• Bayesian inference
• fluid models
• Hall thruster
• anomalous diffusivity

The aim of the thesis is to study the anomalous electron diffusivity using Integrated Data Analysis techniques, which rely on Bayesian modelling methodologies to combine a fluid computational model of the plasma discharge in a Hall thruster with experimental measurements of different nature. Experimental data includes thruster discharge current, thrust balance and ionic probe measurements.
The combination of physics-based method with probabilistic techniques allows us to manage this phenomenon.
In this data-driven model the anomalous diffusivity is studied through the calibration of free parameters, the injection velocity, the wall interaction coefficient, and the electron mobility coefficient.
The problem is formulated as a Bayesian inference problem to properly deal with the underlying uncertainty of the experimental measurements and how they impact on the uncertainty of the inferred anomalous probability profiles.
The developed methodology was applied to experimental measurements of a 5kW and a 20 kW Hall thruster prototypes operating at different regimes with various operating conditions and propellant. Being probability distributions, not only the uncertainty can be assessed, but also the study of the correlation between the different parameters permits to gain further insight on the underlying relations between the different problem parameters.