数字孪生驱动的飞机起降系统可靠域分析

doi:10.7527/S1000-6893.2025.32256

Abstract

Abstract:

Aimed at the failure issues of aircraft landing gear systems caused by complex operating conditions such as high and low temperatures， this study proposes a reliability domain analysis method based on digital twin technology to achieve global parameter optimization and dynamic determination of reliability domain. First， a five-level digital twin framework is established， with the functionalities of each layer elaborated. Second， leveraging the high efficiency， robust balancing capability， and absence of control parameter requirements of the Coati Optimization Algorithm （COA）， an optimized Support Vector Machine （SVM） is developed， and a COA-SVM digital twin model is constructed. This model establishes functional mappings for three performance indicators： system efficiency， stroke， and overload. Subsequently， a population-interaction-enhanced COA is proposed to address the optimization challenges arising from high-dimensional parameter spaces formed by multi-failure modes under extreme temperatures， thereby determining the reliability domain of aircraft landing gear systems in low-temperature environments. Finally， a landing gear drop test incorporating temperature effects is conducted for validation. The established digital twin model accurately reflects the physical system and reveals that the failure probability of aircraft landing gear systems increases under low-temperature conditions. The proposed digital-twin-driven reliability analysis provides novel strategies and technical pathways for the optimal design of aircraft landing gear systems.

Key words: aircraft landing gear systems, digital twin, multimodal failure, low-temperature environments, sensitivity analysis, reliability domain

CLC Number:

V226

Yuqian ZHANG, Yongge LI, Xiaochuan LIU, Yong XU. Digital-twin-driven reliability domain analysis for aircraft landing gear systems[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(24): 632256.

Figures/Tables 17

Fig.1

Fig.2

Table 1

Statistical information of random variables

随机变量	标识
初始充填压力/MPa	$x 1$
初始充气体积/cm³	$x 2$
压气面积/m²	$x 3$
正行程回油孔流量系数	$x 4$
反行程回油孔流量系数	$x 5$
主油腔压油面积/m²	$x 6$
回油腔压油面积/m²	$x 7$
主油孔面积/m²	$x 8$
正行程回油腔油孔面积/m²	$x 9$
反行程回油腔油孔面积/m²	$x 10$

Table 1

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 2

Failure probability values in low-temperature environments

温度/℃	效率失效模式	行程失效模式	过载失效模式
$- 40$	$1.0 × 10 - 6$	$6.76 × 10 - 4$	$6.76 × 10 - 4$
$- 30$	$0$	$8.3 × 10 - 5$	$8.3 × 10 - 5$
$- 20$	$0$	$2.0 × 10 - 6$	$2.0 × 10 - 6$

Table 2

Fig.10

Fig.11

Fig.12

Fig.13

Table 3

Optimal parameter value in low temperature environments

温度/℃	初始充填压力/MPa	初始充气体积/cm³	压气面积/m²
$- 40$	$1.818 520$	$660.01$	$0.002 203$
$- 30$	$1.704 350$	$641.22$	$0.002 238 8$
$- 20$	$1.648 956$	$637.64$	$0.002 190 9$

Table 3

Fig.14

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Digital-twin-driven reliability domain analysis for aircraft landing gear systems

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