基于多层感知机的航空发动机压气机盘应力和温度预测

王学民; 徐敬沛; 何云

doi:10.13224/j.cnki.jasp.20220297

基于多层感知机的航空发动机压气机盘应力和温度预测

doi: 10.13224/j.cnki.jasp.20220297

中国航发四川燃气涡轮研究院，成都 610500

基金项目: 航空动力基础研究项目

详细信息

作者简介:
王学民（1992-），男，工程师，主要从事航空发动机结构强度和寿命管理研究

中图分类号: V232.3
计量
- 文章访问数: 88
- HTML浏览量: 37
- PDF量: 37
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-01
- 网络出版日期: 2023-11-08

Stress and temperature prediction of aero-engine compressor disk based on multilayer perceptron

Sichuan Gas Turbine Establishment，Aero Engine Corporation of China，Chengdu 610500，China

摘要

摘要:
将发动机可测参数作为初始特征，利用人工神经网络技术建立航空发动机压气机盘应力和温度预测的MLP （multilayer perceptron）模型，采用BP（back propagation）神经网络算法进行训练。结果表明：该方法预测结果与传统有限元计算结果吻合较好，相对偏差均在1%以内，判定系数达到0.95以上，方均根误差均在5以内，且计算速度由小时级提升为分秒级，可为后续工程应用提供依据。
- 压气机轮盘 /
- 神经网络 /
- 多层感知机 /
- 应力 /
- 温度 /
- 寿命管理
Abstract:
Taking the measures parameters of the engine as the initial characteristics, the MLP （multilayer perceptron） model of aero-engine compressor disk stress and temperature prediction was established by using artificial neural network technology, and BP （back propagation） neural network algorithm was used for training. The results showed that the prediction results of this method were in good agreement with the traditional finite element calculation results. The relative deviations were all within 1%, the determination coefficients were above 0.95, and the root mean squared error was within 5. Moreover, the calculation speed increased from hour level to minute second level, providing a basis for subsequent engineering applications.
- compressor disk /
- neural network /
- multilayer perceptron /
- stress /
- temperature /
- life management

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图 1 多层感知机示意

Figure 1. Schematic representation of multilayer perceptron

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图 2 3种常用的激活函数

Figure 2. Three commonly used activation functions

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图 3 BP算法思想

Figure 3. Idea of BP algorithm

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图 4 BP算法概述

Figure 4. Overview of BP algorithm

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图 5 模型构建流程

Figure 5. Model building process

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图 6 各输入参数的分布（无量纲）

Figure 6. Distribution of input parameters （non-dimensional）

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图 7 数据归一化效果（以P₂₅为例）

Figure 7. Effect of data normalization （taking P₂₅ as an example）

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图 8 模型预测值与盘心真实应力拟合图

Figure 8. Fitting diagram of model predicted value and real stress of dick center

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图 9 应力预测误差分布直方图

Figure 9. Histogram of stress prediction error distribution

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图 10 模型预测值与盘心真实温度拟合图

Figure 10. Fitting diagram of model predicted value and real temperature of dick center

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图 11 温度预测误差分布直方图

Figure 11. Histogram of temperature prediction error distribution

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表 1 模型输入层参数及含义

Table 1. Model input layer parameters and meanings

输入层参数	含义
H/km	高度
Ma	马赫数
N₂/（r/min）	压气机转速
W₂₅/（kg/s）	压气机进口空气流量
P₂₅/kPa	压气机进口总压
P₃/kPa	压气机出口总压
T₂₅/℃	压气机进口总温
T₃/℃	压气机出口总温

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表 2 应力预测模型评价指标

Table 2. Evaluation index of stress prediction model

最优化策略	sigmoid函数		tanh函数		ReLU函数
最优化策略	R²	J_RMSE	R²	J_RMSE	R²	J_RMSE
LBFGS	0.95	15.3	−0.02	145	1.00	2.81
SGD	−0.06	139	−0.07	149	−55.8	807
Adam	−19.4	707	−13.7	676	−1.08	235

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表 3 模型应力预测结果对比

Table 3. Comparison of model stress prediction results

真实值/ MPa	预测值/ MPa	偏差/ %	真实值/ MPa	预测值/ MPa	偏差/ %
808.0	807.8	0.02	844.0	843.6	0.05
789.0	790.0	0.14	641.0	643.8	0.44
759.0	759.5	0.07	397.0	395.0	0.50
844.0	846.9	0.35	422.0	424.1	0.51
858.0	857.5	0.05	860.0	858.6	0.15
807.0	807.5	0.07	843.0	844.9	0.23
806.0	803.5	0.31	782.0	782.2	0.03
791.0	788.0	0.38	796.0	794.8	0.15

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表 4 应力预测模型评价指标分析结果

Table 4. Evaluation index analysis results of stress prediction model

评价指标	抽样次数
评价指标	1	2	3	4	5	6
R²	1.00	1.00	1.00	0.99	1.00	1.00
${J_{{\text{RMSE}}}}$	2.41	2.32	1.67	2.68	2.37	1.76

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表 5 模型温度预测结果对比

Table 5. Comparison of model temperature prediction results

真实值/ ℃	预测值/ ℃	偏差/ %	真实值/ ℃	预测值/ ℃	偏差/ %
554.0	552.7	0.23	600.0	600.6	0.11
531.0	530.8	0.02	501.0	498.4	0.51
643.0	641.8	0.18	397.0	395.1	0.46
505.0	503.5	0.30	694.0	695.9	0.28
543.0	543.2	0.04	603.0	604.3	0.23
579.0	580.2	0.21	630.0	631.0	0.16
564.0	565.1	0.21	607.0	607.6	0.11
570.0	569.0	0.16	452.0	450.1	0.41

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表 6 温度预测模型评价指标分析结果

Table 6. Evaluation index analysis results of temperature prediction model

次数	抽样次数
次数	1	2	3	4	5	6
R²	1.00	1.00	1.00	1.00	1.00	1.00
${J_{{\text{RMSE}}}}$	1.24	1.31	1.34	1.10	1.46	1.36

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