Deployment Dynamics and Optimization of Novel Space Antenna Deployable Mechanism

TL;DR

This paper presents a novel deployable space antenna mechanism, the TSDTM, optimized using AI methods, with demonstrated accuracy and potential for efficient large aperture deployment in space missions.

Contribution

Introduces the Triple Scissors Deployable Truss Mechanism and integrates AI-based optimization for material and geometric design in space antennas.

Findings

The TSDTM achieves minimal launch volume and maximum aperture size.

AI-based optimization yields highly accurate structural predictions.

Simulation and analytical results show less than 2% deviation in natural frequencies.

Abstract

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.