Coupled global and PIC modelling of the REGULUS cathode-less plasma thrusters operating on xenon, iodine and krypton

Andrews, Shaun; Andriulli, Raoul; Souhair, Nabil; Fede, Simone Di; Pavarin, Daniele; Ponti, Fabrizio De; Magarotto, Mirko

doi:10.1016/j.actaastro.2023.03.015

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…It is worth noting that the control loop described in this section is derived from previous studies [11,47,48,53,54,86,87,91,92] in which successful validations are presented.…”

Section: Control Loopmentioning

confidence: 99%

“…Configuration C has been simulated accounting for the collisions described in section 2.1. The mass utilization efficiency has been assumed equal to η u = ṁi / ṁ = 0.98 for both thrusters [92], where ṁi the ion mass flow rate at the thruster outlet. The plasma potential ϕ is depicted in figure 15 and the ion plasma density n i in figure 16.…”

Section: Appendix Cmentioning

confidence: 99%

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Magnetic nozzle performance in a cluster of helicon plasma thrusters

Di Fede,

Manente,

João Comunian

et al. 2023

Plasma Sources Sci. Technol.

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A numerical study of the plasma dynamics in a Helicon Plasma Thrusters' (HPT) cluster is presented. For the first time in the literature, the three-dimensional (3D) plasma dynamics occurring in the plume of a HPTs' cluster is analyzed. The physical investigation relies on ProPic, a 3D Particle-In-Cell (PIC) code specifically designed to simulate the plasma dynamics in magnetic nozzles and in a non-axi-symmetric domain. The code has been validated against experiments reported in the literature and cross-validated with Starfish, an open-source two-dimensional PIC software. The physical investigation has revealed an interesting mutual influence between the thrusters that constitute the cluster. Three significant phenomena that affect the cluster's performance have been identified. The first phenomenon is related to the effect that clustering has on the shape of the magnetic field lines and, in turn, on the divergence angle of the plume. The second phenomenon is related to electron currents flowing among different thrusters, which affect the potential drop across the plume. The third phenomenon is related to the effect that neighboring thrusters have on the plasma potential map and, in turn, on the expansion of the ions.

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“…It is worth noting that the control loop described in this section is derived from previous studies [11,47,48,53,54,86,87,91,92] in which successful validations are presented.…”

Section: Control Loopmentioning

confidence: 99%

Section: Appendix Cmentioning

confidence: 99%

Magnetic nozzle performance in a cluster of helicon plasma thrusters

Di Fede,

Manente,

João Comunian

et al. 2023

Plasma Sources Sci. Technol.

Self Cite

View full text Add to dashboard Cite

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Predicting the antenna properties of helicon plasma thrusters using machine learning techniques

Malm,

Souhair,

Rossi

et al. 2024

J Electr Propuls

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When designing helicon plasma thrusters, one important characteristic is the impedance of the radio-frequency antenna that is used to deposit power into the plasma. This impedance can be characterized both experimentally and numerically. Recently, a numerical tool capable of predicting the antenna impedance, called Adamant, has been developed. However, Adamant takes a long time to run and has high computer resource demands. Therefore, this work has been done to evaluate whether machine learning models, trained on Adamant-generated data, can be used instead of Adamant for small design change evaluations and similar works. Six different machine learning models were implemented in MATLAB: decision trees, ensembles, support vector machines, Gaussian process regressions, generalized additive models and artificial neural networks. These were trained and evaluated using nested k-fold cross-validation with the hyperparameters selected using Bayesian optimization. The performance target was to have less than 5% error on a point-to-point basis. The artificial neural network performed the best when taking into account both maximum error magnitudes and generalization ability, with a maximum error of 3.98% on the test set and with considerably better performance than the other models when tested on some practical examples. Future work should look into different solver algorithms for the artificial neural network to see if the results could be improved even further. To expand the model’s usefulness it might also be worth looking into implementing different antenna types that are of interest for helicon plasma thrusters.

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