样品，并系统表征了常温特性（包括化学组分、粒度分布、晶体相对含量和吸附水相对含量）与高温特性（包括熔融温度与高温黏度）。结果表明，沙石、灰尘和火山灰的常/高温特性存在显著差异，其中沙石和灰尘表现出较强的区域均质性，火山灰则异质性显著。本研究构建了CMAS高温黏度的机器学习预测模型，SVM、MLP和GBDT模型预测精度（R2 ≥ 0.97）明显优于传统方法（GRD与FactSage）。基于SHAP分析揭示沙石CMAS黏度受化学成分与结构特征多因素耦合控制。本研究构建了我国首个系统性CMAS特性数据库，所提出的机器学习方法为高温黏度预测提供了新途径，为抗CMAS涂层设计提供了关键数据与理论支撑。

关键词： 航空发动机; 环境沉积物; 机器学习; CMAS

本文引用格式

鄢浩 , 王哲 , 蒋成洋 , 毛海荣 , 阮宏泽 , 梁磊 , 宋文佳 . 航空发动机 CMAS 的本征特性分析及黏度预测[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.33728

Abstract

High-temperature thermal barrier/environmental barrier coatings serve as critical protective materials for hot-end components in aero-engines. However, during service, they are susceptible to corrosion damage from environmental deposits (CMAS, primarily composed of sand, dust, and volcanic ash) at elevated temperatures, posing a severe threat to engine operational safety. Given that the physicochemical properties of CMAS are highly dependent on geographical origin, corrosion protection models developed for specific environments have limited applicability across China's complex topography. However, systematic research on the ambient/high-temperature characteristics of CMAS across the entire nation remains lacking. To address this gap, this study collected 90 CMAS samples from representative inland regions of China. It systematically characterised both ambient properties (including chemical composition, particle size distribution, relative crystalline content, and relative adsorbed water content) and high-temperature properties (including melting temperature and high-temperature viscosity). Results indicate significant differences in the normal/high-temperature properties of sand-gravel, dust, and volcanic ash. Sand-gravel and dust exhibit strong regional homogeneity, whereas volcanic ash shows pronounced heterogeneity. A machine learning predictive model for CMAS high-temperature viscosity was developed, with SVM, MLP, and GBDT models demonstrating significantly higher prediction accuracy (R2 ≥ 0.97) than traditional methods (GRD and Factsage). SHAP analysis revealed that sand/gravel CMAS viscosity is governed by the multi-factor coupling of chemical composition and structural characteristics. This study established China's first systematic CMAS property database. The proposed machine learning approach provides a novel pathway for high-temperature viscosity prediction, offering critical data and theoretical support for the design of CMAS-resistant coatings.

Key words： aeroengine,; environmental deposits,; machine learning,; CMAS

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