Multiscale Coupling of Spacecraft Charging Model With Electric Propulsion Plume Simulation
When designing spacecraft with electric propulsion (EP) devices, it is important to assess spacecraft integration to ensure that the important components are not subject to significant sputtering by high-energy ions. In addition to the EP device and its plume, surface charging of spacecraft has to b...
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Veröffentlicht in: | IEEE transactions on plasma science 2019-11, Vol.47 (11), p.4898-4908 |
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Sprache: | eng |
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Zusammenfassung: | When designing spacecraft with electric propulsion (EP) devices, it is important to assess spacecraft integration to ensure that the important components are not subject to significant sputtering by high-energy ions. In addition to the EP device and its plume, surface charging of spacecraft has to be modeled properly, as surface potential can directly affect the sputtering rate. Three surface charging models are incorporated into the spacecraft module of the numerical simulation framework-Thermophysics Universal Research Framework (TURF), and these include: 1) dielectric; 2) conductive; and 3) charge propagation models. The charge propagation model has been upgraded to solve the surface charge distribution implicitly, allowing a wide range of electrical conductivity values without causing the simulation to become unstable. Each of the charging models is verified against a simple problem where an analytical solution can be determined. Then, the coupling of the surface charging model and a hybrid particle/fluid model is tested in a more complex problem, where the floating potential on a sphere immersed in plasma is to be obtained. Finally, the surface charging model in an EP plume simulation is demonstrated. These problems are multiscale in that the charging model has to resolve an electron timescale (i.e., plasma oscillation) while the particle time step has to be orders of magnitude larger than the electron timescale in order to maintain a long enough sampling window for the ion current to effectively reduce the statistical noise. Therefore, two separate time steps are introduced for a stable convergence of the coupled models. |
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ISSN: | 0093-3813 1939-9375 |
DOI: | 10.1109/TPS.2019.2945534 |