用深度生成模型生成物理上合理的降落伞动力学
Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
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- PhD Candidate, Mechanical and Aerospace Engineering, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学机械与航空航天工程系博士研究生）
- Group Supervisor, Dynamics, Modeling, Flight Mechanics & UQ, 4800 Oak Grove Dr, M/S 198-326, Pasadena, CA 91011, AIAA Member（加州帕萨迪纳AIAA会员，动力学、建模、飞行力学及不确定性量化组负责人）
- Assistant Professor, Mechanical and Aerospace Engineering, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学机械与航空航天工程系助理教授）
- Assistant Professor, Center for Statistics and Machine Learning, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学统计与机器学习中心助理教授）
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中文总结 AI 辅助
研究针对行星降落伞和进入飞行器系统动力学建模难题，提出辛降落伞生成对抗网络（SPar-GAN），通过物理感知生成建模直接从数据学习动力学，应用于亚比例测试，能准确再现动力学并恢复相空间，可减少物理测试量。
中文摘要 AI 辅助
准确模拟行星降落伞和进入飞行器系统的动力学对于诸如飞行器分离和传感器激活等进入、下降和着陆事件至关重要。传统系统识别方法难以捕捉这些动力学，因为降落伞运动高度非线性、控制方程不完全清楚且相关测试数据稀缺昂贵。本文利用物理感知生成建模方法直接从数据中学习降落伞动力学来规避这些挑战。提出的辛降落伞生成对抗网络（SPar-GAN）通过以伞衣设计和自由流速度为条件，将哈密顿生成架构应用于降落伞设置，同时通过辛积分强制能量守恒。将SPar-GAN应用于在国家全尺寸空气动力学综合体进行的亚比例降落伞测试，结果表明它能定性准确地再现不同降落伞配置的俯仰-偏航动力学，同时恢复与伞衣轴对称一致的紧凑二自由度相空间。这些结果表明物理约束生成模型可以表征不同工况下的降落伞动力学，并可能有助于减少评估性能所需的物理测试量。
英文摘要
Accurately modeling the dynamics of planetary parachute and entry vehicle systems is critical for Entry, Descent, and Landing events such as vehicle separation and sensor activation. These dynamics are difficult to capture with traditional system-identification methods as parachute motion is highly nonlinear, the governing equations are not fully known, and relevant test data are scarce and expensive to acquire. In this work, we sidestep these challenges by leveraging a physics-aware generative modeling approach that learns parachute dynamics directly from data. The proposed method, Symplectic Parachute Generative Adversarial Network (SPar-GAN), adapts a Hamiltonian generative architecture to the parachute setting by conditioning on canopy design and freestream velocity, while enforcing conservation of energy through symplectic integration. We apply SPar-GAN to subscale parachute tests conducted at the National Full-Scale Aerodynamics Complex and show that it reproduces qualitatively accurate pitch-yaw dynamics of different parachute configurations while recovering a compact two-degree-of-freedom phase-space consistent with canopy axisymmetry. These results suggest that physics-constrained generative models can characterize parachute dynamics across operating conditions and may help reduce the volume of physical testing required to assess performance.
