基于部件特性图优化的民航发动机性能退化建模

郭庆; 黄启廉; 陈金亮

doi:10.13700/j.bh.1001-5965.2023.0341

基于部件特性图优化的民航发动机性能退化建模

doi: 10.13700/j.bh.1001-5965.2023.0341

1.
中国民航大学航空工程学院，天津 300300
2.
山东航空股份有限公司烟台分公司，烟台 264000

基金项目:

2022年天津市研究生科研创新项目(2022SKY156)

详细信息

通讯作者:
E-mail：qguocauc@sina.com

中图分类号: V263.6
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- 被引次数: 0
出版历程
- 收稿日期: 2023-06-12
- 录用日期: 2023-08-11
- 网络出版日期: 2023-08-22
- 整期出版日期: 2025-06-30

Performance degradation modelling of civil aviation engines based on component characteristic map optimization

1.
School of Aeronautical Engineering，Civil Aviation University of China，Tianjin 300300，China
2.
Shandong Airlines Co.，Ltd. Yantai Branch，Yantai 264000，China

Funds:

2022 Tianjin Postgraduate Research Innovation Project (2022SKY156)

More Information

Corresponding author: E-mail：qguocauc@sina.com

摘要

摘要:
为从单元体层级给出民航发动机气路性能退化的理论依据，以CFM56-3发动机为研究对象，在使用特性图缩放法获取部件特性方程的基础之上，优化了风扇通用特性图缩放基准点的选取过程，提出特性图的曲面拟合方法，构建出稳态工况下符合特定转速条件的发动机部件级基准性能模型。通过引入故障因子生成故障系数矩阵，计算发动机监控参数随部件效率下降的偏离量，并与美国通用电气公司培训手册的指印图资料作对比，验证了融合风扇特性图缩放基准点优化及特性图曲面拟合方法的发动机稳态性能模型在气路性能退化分析中具有较好的精度和使用前景。
- 民航发动机 /
- 气路分析 /
- 部件级建模 /
- 缩放基准点优化 /
- 曲面拟合
Abstract:
In order to provide a theoretical basis for the gas path performance degradation of civil aviation engines at the module level, the CFM56-3 engine was taken as the research object. Firstly, the characteristic map scaling method was used to obtain the component characteristic equations, and the selection process of the general fan characteristic map scaling reference point was optimized. A characteristic map surface fitting method was proposed to construct an engine component-level benchmark performance model that conformed to specific speed conditions under steady-state operating mode. Then, by introducing fault factors to generate a fault coefficient matrix, the deviation of engine monitoring parameters with the decrease in component efficiency was calculated and compared with the fingerprint diagram data in the General Electric Company training manual. The results show that the engine steady-state performance model integrating fan characteristic map scaling reference point optimization and characteristic map surface fitting methods has good accuracy and application prospects in gas performance degradation analysis.
- civil aviation engine /
- gas path analysis /
- component-level modelling /
- scaling reference point optimization /
- surface fitting

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图 1 特性图缩放法

Figure 1. Characteristic map scaling method

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图 2 技术路线图

Figure 2. Technology roadmap

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图 3 通用特性图缩放基准点选取

Figure 3. Selection of general characteristic map scaling reference point

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图 4 适应度进化曲线

Figure 4. Fitness evolution curve

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图 5 CFM56-3发动机风扇特性

Figure 5. Fan characteristic of CFM56-3 engine

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图 6 风扇部件特性曲面

Figure 6. Fan component characteristic surface

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图 7 CFM56-3发动机气路简化模型

LPC：低压压气机，HPC：高压压气机，HPT：高压涡轮，LPT：低压涡轮，各数字：发动机的位置，1：进气道入口，2：风扇入口，13：风扇出口，18：外涵喷管出口，22：LPC入口，25：HPC入口，3：HPC五级出口，31：HPC出口，4：燃烧室出口，41：HPT导向器出口，44：HPT出口，43：LPT导向器出口，5：LPT出口，9：内涵喷管出口。

Figure 7. Simplified gas path model of CFM56-3 engine

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图 8 监控参数偏离量实验结果

Figure 8. Experimental results of monitoring parameter deviation

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表 1 参数信息

Table 1. Parameters information

参数	数值
涵道比$ {R_{{\mathrm{BPR}}}} $	5.0245
气体常数$ R $/(J·(kg·K)⁻¹)	287.0
燃油热值$ {H_{\text{U}}} $/10⁷ (J·kg⁻¹)	4.277
低压转子转速$ {N_{1{\text{C}}}} $	0.9300
燃烧效率$ {\eta _{\text{B}}} $	0.9960
风扇进口空气总温$ T_2^* $/K	244.14
风扇进口空气总压$ P_2^* $/10⁴ Pa	3.4615
国际标准大气总温$ T_{{\text{ISA}}}^* $/K	288.15
国际标准大气总压$ P_{{\text{ISA}}}^* $/10⁵ Pa	1.01325
高压涡轮轴机械效率$ {\eta _{{\text{MH}}}} $	0.99
低压涡轮轴机械效率$ {\eta _{{\text{ML}}}} $	1.00
进气道总压恢复系数$ {\sigma _{\text{I}}} $	0.99
中介机匣总压恢复系数$ {\sigma _{24}} $	0.98
HPT转子轴承冷却空气比例$ {\nu _{{\text{CHR}}}} $	0.05
HPT进口导向叶片冷却空气比例$ {\nu _{{\text{CHN}}}} $	0.06
LPT进口导向叶片冷却空气比例$ {\nu _{{\text{CLN}}}} $	0.02

