Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models

Abstract

Sustainability in space is now the centre of attention for different space research organisations given the scale of current investment in planetary search activities, ambitious plans for habitation in future, and focus on electric space propulsion systems. One potential propulsive means for future spacecraft is the applied-field magnetoplasmadynamic thruster (AF-MPDT). This type of thruster uses the principle of the Lorentz force, where ionized gas is propelled through the interaction of a current and a magnetic field. These thrusters are characterized by nonlinear and complex interaction between controllable parameters, such as current and magnetic field, and structural attributes like part dimensions including anode and cathode radii. Consequently, traditional empirical modelling approaches have encountered challenges in predicting certain outputs, such as thrust, with sufficient precision across different operational regimes. As an alternative to analytical/empirical formulas that approximate the true physics only partially, this paper demonstrates the potential of artificial intelligence (AI) techniques to predict thrust in AF-MPDTs. Through training and meticulous hyperparameter tuning, this study compares 7 different AI models fed with experimental data from 21 thrusters and their different configurations, reaching a total of 58 thruster designs, spanning decades of thruster research and development work. Results indicate that the supervised ensemble algorithm, eXtreme Gradient Boosting (XGBoost), outperforms all other utilized techniques such as random forest, Gradient Boosting Regressor, support vector regression, kernel ridge regression, K-nearest neighbors, and Gaussian process regression. With a Goodness of Fit ( R ^2 ) of 98.55%, root mean square error of 1.421 N, and mean absolute error of 0.453 N, XGBoost specifically, and AI in general, has demonstrated its superiority, by significantly improving on the accuracy of previously published empirical models for AF-MPDT thrust prediction. Additionally, the fast response associated with these techniques further expands their applicability to real-time data operation or to being used as a subroutine in thruster design procedure. This can potentially become a fundamental component of AF-MPDT designing software, whereby it may be used to check a configuration’s functionality, applicability and feasibility all in a few milliseconds. This data-driven approach can be helpful in upscaling or specially down-scaling AF-MPDTs to make them better suited for many lower power space applications.

Keywords

• aerospace
• benchmark
• intelligent model
• machine learning
• regression
• space electric propulsion