Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN

2026-07-14

AI Summary

This study addresses the challenge of efficiently and accurately estimating suspension line tension during the parachute deployment phase, a critical factor limiting the analysis of inflation stability. For the first time, physics-informed neural networks (PINNs) are introduced to this domain, yielding a tension prediction model that embeds parachute dynamical equations to enable high-fidelity, real-time, full-field tension estimation at arbitrary locations. Validation against flight test data and conventional numerical simulations demonstrates that the proposed model significantly outperforms standard approaches in both computational efficiency and accuracy. Furthermore, the model elucidates how harness parameters govern tension evolution, highlighting its strong engineering applicability and reliability for practical parachute system design and analysis.

Abstract

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.

Problem

• parachute deployment
• suspension line tension
• dynamic load
• binding tapes
• tension prediction

Innovation

• Physics-Informed Neural Network
• Parachute Suspension Line
• Dynamic Tension Prediction
• Binding Tape Parameter
• Deployment Dynamics