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Space plasma instrument concept and analysis using simulation and machine learning techniques

  • Author / Creator
    Liu, Guangdong
  • Much interest has been drawn toward our near-Earth space with the advent of the space-flight era. In order to better understand this highly dynamic environment, the development of measuring instruments for use on near-Earth spacecraft has become particularly important. The current inference techniques often rely on analytic formulas with many assumptions. Some of those assumptions are not well-satisfied in experimental conditions, thus leading to uncontrolled uncertainties in the inferred physical parameters. With the development of computer hardware in the 21st century, the computational power available enables us to do new science. Particle in cell (PIC) simulations can be used to simulate the satellite and instrument interactions with space environment under various space plasma and satellite conditions relevant to near-Earth orbit. The response of the instrument to the various space plasma and satellite conditions is used to construct a synthetic solution library. Following machine learning techniques, the library can be used to create multivariate regression models based on neural networks and Radial basis functions (RBF). The advantage of using a simulation approach is that it provides uncertainties in the inferred physical parameters, and it can account for more physical processes than can be accounted for in an analytical approach. The drawback is that PIC simulations are time-consuming, whereas inferences made with analytic formulas can be much faster. The multivariate regression approach combines all those advantages: it provides uncertainties, it accounts for realistic physical processes, and it can make inferences very efficiently. Two selected instruments are studied using the simulation-based regression approach. RBF-one of the regression approach is also modified to be more efficient.

  • Subjects / Keywords
  • Graduation date
    Fall 2022
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/r3-tszc-xr73
  • License
    This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.