Electric Thrusters- Combining Particle-in-cell plasma simulations and Machine Learning
Abstract
The aim of the present project is to combine the Particle in Cell (PIC) approach with Machine Learning to advance the study of electric propulsion for spacecraft. This effort will focus on two concepts of thruster of special current interest- the famous Hall and the more frontier reconnection thrusters that received considerable attentions in recent years. This research will make first principle full PIC simulations in both two-dimensions and three-dimensions. This study will focus, for the Hall thruster, on the Landmark benchmark and for the reconnection thruster on our past investigations. Once satisfactory PIC simulations have been generated, machine learning will be utilized to discover the equations that can best model the behavior using simpler fluid models. To reveal these equations, a neural network approach will be used in tandem with sparsity-based model-discovery techniques. These can lead to the “correct� closure relations to be used to capture the essence of the full kinetic approach in a reduced model.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 22, 2024
- Source ID
- FA86552317030
Entities
People
- Giovanni Lapenta
Organizations
- Air Force Office of Scientific Research
- Katholieke Universiteit Leuven
- United States Air Force