special column

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

  • Yuqian ZHANG ,
  • Yongge LI ,
  • Xiaochuan LIU ,
  • Yong XU
Expand
  • 1.School of Mathematics and Statistics,Northwestern Polytechnical University,Xi’an 710072,China
    2.MOE Key Laboratory for Complexity Science in Aerospace,Northwestern Polytechnical University,Xi’an 710072,China
    3.National Key Laboratory of Strength and Structural Integrity,Aircraft Strength Research Institute of China,Xi’an 710065,China

Received date: 2025-05-19

  Revised date: 2025-06-10

  Accepted date: 2025-07-01

  Online published: 2025-07-15

Supported by

National Natural Science Foundation of China(52225211);Aeronautical Science Foundation of China(20230041053005);Northwestern Polytechnical University“1-0” Major Engineering Science Problem Project(G2024KY0613)

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.

Cite this article

Yuqian ZHANG , Yongge LI , Xiaochuan LIU , Yong XU . Digital-twin-driven reliability domain analysis for aircraft landing gear systems[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(24) : 632256 -632256 . DOI: 10.7527/S1000-6893.2025.32256

References

[1] 惠旭龙, 刘小川, 白春玉, 等. 民机机身框段和全机坠撞响应的对比研究[J/OL]. 航空学报,(2025-01-21)[2025-02-18]. .
  XI X L, LIU X C, BAI C Y, et al. Research on the com parison of crash response between the fuselage section and full-scale civil aircraft[J/OL]. Acta Aeronautica et Astronautica Sinica, (2025-01-21)[2025-02-18]. (in Chinese).
[2] SWATI R F, ASFANDYAR AMJAD M, TALHA M, et al. Crashworthiness study of UCAV’s main landing gear using explicit dynamics[J]. International Journal of Crashworthiness202227(6): 1843-1859.
[3] 朱晨辰, 王彬文, 刘小川, 等. 复杂环境下起落架动力学行为研究现状与展望[J]. 航空科学技术202334(1): 1-11.
  ZHU C C, WANG B W, LIU X C, et al. Research status and prospect of landing gear dynamics in complex environment[J]. Aeronautical Science & Technology202334(1): 1-11 (in Chinese).
[4] HEININEN A A. Modeling and simulation of an aircraft main landing gear shock absorber[D]. Tampere: Tampere University of Technology, 2015: 34-40.
[5] 胡锐, 牟让科, 宋得军, 等. 温度对油: 气式起落架缓冲性能的影响研究[J]. 航空工程进展202213(3): 150-156.
  HU R, MU R K, SONG D J, et al. Research on the influence of temperature on the cushioning performance of oil-air landing gear[J]. Advances in Aeronautical Science and Engineering202213(3): 150-156 (in Chinese).
[6] 方威, 朱林刚, 王友善. 环境温度对飞机起落架缓冲性能影响分析[J]. 机械设计与制造工程202150(11): 76-80.
  FANG W, ZHU L G, WANG Y S. Analysis of ambient temperature influence to landing gear shock absorber performance of aircraft[J]. Machine Design and Manufacturing Engineering202150(11): 76-80 (in Chinese).
[7] 陈艺夫, 马宇航, 蓝庆生, 等. 基于多项式混沌法的翼型不确定性分析及梯度优化设计[J]. 航空学报202344(8): 127446.
  CHEN Y F, MA Y H, LAN Q S, et al. Uncertainty analysis and gradient optimization design of airfoil based on polynomial chaos expansion method[J]. Acta Aeronautica et Astronautica Sinica202344(8): 127446 (in Chinese).
[8] LUO C Q, ZHU S P, KESHTEGAR B, et al. Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy[J]. Computer Methods in Applied Mechanics and Engineering2024423: 116863.
[9] 杨倩, 郭晓峰, 李芹, 等. 基于POD和代理模型的热气防冰性能预测方法[J]. 航空学报202344(1): 626992.
  YANG Q, GUO X F, LI Q, et al. Hot air anti-icing performance estimation method based on POD and surrogate model[J]. Acta Aeronautica et Astronautica Sinica202344(1): 626992 (in Chinese).
