面向运维的HPT叶片寿命数字孪生建模方法

doi:10.7527/S1000-6893.2023.29385

Abstract

Abstract:

High-Pressure Turbine （HPT） blade is a typical hot-end component with high temperature， high load and complex structure. Its in-service life depends not only on the level of design， manufacture and process， but also on the actual operating environment， operating conditions and maintenance of the engine. Therefore， how to fuse multi-modal operation and maintenance data to improve the prediction accuracy of HPT blade in-service life and reduce the prediction uncertainty is an important issue. Based on the Usage-Based Life （UBL） method and digital twin technology， this paper proposes a Life Digital Twin （LDT） modeling method for in-service HPT blades driven by data and model fusion. Based on multi-modal operation and maintenance data， the life consumption of HPT blades under actual service conditions can be characterized and tracked， and the in-service lifetime can be predicted under specific operating condition. In addition， the approach of uncertainty quantification and management involved in LDT is also proposed. The approach proposed has been verified on an HPT blade. The results show that the LDT and uncertainty quantification and management approach proposed can effectively fuse multi-modal operation and maintenance data， reduce the prediction uncertainty of HPT blade’s in-service load and life， as well as improve the fidelity of HPT blade life digital twin model； therefore， provides a basic approach support for single engine life management and intelligent operation and maintenance based on digital twin.

Key words: life digital twin, uncertainty quantification and management, in-service HPT blade, operation and maintenance, information fusion

CLC Number:

V232.4

Chunhua LI, Jianzhong SUN, Jilong LU. Maintenance-oriented approach for HPT blade life digital twin modeling[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(21): 629385.

Figures/Tables 38

Fig.1

Table 1

Table 2

Fig.2

Fig.3

Fig.4

Table 3

Fig.5

Table 4

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Table 5

Stochastic uncertainty parameters

参数	概率分布	真实值
$F R T D$	N （0.1，0.025）	0.12
$P T m a x$	N （0.5，0.04）	0.48
$F T$	N （1，0.05）	1.02
$F M$	N （1，0.05）	0.98
$H s$ /μm	N （0.02，0.005）	0.022

Table 5

Fig.14

Fig.15

Fig.16

Fig.17

Fig.18

Fig.19

Fig.20

Fig.21

Fig.22

Fig.23

Table 6

MCMC posterior parameter results compared to true value

参数	真实解	校准结果
参数	真实解	μ	CoV
$F R T D$	0.12	0.11	0.17
$P T m a x$	0.48	0.47	0.04
$F M$	1.02	1.01	0.05
$F T$	0.98	0.98	0.02
$H s$ /μm	0.022	0.021	0.13

Table 6

Table 7

Temperature calibration results

关注点	$μ r a t i o$	$σ r a t i o$	关注点	$μ r a t i o$	$σ r a t i o$	关注点	$μ r a t i o$	$σ r a t i o$	关注点	$μ r a t i o$	$σ r a t i o$
位置1	0.31	0.37	位置5	0.51	0.47	位置9	0.41	0.44	位置13	0.70	0.46
位置2	0.26	0.26	位置6	0.14	0.23	位置10	0.10	0.28	位置14	0.68	0.47
位置3	0.31	0.23	位置7	0.06	0.27	位置11	0.37	0.53
位置4	0.45	0.43	位置8	0.40	0.30	位置12	0.71	0.46

Table 7

Fig.24

Fig. 25

Fig.26

Fig.27

Fig.28

Fig.29

Fig.30

Table 8

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参数	线性相关系数
参数	N1	N2	T25	P3	T3	EGT
N1	1.0	0.96	0.81	0.83	0.97	0.49
N2		1.0	0.91	0.94	0.97	0.44
T25			1.0	0.87	0.90	0.82
P3				1.0	0.90	0.71
T3					1.0	0.61
EGT						1.0

参数	输入（+）/输出（-）	参数	输入（+）/输出（-）
飞行高度/m	+	HPC入口温度/℃	-
标准大气温差/℃	+	HPC出口温度/℃	-
飞行马赫数	+	HPT入口温度/℃	-
燃油流量/（kg·s^-1）	+	HPT出口温度/℃	-
高压转子转速/（r·min^-1）	-	HPC修正空气流量/（kg·s^-1）	-
HPC入口压力/Pa	-	HPT修正空气流量/（kg·s^-1）	-
HPC出口压力/Pa	-	HPT出口压力/Pa	-

网格	单元数量	出口温度/K	等熵效率
1	4 751 622	1 223.53	0.812 8
2	9 634 993	1 223.62	0.812 7
3	13 622 902	1 224.41	0.811 9
4	13 962 582	1 224.25	0.812 2
5	15 832 058	1 224.27	0.812 1

参数	数值
冷气总温/K	789
进口燃气总温/K	1 586
高压转子转速/（r·min^-1）	14 282
动叶出口静压/kPa	559
进口燃气流量/（kg·s^-1）	1.05
静叶冷气流量/（kg·s^-1）	0.058
动叶冷气流量/（kg·s^-1）	0.036

未考虑不确定性/FC	考虑不确定性
未考虑不确定性/FC	条件	均值/FC	标准差/FC
27 100	校准前	16 200	1 775
27 100	校准后	19 160	530

Maintenance-oriented approach for HPT blade life digital twin modeling

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