群伞充气参数重构与动力学建模方法研究

Research on Inflation Parameter Reconstruction and Dynamic Modeling Methods for Cluster Parachute Systems

摘要: 群伞系统在充气过程中表现出强烈的非线性与时变性，传统的基于经验或试错的参数提取方法效率低下且精度有限，难以支持高保真的动力学建模与系统优化设计。本文基于猎户座飞船降落伞系统的充气载荷重建方法，结合群伞系统飞行试验数据，开展了群伞充气参数自动重构与充气阶段动力学建模研究。首先，基于主伞吊带拉力传感器数据，建立降落伞的气动载荷重构模型，实现阻力面积的高精度反演；其次，引入参数优化算法自动拟合充气过程中的阻力面积增长曲线，提取充气时间常数、充气形态指数等关键参数，实现参数化建模与自动重构；在此基础上，建立了多级展开与动态耦合的群伞充气阶段动力学模型，并通过与实测数据的对比验证模型的有效性。以典型群伞系统为例开展案例研究，结果表明，该方法可显著提升参数重构效率与精度，重构模型的速度变化曲线、高度变化曲线与试验数据吻合良好，开伞动载计算值与试验数据误差在10%以内，具备良好的工程适用性与预测能力。

Abstract: The cluster parachute system exhibits strong nonlinearity and time-varying characteristics during the inflation process. Traditional empirical or trial-and-error-based parameter extraction methods are inefficient and limited in accuracy, making it difficult to support high-fidelity dynamic modeling and system optimization. Based on the inflation load reconstruction method of the Orion spacecraft parachute system, this paper combines flight test data of cluster parachute systems to conduct research on automatic reconstruction of inflation parameters and dynamic modeling during the inflation phase. First, an aerodynamic load reconstruction model for the parachute is established using the main parachute suspension line tension sensor data, achieving high-precision drag area inversion. Second, a parameter optimization algorithm is introduced to automatically fit the drag area growth curve during the inflation process, extracting key parameters such as the inflation time constant and inflation shape index, thereby enabling parametric modeling and automatic reconstruction. On this basis, a multi-stage deployment and dynamically coupled dynamic model for the inflation phase of the cluster parachute system is established, and the model’s validity is verified through comparison with measured data. A case study is conducted on a typical cluster parachute system. The results show that this method significantly improves the efficiency and accuracy of parameter reconstruction. The velocity and altitude variation curves obtained from the reconstructed model are in good agreement with the test data. The error between the computed opening shock load and the experimental data is within 10%, demonstrating good engineering applicability and predictive capability.