新型三剪刀展开桁架结构让大口径天线轻松上天，还能智能优化设计。
Deployment Dynamics and Optimization of Novel Space Antenna Deployable Mechanism
- 用三剪刀机构实现天线在轨高效展开，大幅节省发射空间。
- 机器学习预测的自振频率与仿真结果偏差仅1.94%，精度极高。
- 结合AI优化材料和结构参数，适合航天结构智能设计人群。
随着空间任务对大口径天线需求增加，如何将大型结构塞进小型运载火箭成为挑战，催生了可展开天线系统的设计。本文提出一种新型三剪刀可展开桁架机构（TSDTM），可在发射时收拢，入轨后高效展开，实现最大口径同时最小化发射体积。研究涵盖几何建模、基于螺旋理论与牛顿法的运动学分析、通过特征值与仿真方法进行的动力学分析，并利用SolidWorks完成验证。此外，基于支持向量机的材料选择优化与基于机器学习的几何构型优化算法被开发。所提TSDTM具有优良的结构动力学特性，仿真与解析预测结果高度一致。优化后的结构表现出极高的准确性，机器学习预测的自然频率与仿真值偏差仅为1.94%，证明了将AI方法应用于航天结构设计的潜力。
原文摘要 · Abstract (English)
Given the increasing need for large aperture antennas in space missions, the difficulty of fitting such structures into small launch vehicles has prompted the design of deployable antenna systems. The thesis introduces a new Triple Scissors Deployable Truss Mechanism (TSDTM) for space antenna missions. The new mechanism is to be stowed during launch and efficiently deploy in orbit, offering maximum aperture size while taking up minimal launch volume. The thesis covers the entire design process from geometric modeling, kinematic analysis with screw theory and Newtonian approaches, dynamic analysis by eigenvalue and simulation methods, and verification with SolidWorks. In addition, optimization routines were coded based on Support Vector Machines for material choice in LEO environments and machine learning method for geometric setup. The TSDTM presented has enhanced structural dynamics with good comparison between simulation and analytical predictions. The structure optimized proved highly accurate, with a deviation of just 1.94% between machine learning-predicted and simulated natural frequencies, demonstrating the potential of incorporating AI-based methods in space structural design.
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