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<p>Arresting gear is the key equipment to ensure the safe and short-range landing of a carrier-based aircraft, which is a complex electromechanical-hydraulic system. Accurately and efficiently modeling of arresting gear system is critical to enhance its service life and safety. However, there is few researches reported that can achieve this goal at the same time, which hinders the quantitative analysis of the effects of various factors, such as velocity, mass, eccentricity etc., on the dynamic response of the arresting gear. Thus, this paper proposed an efficient model for solution of arresting gear dynamic responses based on BP neural network optimized by whale optimization algorithm (WOA-BPNN). And, based on this model a sensitivity study was carried out to quantitatively analyze the effects of various factors on the dynamic response of the arresting gear. First, a mathematical model of the arresting gear based on multibody dynamics and hydraulic system analysis was established, and its effectiveness was validated by the experiment data. Second, the dynamic response of the arresting gear under various factors was figured out by the proposed mathematical model, and a WOA-BPNN was established based on the obtained data, which could predict the dynamic response of the arresting gear with high efficiency and fidelity. Then, the Sobol global sensitivity analysis method was employed to evaluate the influence of each landing parameter on the dynamic response. Finally, the maximum cable force of the arresting gear was studied as an example of the dynamic response of the solution. According to the results, it could be concluded that the landing velocity and landing mass were the dominant factors affecting the cable force, and there were interactions among landing parameters. The proposed model could efficiently figure out the dynamic response of each component in an arresting gear and identify the most influential landing parameter, which could lay a foundation for enhancing the operational reliability of an arresting gear and optimizing the structure of the arresting gear.</p>

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Modeling of dynamic responses of arresting gear and its sensitivity study based on BP neural network optimized by Whale optimization algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Qi Deng,
  • Jisong Wang,
  • Jingbo Gai,
  • Yao Jian,
  • Yupeng Wang,
  • Mingsheng Zheng

摘要

Arresting gear is the key equipment to ensure the safe and short-range landing of a carrier-based aircraft, which is a complex electromechanical-hydraulic system. Accurately and efficiently modeling of arresting gear system is critical to enhance its service life and safety. However, there is few researches reported that can achieve this goal at the same time, which hinders the quantitative analysis of the effects of various factors, such as velocity, mass, eccentricity etc., on the dynamic response of the arresting gear. Thus, this paper proposed an efficient model for solution of arresting gear dynamic responses based on BP neural network optimized by whale optimization algorithm (WOA-BPNN). And, based on this model a sensitivity study was carried out to quantitatively analyze the effects of various factors on the dynamic response of the arresting gear. First, a mathematical model of the arresting gear based on multibody dynamics and hydraulic system analysis was established, and its effectiveness was validated by the experiment data. Second, the dynamic response of the arresting gear under various factors was figured out by the proposed mathematical model, and a WOA-BPNN was established based on the obtained data, which could predict the dynamic response of the arresting gear with high efficiency and fidelity. Then, the Sobol global sensitivity analysis method was employed to evaluate the influence of each landing parameter on the dynamic response. Finally, the maximum cable force of the arresting gear was studied as an example of the dynamic response of the solution. According to the results, it could be concluded that the landing velocity and landing mass were the dominant factors affecting the cable force, and there were interactions among landing parameters. The proposed model could efficiently figure out the dynamic response of each component in an arresting gear and identify the most influential landing parameter, which could lay a foundation for enhancing the operational reliability of an arresting gear and optimizing the structure of the arresting gear.