A Macroscopic Model of Bipolar Corona Discharges for Ionic Thrusters Simulation
Abstract
Recent studies on Electro-Aero-Dynamic (EAD) thrusters have demonstrated that optimized, multi-stage, electrode arrangements can lead to substantial performance improvements. Within this context, numerical models can play a crucial role in the design process. Existing models can be broadly classified into full plasma models, that account for the chemical kinetics in the ionization region and reduced models, that rely on simplifying assumptions, e.g. Kaptzov's hypothesis, to avoid the computation of the ionization region. Full plasma models, despite their accuracy, are computationally demanding, especially when coupled with the Navier-Stokes equations. Reduced models are computationally cheaper and therefore are well suited for the design process. Compact design EAD thrusters that use multiple stages with alternating polarity have recently appeared in literature. Such a configuration allows smaller inter-stage distances, because the emitter(s) of a stage cannot be ignited by the collector(s) of the previous one, as no voltage difference is applied between them. In this work, we introduce a reduced bipolar corona discharge model coupled to the Navier-Stokes equations. A numerical simulation code is implemented based on the high performance finite element deal.II library. Our model can successfully simulate multi-stage thrusters with alternating polarity configurations and automatically detect the ignition pattern, when a configuration with multiple emitters is considered. Validation is performed against previous experimental and numerical campaigns, demonstrating the good predictive capabilities of our model.
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BibTeX
@misc{calio2026macroscopic,
title = {{A Macroscopic Model of Bipolar Corona Discharges for Ionic Thrusters Simulation}},
author = {Giuseppe Caliò and Francesco Fischetti and Lorenzo Valdettaro and Carlo De Falco and Paolo Barbante},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38556},
eprint = {2610.38556},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38556},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A Macroscopic Model of Bipolar Corona Discharges for Ionic Thrusters Simulation
AU - Giuseppe Caliò
AU - Francesco Fischetti
AU - Lorenzo Valdettaro
AU - Carlo De Falco
AU - Paolo Barbante
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.38556
UR - https://arxiv.org/abs/2610.38556
AB - Recent studies on Electro-Aero-Dynamic (EAD) thrusters have demonstrated that optimized, multi-stage, electrode arrangements can lead to substantial performance improvements. Within this context, numerical models can play a crucial role in the design process. Existing models can be broadly classified into full plasma models, that account for the chemical kinetics in the ionization region and reduced models, that rely on simplifying assumptions, e.g. Kaptzov's hypothesis, to avoid the computation of the ionization region. Full plasma models, despite their accuracy, are computationally demanding, especially when coupled with the Navier-Stokes equations. Reduced models are computationally cheaper and therefore are well suited for the design process. Compact design EAD thrusters that use multiple stages with alternating polarity have recently appeared in literature. Such a configuration allows smaller inter-stage distances, because the emitter(s) of a stage cannot be ignited by the collector(s) of the previous one, as no voltage difference is applied between them. In this work, we introduce a reduced bipolar corona discharge model coupled to the Navier-Stokes equations. A numerical simulation code is implemented based on the high performance finite element deal.II library. Our model can successfully simulate multi-stage thrusters with alternating polarity configurations and automatically detect the ignition pattern, when a configuration with multiple emitters is considered. Validation is performed against previous experimental and numerical campaigns, demonstrating the good predictive capabilities of our model.
ER -