Maneuver Optimization through Simulated Annealing

Computer Science – Performance

Scientific paper

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Scientific paper

We developed an efficient method for satellite maneuver optimization. It is based on a Monte Carlo (MC) approach in combination with Simulated Annealing. The former component enables us to consider all imaginable trajectories possible given the current satellite position and its available thrust, while the latter approach ensures that we reliably find the best global optimization solution. Furthermore, this optimization setup is eminently scalable. It runs efficiently on the current multi-core generation of desktop computers, but is equally at home on massively parallel high performance computers (HPC). The baseline method for desktops uses a modified two-body propagator that includes the lunar gravitational force, and corrects for nodal and apsidal precession. For the HPC environment, on the other hand, we can include all the necessary components for a full force-model propagation: higher gravitational moments, atmospheric drag, solar radiation pressure, etc.
A typical optimization scenario involves an initial orbit and a destination orbit / trajectory, a time period under consideration, and an available amount of thrust. After selecting a particular optimization (e.g., least amount of fuel, shortest maneuver), the program will determine when and in what direction to burn by what amount. Since we are considering all possible trajectories, we are not constrained to any particular transfer method (e.g., Hohmann transfers). Indeed, in some cases gravitational slingshots around the Earth turn out to be the best result. The paper will describe our approach in detail, its complement of optimizations for single- and multi-burn sequences, and some in-depth examples. In particular, we highlight an example where it is used to analyze a sequence of maneuvers after the fact, as well as showcase its utility as a planning and analysis tool for future maneuvers.

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