基于物理信息神经网络（PINN）的带有捆绑带的降落伞悬挂线展开的力学分析
Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN
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中文总结 AI 辅助
研究降落伞悬挂线展开时的力学问题，提出基于物理信息神经网络（PINN）算法预测张力，该算法在计算效率和精度上优于传统方法，还研究了捆绑带参数对线动态张力的调节规律，经对比验证了PINN框架的可靠性和有效性。
中文摘要 AI 辅助
降落伞广泛应用于航空、航天和救生任务中。作为降落伞展开的初始阶段，悬挂线的抽出和拉直直接决定了后续充气过程的顺利进行。这个超短过程涉及复杂的动态载荷变化。大多数现有研究采用常微分方程的数值积分来计算绳索张力，但该方法无法快速获取沿悬挂线任意位置的张力值。本文开发了一种物理信息神经网络（PINN）算法，用于在绳索抽出和拉直过程中预测张力，在计算效率和数值精度方面均优于传统积分方法。此外，还研究了捆绑带参数对线动态张力的调节规律。与飞行测试数据和传统数值结果的对比验证了所提出的PINN框架的可靠性和有效性。
英文摘要
Parachutes are widely utilized in aviation, aerospace and lifesaving missions. As the initial stage of parachute deployment, suspension line extraction and straightening directly determines the smooth implementation of subsequent inflation procedures. This ultra-short process involves intricate dynamic load variations. Most existing studies adopt numerical integration of ordinary differential equations to calculate line tension, yet this method fails to rapidly acquire tension values at arbitrary positions along suspension lines. This paper develops a physics-informed neural network (PINN) algorithm for tension prediction during line extraction and straightening, which outperforms traditional integration methods in both computational efficiency and numerical accuracy. Furthermore, the regulatory law of binding tape parameters on line dynamic tension is investigated. Comparative validations against flight test data and conventional numerical results verify the reliability and effectiveness of the proposed PINN framework.
发表机构
- Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
- Beijing Institute of Space Mechanics and Electricity（北京空间机电研究所）
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