Hall effect thruster design via deep neural network for additive manufacturing
This work addresses the need for efficient and flexible design tools for Hall effect thrusters in the aerospace industry, though it appears incremental as it builds on existing machine learning approaches.
The paper tackled the design of Hall effect thrusters for space propulsion by developing a deep neural network model to predict performance and generate designs, reducing computational power compared to traditional methods and offering more flexibility than scaling laws.
Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics model of such propulsion system is not yet optimized for fast computations and design iterations, most thrusters are being designed using so-called scaling laws. But this work focuses on rather novel approach, which is outlined less frequently than ordinary scaling design approach in literature. Using deep machine learning it is possible to create predictive performance model, which can be used to effortlessly get design of required hall thruster with required characteristics using way less computational power than design from scratch and way more flexible than usual scaling approach.