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A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa
A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa
Dissertations
This dissertation explores a novel method of solving low-thrust spacecraft targeting problems using reinforcement learning. A reinforcement learning algorithm based on Deep Deterministic Policy Gradients was developed to solve low-thrust trajectory optimization problems. The algorithm consists of two neural networks, an actor network and a critic network. The actor approximates a thrust magnitude given the current spacecraft state expressed as a set of orbital elements. The critic network evaluates the action taken by the actor based on the state and action taken. Three different types of trajectory problems were solved, a generalized orbit change maneuver, a semimajor axis change maneuver, …
Improving Boron For Combustion Applications, Kerri-Lee Annique Chintersingh
Improving Boron For Combustion Applications, Kerri-Lee Annique Chintersingh
Dissertations
Boron has received much attention as a potential additive to explosives and propellants due to its high theoretical gravimetric and volumetric heating values. The challenge, however, is that boron particles tend to agglomerate, have lengthy ignition delays and very low combustion rates. Prior research indicates that boron’s long ignition delays are due to its inhibiting naturally occurring oxide layer, impeding the diffusion of reactants for oxidation. For combustion, current studies report that boron particles have two consecutive stages, but the actual reaction mechanism is poorly understood. Despite many years of relevant research, quantitative combustion data on micron-sized boron particles are …