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Simulation and optimisation of a spacecraft propulsion system based on a Field Reversed Configuration plasmoid using deep learning and machine learning algorithms

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

This project aims to investigate an enabling technology for a more significant and complex system based on a High-Energy Density Electromechanical Thruster based on Stabilized Liner Compression of Plasma that could lead to manned exploration of the Solar system [1]. The project will look at the creation of a Field Reversed Configuration (FRC) plasmoid in a vacuum environment [2], using technology related to the Pulsed Inductive Thruster [3]. The FRC plasmoid is manipulated and transferred into a magnetic nozzle, where an expanding magnetic nozzle then accelerates it.
The project will focus on the plasma simulation of the FRC plasmoid as it transfers from the PIT thruster to and through the expanding magnetic nozzle. The work will use the Kingston University high performance cluster infrastructure running classical plasma simulation routines and newly developed routines based on deep learning algorithms.
If significant funding is obtained during the course of this PhD, a concerted effort will be to relate simulations to experimental results, but this is not currently a prime focus of this work as presented.
Aims of the project
• Understand the working principles and physics of FRC plasmoid creation and optimisation through simulation.
• Optimise the FRC plasmoid thruster through simulation and design a thruster for spacecraft applications.
• Scale up Particle-in-Cell (PIC) simulation code, using enhancements gained from deep learning algorithms, so PIC can be used to model large systems.
• Support research bid and funding work for this project.
Information about the candidate
The candidate should be self-motivated, determined and be able to show a quality of perseverance. There will be a significant amount of self-learning involved and the candidate should show they have the confidence to reach out and network with other academics internationally to achieve the project’s aims.
The candidate must have a keen interest in space propulsion technologies, having completed a postgraduate degree in a relevant field and achieved a minimum of an upper second-class degree (i.e. 2:1). Candidates should have experience, or show they have the aptitude with a majority of the following subject areas:
• Deep learning and machine learning
• Plasma Physics – Pulsed Plasma and Fusion Plasma
• Mega gauss magnetic fields
• Plasma Modelling & Simulations
• Electric Propulsion
• Space Technology

A significant aspect to this PhD will be the writing of research proposals to win external funding. Experience and demonstration of writing research bids that have won funding would be advantageous.

Funding Notes

There is no funding for this project


[[1] Turchi P.J., Frese S.D., Frese M.H., “High-Energy Density Electromechanical Thruster Based on Stabilized Liner Compression of Plasma”, 35th IEPC, Georgia Institute of Technology, Atlanta, Georgia, USA, October 8th -12th, 2017
[2] Weber T. E., “The Electrodeless Lorentz Force Thruster Experiment”, PhD Thesis, University of Washington, USA, 2010
[3] Mikellides P. G., “Pulsed Inductive Thruster (PIT): Modeling and Validation using the Mach2 code”, Department of Mechanical and Aerospace Engineering, Arizona State University.

How good is research at Kingston University in General Engineering?

FTE Category A staff submitted: 14.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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