[10] 刘佳奇, 冯蕴雯, 路成, 等. 基于智能神经网络的航空发动机运行安全分析[J]. 航空学报202243(9): 625375.
  LIU J Q, FENG Y W, LU C, et al. Safety analysis of aero-engine operation based on intelligent neural network[J]. Acta Aeronautica et Astronautica Sinica202243(9): 625375 (in Chinese).
[11] LIU Q, XU Y, LI Y G, et al. Fixed-interval smoothing of an aeroelastic airfoil model with cubic or free-play nonlinearity in incompressible flow[J]. Acta Mechanica Sinica202137(7): 1168-1182.
[12] FENG J, WANG X L, LIU Q, et al. Fusing deep learning features for parameter identification of a stochastic airfoil system[J]. Nonlinear Dynamics2025113(5): 4211-4233.
[13] TORZONI M, TEZZELE M, MARIANI S, et al. A digital twin framework for civil engineering structures[J]. Computer Methods in Applied Mechanics and Engineering2024418: 116584.
[14] TAO F, QI Q. Make more digital twins[J]. Nature2019573(7775): 490-491.
[15] 郭丞皓, 于劲松, 宋悦, 等. 基于数字孪生的飞机起落架健康管理技术[J]. 航空学报202344(11): 227629.
  GUO C H, YU J S, SONG Y, et al. Application of digital twin-based aircraft landing gear health management technology[J]. Acta Aeronautica et Astronautica Sinica202344(11): 227629 (in Chinese).
[16] 朱晨辰, 王彬文, 马晓利, 等. 考虑温度效应的起落架落震缓冲性能研究[J]. 应用力学学报202340(1): 25-33.
  ZHU C C, WANG B W, MA X L, et al. Landing gear shock buffering performance considering the temperature effect[J]. Chinese Journal of Applied Mechanics202340(1): 25-33 (in Chinese).
[17] 张峰, 杨旭锋, 刘永寿, 等. 飞机起落架缓冲器参数可靠性灵敏度分析[J]. 振动工程学报201528(1): 67-72.
  ZHANG F, YANG X F, LIU Y S, et al. Reliability parameter sensitivity analysis for aircraft landing gear shock absorber[J]. Journal of Vibration Engineering201528(1): 67-72 (in Chinese).
[18] SOARES C, BRAZDIL P B, KUBA P. A meta-learning method to select the kernel width in support vector regression[J]. Machine Learning200454(3): 195-209.
[19] DEHGHANI M, MONTAZERI Z, TROJOVSKá E, et al. Coati optimization algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems[J]. Knowledge-Based Systems2023259: 110011.
[20] GUIMAR?ES H, MATOS J C, HENRIQUES A A. An innovative adaptive sparse response surface method for structural reliability analysis[J]. Structural Safety201873: 12-28.
[21] TIZHOOSH H R. Opposition-based learning: A new scheme for machine intelligence[C]∥International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06). Piscataway: IEEE Press, 2005: 695-701.
[22] YAN A J, HU K C. Improved strategy and its application to the optimization of seagull optimization algorithm[J]. Information and Control202251(6): 688-698.??
[23] TANYILDIZI E, DEMIR G. Golden sine algorithm: A novel math-inspired algorithm[J]. Advances in Electrical and Computer Engineering201717(2): 71-78.
[24] WU Y T. Computational methods for efficient structural reliability and reliability sensitivity analysis[J]. AIAA Journal199432(8): 1717-1723.
[25] WU Y T, MOHANTY S. Variable screening and ranking using sampling-based sensitivity measures[J]. Reliability Engineering & System Safety200691(6): 634-647.
[26] XUE J K, SHEN B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing202379(7): 7305-7336.
[27] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software201469: 46-61.
[28] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software201695: 51-67.
Outlines

/