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表 2 CFM56-3发动机参数基准值

Table 2. Parameter reference values of CFM56-3 engine

参数	基准值	参数	基准值
$ T_{13}^* $/K	286.8200	$ {W_{\text{F}}} $/(kg·s⁻¹)	0.3800
$ T_{25}^* $/K	317.2400	$ {W_4} $/(kg·s⁻¹)	17.5076
$ T_3^* $/K	663.3500	$ {W_{41}} $/(kg·s⁻¹)	18.6934
$ T_4^* $/K	1345.7188	$ {W_{44}} $/(kg·s⁻¹)	19.6801
$ T_{41}^* $/K	1305.8580	$ {W_{45}} $/(kg·s⁻¹)	20.0752
$ T_{43}^* $/K	991.8628	$ {\pi _{{\text{Fan}}}} $	1.6600
$ T_{44}^* $/K	976.2153	$ {\pi _{{\text{LPC}}}} $	2.2480
$ T_{45}^* $/K	967.7159	$ {\pi _{{\text{HPC}}}} $	10.7020
$ T_5^* $/K	717.9947	$ {\pi _{{\text{HPT}}}} $	4.2566
$ {W_{13}} $/(kg·s⁻¹)	99.1820	$ {\pi _{{\text{LPT}}}} $	3.8993
$ {W_2} $/(kg·s⁻¹)	118.9210	$ {\eta _{{\text{Fan}}}} $	0.8931
$ {W_{2{\text{Rstd}}}} $/(kg·s⁻¹)	320.4200	$ {\eta _{{\text{LPC}}}} $	0.8700
$ {W_{22}} $/(kg·s⁻¹)	19.7396	$ {\eta _{{\text{HPC}}}} $	0.8616
$ {W_{22{\text{Rstd}}}} $/(kg·s⁻¹)	53.1860	$ {\eta _{{\text{HPT}}}} $	0.8215
$ {W_{25{\text{Rstd}}}} $/(kg·s⁻¹)	27.4870	$ {\eta _{{\text{LPT}}}} $	0.8860
$ {W_3} $/(kg·s⁻¹)	19.3450	$ {N_{2{\text{C}}}} $	0.9099
$ {W_{31}} $/(kg·s⁻¹)	17.1747	$ T_{{\mathrm{EGT}}}^* $/K	891.6017

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表 3 不同监控参数偏离量和故障因子下的故障系数

Table 3. Fault coefficients under different monitoring parameter deviations and fault factors

监控参数偏移	故障系数a_ij/%
监控参数偏移	$ \delta {\tilde W_{2{\text{Rstd}}}} $	$ \delta {\tilde \eta _{{\text{Fan}}}} $	$ \delta {\tilde W_{22{\text{Rstd}}}} $	$ \delta {\tilde \eta _{{\text{LPC}}}} $	$ \delta {\tilde W_{25{\text{Rstd}}}} $	$ \delta {\tilde \eta _{{\text{HPC}}}} $	$ \delta {\tilde \eta _{{\text{HPT}}}} $	$ \delta {\tilde \eta _{{\text{LPT}}}} $
$ \delta T_{13}^* $	−0.32	0.15	0.02	0	−0.01	0.02	0.03	−0.01
$ \delta T_{25}^* $	0.21	−0.06	−0.39	0.23	−0.03	0.15	0.19	−0.09
$ \delta T_3^* $	−0.63	0.21	−0.06	0.25	0.07	0.24	−0.37	0.31
$ \delta T_4^* $	−0.99	0.71	0.19	0.40	−0.11	0.80	0.64	0.76
$ \delta T_{41}^* $	−0.98	0.69	0.18	0.39	−0.11	0.79	0.61	0.74
$ \delta T_{43}^* $	−0.93	0.80	0.19	0.45	−0.20	0.99	1.17	0.81
$ \delta T_{44}^* $	−0.92	0.78	0.18	0.44	−0.19	0.97	1.12	0.79
$ \delta T_{45}^* $	−0.92	0.78	0.18	0.44	−0.19	0.97	1.11	0.79
$ \delta T_5^* $	−0.88	0.93	0.20	0.51	−0.22	1.07	1.20	1.29
$ \delta {\pi _{{\text{Fan}}}} $	−0.96	0	0.06	0	0	0.06	0.12	0
$ \delta {\pi _{{\text{LPC}}}} $	1.16	−0.40	−1.47	0.04	−0.13	0.80	1.02	−0.58
$ \delta {\pi _{{\text{HPC}}}} $	−2.79	0.92	1.00	0.07	0.28	−1.28	−1.77	1.32
$ \delta {\pi _{{\text{HPT}}}} $	−0.40	−0.52	−0.02	−0.24	0.15	−0.31	−0.36	−0.27
$ \delta {\pi _{{\text{LPT}}}} $	−0.31	−0.66	−0.06	−0.30	0.12	−0.30	−0.17	−0.55
$ \delta {W_{\text{F}}} $	−2.30	1.43	−0.20	0.50	−0.14	0.57	0.55	1.56
$ \delta {N_{2{\text{C}}}} $	−1.19	0.39	0.27	0.16	0.79	−0.82	−1.04	0.56
$ T_{\mathrm{ EGT}} $	−0.91	0.80	0.18	0.45	−0.20	0.98	1.13	0.87

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表 4 监控参数偏离量比例关系

Table 4. Proportional relationship of monitoring parameters deviation

监控参数偏离量比例关系	$ \delta T_{\mathrm{EGT}}:\delta W_{\text{F}}:\delta N_{2\text{C}} $
监控参数偏离量比例关系	实验	指印图
−1% η_PAN	1:0.20:0.05	1:0.22:0.12
−1% η_LPC	1:0.12:0.04	1:0.10:0.03
−1% η_HPC	1:0.06:−0.09	1:0.10:−0.13
−1% η_HPT	1:0.06:−0.10	1:0.10:−0.13
−1% η_LPT	1:0.20:0.07	1:0.21:0.10

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