The ion beam shepherd is an innovative contactless technique for space debris removal, in which an impulse transfer thruster pushes the debris object through the action of a plasma plume and an impulse compensation thruster maintains formation ying. The optimal operational point of both thrusters strongly depends on their characteristics and on the physics of the plasma plume expansion into vacuum. With the use of dedicated thruster performance models, complemented with simplied plume expansion and plasma-debris interaction models, a system-level optimization study of the impulse transfer thruster alone and of the overall electric propulsion subsystem is presented for an ion beam shepherd mission example. An optimum design point is found for minimum overall power consumption in both cases.
Abstract.Recently proposed space missions such as Darwin, eLISA and NGGM have encouraged the development of electric propulsion thrusters capable of operating in the micro-Newton (μN) thrust range. To meet these requirements, radio frequency (RF) gridded-ion thrusters need to be scaled down to a few centimeters in size. Due to the small size of these thrusters, it is important to accurately determine the thermal and performance parameters. To achieve this, a multi-physics performance model has been developed, composed of plasma discharge, 2D axisymmetric ion extraction, 3D electromagnetic and RF circuit models. The plasma discharge model itself is represented using 0D global, 2D axisymmetric and 3D molecular neutral gas, and Boltzmann electron transport sub-models. A 3D thermal model is introduced to determine the temperature distribution for various throttle points, using as inputs the plasma and electromagnetic field heating values obtained from the performance model. This also allows the validation of the performance model itself. Additionally, we analyze the effect the thruster's temperatures play on the plasma properties/performance and vice versa. The model is based on the RIT 3.5 thruster developed for the NGGM mission geometry and predicts the RIT 3.5 experimental data within approximately 10%.
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