A shape -based approach to automated, low-thrust, gravity -assist trajectory design

Anastassios Evangelos Petropoulos, Purdue University

Abstract

It is well known that highly efficient, continuous-thrust propulsion and use of the gravity assist concept each provide significant benefits in two-body trajectory design. The coupling of high specific impulse, low-thrust propulsion with gravity assists is a natural next step in the development of highly efficient trajectories for deep space missions. In the literature, the design of such trajectories is typically treated as an optimisation problem, which can be solved by a variety of techniques. However, all techniques need some sort of initial guess for at least part of the solution, and even with such a guess, experience has shown that convergence to an optimal solution, particularly in the case of multiple gravity assists, is a formidable challenge. In this thesis, rather than addressing the optimisation of particular initial guesses, we present a shape-based method for efficiently generating such initial guesses for low-thrust, gravity-assist trajectories. These initial guesses serve a twofold purpose: They provide mission designers with rapid, broad overviews of the trajectory design space, and they provide a starting point for trajectory optimisation. We demonstrate that, with an assumed shape, tangentially applied thrust will always yield analytic solutions for some portion of the shape, with the thrust magnitude determined a posteriori. We devise the exponential sinusoid shape and use it as the basis for broad, automated searches. Significantly, the automated searches can include any number of flyby bodies, multiple flyby trajectories being the most in need of initial guesses. Several examples of broad searches are shown for rendezvous and flyby trajectories of a variety of targets. Selected trajectories from these searches show a good correspondence with optimised trajectories reported in the literature, and are also successfully used as initial guesses in optimisation based on direct methods.

Degree

Ph.D.

Advisors

Longuski, Purdue University.

Subject Area

Aerospace engineering

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