Hall effect thruster design via deep neural network for additive manufacturing

TL;DR

This paper introduces a deep neural network-based method for designing Hall effect thrusters, enabling faster and more flexible performance predictions compared to traditional scaling laws, thus advancing electric propulsion system development.

Contribution

It presents a novel deep learning approach for Hall thruster design, reducing computational effort and increasing flexibility over conventional scaling law methods.

Findings

Deep neural networks can accurately predict thruster performance.

The method significantly reduces design time and computational resources.

It offers a flexible alternative to traditional scaling laws.

Abstract

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.