Machine Learning: A novel approach to infer plasma parameters from probe measurements

• Author / Creator

  Olowookere, Akinola

• The research reported in this thesis is multidisciplinary in nature. It presents the use
  of kinetic simulations, state of the art data analysis, and machine learning techniques
  to infer plasma parameters and satellite parameter from Langmuir probe measurement.
  Physical parameters such as plasma state variables, are generally inferred from
  probe measurements using analytic or empirical formulas derived by applying different
  approximations to physical theories. The objective of this work is to develop more
  accurate techniques to infer physical parameters from probe measurements under
  more realistic plasma conditions with quantifiable uncertainties and with a potential
  for incremental improvements by adding more complex physical processes. Threedimensional
  particle-in-cell simulations are used to calculate the current collected
  by fixed-bias spherical Langmuir probes relative to the satellite under conditions of
  increasing realism, starting with an isolated probe attached to a guard, to probes
  attached to a satellite. The advantage of fixed bias probes considered in cases studied
  throughout this thesis, is their higher temporal and spatial resolutions, compared
  to the more standard mode operation where bias voltages are swept in time. The
  calculated currents and the plasma and satellite parameters assumed in the simulations
  are then used to build a solution library or synthetic data set, to construct
  regression models to infer parameters of interest such as the satellite potential, the
  density, the plasma flow velocity, and the ratio between the plasma density and the
  square root of the temperature. The solution library is randomly split into two disjoint
  sets; one to train models, and the other to validate inferences, and assess the
  skill of the models and quantify their uncertainties. Different approaches are used to train models, including radial basis function (RBF) regressions, deep neural networks,
  and combinations of these methods with analytic estimates using the boosting
  ensemble learning technique, and affine transformations. In the combined approach,
  the parameters of interest are first estimated using analytic expressions, followed by
  regressions to reduce inference errors. Simple affine transformations are also applied
  to improve the accuracy of analytic inferences, when the Pearson correlation coefficient
  R with known values is high. In each case, models’ inference skills are assessed
  using different metrics to quantify discrepancies compared with known values in synthetic
  data sets. The models show excellent performance with the maximum relative
  error ranging from 7% to 12% for the density and the ratio of density and the square
  root of temperature, and a maximum absolute error in the range of 0.2 V to 0.4 V for
  the floating potential in all the cases considered. The models are applied to in situ
  data, and the inferences are compared to in-situ data from satellites, with which they
  show excellent qualitative agreement. Finally, the agreements between the models
  inferences and the synthetic data values indicate that the approaches used in this
  thesis are promising with the advantage of producing uncertainty margins that are
  specifically related to the inference techniques used.

• Subjects / Keywords

  • Langmuir Probe
  • Kinetic Simulation
  • Space Plasma
  • Regression Techniques
  • Swarm Satellite

• Graduation date

  Fall 2022

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-r878-3d17

• 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.