  Hall-effect thrusters (HETs) are widely used for modern near-earth spacecraft
propulsion and are vital for future deep-space missions. Methods of modeling
HETs are developing rapidly. However, such methods are not yet precise enough
and cannot reliably predict the parameters of a newly designed thruster, mostly
due to the enormous computational cost of a HET plasma simulation. Another
approach is to use scaling techniques based on available experimental data.
This paper proposes an approach for scaling HETs using neural networks and
other modern machine learning methods. The new scaling model was built with
information from an extensive database of HET parameters collected from
published papers. Predictions of the new scaling model are valid for the
operating parameters domain covered by the database. During the design, this
model can help HET developers estimate the performance of a newly-designed
thruster. At the stage of experimental research, the model can be used to
compare the achieved characteristics of the studied thruster with the level
obtained by other developers. A comparison with the state-of-the-art HET
scaling model is also presented.

Plasma Physics