Graduate Thesis Or Dissertation
 

Design of Ion Thruster Optics Simulator and a Neural Network Emulator

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/d791sq02t

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  • The grid geometry plays a vital role in the performance of gridded ion thrusters. It can dramatically impact the erosion rate and thrust. Designers push the limits of electric in-space propulsion by optimizing grid parameters through simulation. At the same time, computational models of plasma physics are intensive. A baseline simulation model is introduced which employs a second-order physics approximation and the particle in cell method. Secondly, a neural network is trained to approximate design performance via supervised learning on a subset of previously developed model simulation solutions. Training inputs to the neural network are the grid geometry and voltages while the thrust achieved for each solution case is used as an output parameter. In testing, the neural network can predict the thrust performance for engines that it has not previously seen. This study aims to understand better the effectiveness of surface models trained on higher fidelity simulations to increase the efficiency of parametric design studies. The neural network accurately predicted the thrust performance of a given grid design. As predicted, the network demonstrated orders of magnitude reduction in simulation time. However, test cases must be within the parametric limits of the training design cases. This method appears reasonably suitable for the rapid approximation of computational simulations. This thesis extends a previous low-fidelity pilot study by the authors for the same case study problem. Future work includes: techniques for bounding and reducing error and physical testing.
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  • Pending Publication
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  • 2022-03-25 to 2022-10-25

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