面向视情维修的航空发动机涡轮叶片剩余寿命预测方法

赵炎; 陈若琦; 沈天宝; 刘宇鹏; 王学民; 胡殿印

doi:10.13224/j.cnki.jasp.20240532

面向视情维修的航空发动机涡轮叶片剩余寿命预测方法

doi: 10.13224/j.cnki.jasp.20240532

赵炎^{1, 2, 3,},
陈若琦¹,
沈天宝⁴,
刘宇鹏¹,
王学民⁵,
胡殿印^{2, 3, 4,*, ,}

1.
北京航空航天大学能源与动力工程学院，北京 100191
2.
中小型航空发动机联合研究中心，北京 100191
3.
北京航空航天大学航空发动机结构强度北京市重点实验室，北京 100191
4.
北京航空航天大学航空发动机研究院，北京 100191
5.
中国航发四川燃气涡轮研究院，成都 610500

基金项目: 基础科研项目（JCKY2021601B204）

详细信息

作者简介:
赵炎（1997-），男，博士，主要从事发动机健康管理研究。E-mail：zy_buaa@buaa.edu.cn

通讯作者:
胡殿印（1980-），女，教授，博士，主要从事结构强度与疲劳可靠性研究。E-mail：hdy@buaa.edu.cn

中图分类号: V232.4
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出版历程
- 收稿日期: 2024-07-31
- 网络出版日期: 2025-02-09

Remaining life prediction method for aero-engine turbine blades oriented to on-condition maintenance

ZHAO Yan^{1, 2, 3
,},
CHEN Ruoqi¹,
SHEN Tianbao⁴,
LIU Yupeng¹,
WANG Xuemin⁵,
HU Dianyin^{2, 3, 4,*
, ,}

1.
School of Energy and Power Engineering，Beihang University，Beijing 100191，China
2.
United Research Center of Mid-Small Aero-Engine，Beijing 100191，China
3.
Beijing Key Laboratory of Aero-Engine Structure and Strength，Beihang University，Beijing 100191，China
4.
Research Institute of Aero-Engine，Beihang University，Beijing 100191，China
5.
Sichuan Gas Turbine Establishment，Aero Engine Corporation of China，Chengdu 610500，China

摘要

摘要:
提出了面向视情维修的航空发动机涡轮叶片寿命消耗计算框架，建立了基于长短时记忆网络的发动机总体性能仿真模型，实现了基于实测飞参的涡轮叶片热力仿真模型内流截面参数快速计算，提出了基于降阶模型的涡轮叶片温度场/应力场快速映射方法，进一步提取涡轮叶片危险部位载荷信息，实现了涡轮叶片的高精度剩余寿命预测。与传统仿真方法相比，提出的气流截面参数模型计算结果最大相对误差不超过5%，提出的温度场/应力场快速映射算法计算结果温度场方均根误差在±5 K内、应力场方均根误差在±3 MPa内，且计算速度提升超过99%。实现了单次飞行起落循环高精度高效率的涡轮叶片载荷状态评估和剩余寿命计算。
- 视情维修 /
- 涡轮叶片 /
- 降阶模型 /
- 载荷快速计算 /
- 剩余寿命预测
Abstract:
A framework for calculating the life consumption of aero-engine turbine blades oriented to on-condition maintenance was proposed. An engine system performance model based on the long-/short-term memory network was established, and the fast calculation of the turbine blade thermal simulation model instream flow cross-section parameters based on the measured flight reference was achieved. A fast mapping method of turbine blade temperature/stress field based on the reduced-order model was presented. Furthermore, the load information of the dangerous parts of the turbine blade were extracted, and the high-precision remaining life prediction of the turbine blade was implemented. Compared with the traditional simulation methods, the maximum relative error of the proposed airflow cross-section parameter model calculation results did not exceed 5%. The proposed fast temperature/stress field mapping algorithm calculated the temperature field root mean square error within ±5 K and the stress field root mean square error within ±3 MPa, and the computation speed was improved by more than 99%. The high accuracy and high efficiency of the turbine blade load state assessment and remaining life calculation in a flight cycle were achieved.
- on-condition maintenance /
- turbine blades /
- reduced order model /
- rapid load calculation /
- remaining life prediction

HTML

图 1 面向视情维修的涡轮叶片剩余寿命预测框架

Figure 1. Framework for predicting the remaining life of turbine blades for on-condition maintenance

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图 2 LSTM单元结构

Figure 2. LSTM unit structure

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图 3 模型结构

Figure 3. Model structure

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图 4 发动机模型示意图

Figure 4. Schematic diagram of engine model

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图 5 模型训练及验证损失变化

Figure 5. Variation of model training and validation loss

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图 6 模型计算结果

Figure 6. Model calculation results

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图 7 几何建模及网格划分

Figure 7. Geometric modelling and meshing

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图 8 结构计算边界条件

Figure 8. Structural calculation boundary conditions

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图 9 涡轮叶片温度场和应力场计算结果

Figure 9. Calculation results of temperature and stress fields of turbine blades

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图 10 计算样本点抽样结果

Figure 10. Calculated sample point sampling results

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图 11 方均根误差随基函数个数选取变化情况

Figure 11. Variation of root mean square error with the selection of number of basis functions

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图 12 气膜孔处等效应变变化情况

Figure 12. Variation of equivalent stress at film cooling hole

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图 13 气膜孔处温度场变化情况

Figure 13. Variation of temperature field at film cooling hole

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表 1 可测飞行工况参数^[23]

Table 1. Measurable flight condition parameters^[23]

符号或缩写	名称	单位
Alt	高度	ft
Ma	马赫数
TRA	节流阀旋转角	%
T₂	风扇入口总温	K

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表 2 可测截面气流参数

Table 2. Measurable cross-section airflow parameters

符号	名称	单位
$ {W_{\text{f}}} $	燃料质量流量	kg/s
${N_{\text{l}}}$	风扇物理转速	r/min
${N_{\text{h}}}$	核心机转速	r/min
${T_{25}}$	低压压气机出口总温	K
${T_5}$	低压涡轮出口总	K
${p_2}$	风扇进口总压力	kPa
${p_3}$	高压压气机出口总压	kPa

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表 3 不可测截面气流参数

Table 3. Non-measurable cross-section airflow parameters

符号	名称	单位
${T_4}$	燃烧室出口总温	K
${p_4}$	燃烧室出口总压力	kPa
${p_{45}}$	高压涡轮出口总压	kPa
${T_{45}}$	高压涡轮出口总温	K
${T_3}$	高压压气机出口总温	K
${p_{25}}$	低压压气机出口总压力	kPa
${p_5}$	低压涡轮出口总压	kPa

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表 4 DD5材料低周疲劳预测模型参数

Table 4. Parameters of low-frequency fatigue prediction model for DD5 material

温度/K	$ {\sigma '_{\text{f}}} $/MPa	E/GPa	b	$ {\varepsilon '_{\text{f}}} $	C
773.15	−98.75	113.3	0.346	0.003	0.871
1123.15	−110.35	98	0.782	0.006	0.659
1473.15	−279.7	61.5	−0.077	0.272	−0.145

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