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Full-Text Articles in Mechanical Engineering
Gearbox Baffle Optimization, Megan Arduin
Gearbox Baffle Optimization, Megan Arduin
Masters Theses
Current literature reveals there is limited consensus on the placement of baffles within a gearbox to reduce churning losses. Thus, there is a need for a process to identify baffle clearances that result in maximum and minimum churning losses. There are two types of baffles: axial and radial. While both axial and radial baffles cause reductions in churning losses to various degrees, the focus is on the effect of radial baffles. The effect of a board (rectangular plate) baffle location on the churning losses of a single gear gearbox are evaluated using computational fluid dynamics (CFD) implemented in Ansys. Several …
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, …