Multi-objective design optimization and physics-based sensitivity analysis of field emission electric propulsion for CubeSat platforms

Abstract

Field-emission electric propulsion is an electrostatic space electric propulsion technology that offers various advantageous features including efficient design, high specific impulse, and versatile thrust capabilities ranging from micro-Newton to milli-Newton levels. These characteristics make this type of propulsion a promising technology for small satellite platforms, enabling precise attitude control, orbit maintenance, and de-orbiting through ionization and acceleration of a liquid metal propellant. The growing demand for small propulsion systems in CubeSat platforms has spurred significant progress in modeling and characterizing field emission electric propulsion thrusters to enhance their overall performance. However, little study has been conducted to investigate the effect of geometric configurations on electric fields or expelled ion trajectories for design optimization. In this study, multi-objective design optimization is performed by incorporating electrostatic simulation coupled with an analytical performance model into evolutionary algorithms based on prediction from surrogate modeling, aiming to optimize the thruster emission design to maximize thruster performance. Physical insights into the key design factors influencing the performance of field emission electric propulsion have been gained by probing into the interaction between ion particles and electric field behavior within the thruster. It has been found that the length of the emitter tip has a significant effect on plume divergence, i.e., a longer emitter tip under the influence of electric field at higher emitter current tends to result in lower initial acceleration of emitted ions and subsequently wider spread or divergence of the ion beam. A shorter emitter tip, on the other hand, generates a sharper E-field gradient, resulting in a more focused and narrower ion beam. Additionally, sensitivity analysis has identified the mass flow rate and potential distributions as the most influential design factors on performance due to the active roles they play in the performance generation process.