Strengthening Ion Thrusters with CFD and Reinforcement Learning
Major
Physics
Second Major
Computer Science
Submission Type
Poster
Area of Study or Work
Computer Science, Physics
Faculty Advisor
Gabriel Spalding
Location
CNS Atrium
Start Date
4-13-2024 8:30 AM
End Date
4-13-2024 9:45 AM
Abstract
Ion thrusters have been explored by many scientists, from hobbyists to space exploration agencies, and they usually have low thrust output / thrust-to-weight ratio, despite the creation of many brilliant designs. We have been exploring methods to use computational fluid dynamics to simulate ion thrusters, with the intention of finding novel designs worthy of experimental implementation. Reinforcement learning often does well at discovering non-intuitive options for complicated design problems, when given a rich parameter space to explore, which in our case includes electrode voltage, electrode separation, chassis geometry, electrode geometry, etc. Our hope is to find a design that offers appreciable thrust output / thrust-to-weight ratio for manufacturable ion thrusters.
Strengthening Ion Thrusters with CFD and Reinforcement Learning
CNS Atrium
Ion thrusters have been explored by many scientists, from hobbyists to space exploration agencies, and they usually have low thrust output / thrust-to-weight ratio, despite the creation of many brilliant designs. We have been exploring methods to use computational fluid dynamics to simulate ion thrusters, with the intention of finding novel designs worthy of experimental implementation. Reinforcement learning often does well at discovering non-intuitive options for complicated design problems, when given a rich parameter space to explore, which in our case includes electrode voltage, electrode separation, chassis geometry, electrode geometry, etc. Our hope is to find a design that offers appreciable thrust output / thrust-to-weight ratio for manufacturable ion thrusters.