Efficient numerical simulation of atmospheric pulsed discharges by introducing deep learning - Zhang, Yuan-Tao; Gao, Shu-Han; Ai, Fei (Frontiers in Physics, 2023)
Abstract Plasma simulation is an important but sometimes time-consuming approach to study the discharge behaviors of atmospheric pulsed discharges. In this work, an efficient simulation method is proposed by introducing deep learning to investigate the discharge characteristics driven by very short pulsed voltages. A loss function is designed and optimized to minimize the discrepancy between the Deep Neural Network (DNN) and the verified fluid model. The prediction data obtained via well-trained DNN can accurately and efficiently reveal the key discharge characteristics, such as the waveforms of discharge current and gap voltage, spatial profiles of charged particles density and electric field. The spatial distributions of charged particles density and electric field obtained form DNN are also given to unveil the underlying mechanisms. Additionally, the predictions from deep learning and the formula analysis both highlight that the breakdown voltage and current density can be effectively reduced by increasing repetition frequency, which quantitatively agrees well with the experimental observations. This study provides a great potential promise for vastly improving the simulation efficiency by introducing deep learning in the field of atmospheric plasmas computation.