用物理神经网络预测降落伞绳索展开时的张力，更快更准。
Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN
- 基于物理信息神经网络，直接预测绳索任意位置张力。
- 计算效率和精度均优于传统微分方程求解方法。
- 揭示绑带参数对动态张力的调控规律，适合航天降落伞设计。
降落伞广泛应用于航空、航天及救生任务中。作为开伞初始阶段的悬索展开与伸直过程，直接影响后续充气的顺利进行。该超短过程涉及复杂的动态载荷变化。现有研究多采用常微分方程数值积分计算绳索张力，但难以快速获取任意位置的张力值。本文提出一种物理信息神经网络（PINN）算法，用于悬索展开与伸直过程中的张力预测，其在计算效率和数值精度上均优于传统积分方法。此外，研究了绑带参数对悬索动态张力的调控规律。通过与飞行试验数据及传统数值结果对比验证，表明所提出的PINN框架具有可靠性和有效性。
原文摘要 · Abstract (English)
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.
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