Numerical study on effects of voltage amplitude in CO2 pulsed discharges under Martian conditions by deep neural network

Abstract

In recent years, non-thermal plasma (NTP) has received an increasing attention for in situ resource utilization of CO2 in the Martian atmosphere. As an important approach to exploring the underpinning physics of NTP, fluid models with tens of species and hundreds of reactions are very time-consuming in simulating CO2 plasmas under Martian conditions, especially driven by the nanosecond pulsed voltage. In this paper, a deep neural network (DNN) with multiple hidden layers is proposed as an example to replace the fluid model to accurately describe the essential discharge features of CO2 pulsed discharge under Martian conditions. After trained by the data from the experimental measurements or numerical simulation and continuously optimized to minimize the loss function, the constructed DNN can achieve a satisfied prediction performance. Compared to the fluid model, the DNN takes only a few seconds to predict the discharge characteristics and profiles of the electric field and particle density, especially to show the spatial–temporal distribution of the given products in CO2 plasmas, such as CO2+, CO3−, CO2v1. This study indicates that a DNN can efficiently yield the essential characteristics in CO2 pulsed discharge even with plenty of species involved in seconds, strongly showing the potential ability to be a highly efficient numerical tool in NTPs with multiple temporal–spatial scales.