Computationally Efficient and Scalable Optimization of Low-Thrust, Many-Revolution Trajectories in Multibody Dynamic Environments

One of the greatest challenges in spacecraft trajectory planning is the large computational cost of finding an optimal solution. This cost increases dramatically as the trajectory and dynamic environment become more complex. Recent advances in technology and space mission design have required and even utilized these complexities. “New Space”, the fast growing next-generation space industry, is increasingly making use of small spacecraft, such as CubeSats, often paired with low-thrust propulsion systems. These low-thrust trajectories are highly efficient and allow for a larger percentage of spacecraft mass to be used for payload when compared to conventional high thrust engines. However, a long Time of Flight (ToF) is usually required, and thus multiple revolutions around the primary body. Finding the optimal trajectory over multiple revolutions is a difficult problem with large computational costs, which is only exacerbated as the number of revolutions increases. Long ToF trajectories also allow significant influence of perturbations such as third (or more) body gravity, solar radiation pressure, and non-sphericity of the central body. These perturbations add significantly to the computational cost and difficulty of optimization. My proposed research will develop computationally efficient and HPC scalable methods for finding low-thrust optimal trajectories with many revolutions, specifically targeting 1,000 to 10,000 revolutions. I will then extend these methods to more complex dynamic models like multibody perturbations. Increasing the number of revolutions in trajectories can allow for more optimal solutions, and a focus on computational efficiency can enable this. This would contribute to NASA Technology Roadmap 5: Communications, Navigation, and Orbital Debris Tracking and Characterization Systems--specifically 5.4.2.1, by improving current trajectory optimization methods for low-thrust spacecraft in complex dynamic environments. By unlocking new many-revolution trajectories, this research would enable more efficient and innovative missions as a whole.

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