Efficient numerical simulation on dielectric barrier discharges at atmospheric pressure integrated by deep neural network

Abstract

Numerical simulation is an essential way to investigate the discharge behaviors of atmospheric low-temperature plasmas (LTPs). In this study, a deep neural network (DNN) with multiple hidden layers is constructed to surrogate the fluid model to investigate the discharge characteristics of atmospheric helium dielectric barrier discharges (DBDs) with very high computational efficiency, working as an example to show the ability and validity of DNN to explore LTPs. The DNN is trained by the well-formed training datasets obtained from a verified fluid model, and a designed loss function coupled in the DNN program is continuously optimized to achieve a better prediction performance. The predicted data show that the essential discharge characteristics of atmospheric DBDs such as the discharge current waveforms, spatial profiles of charged particles, and electric field can be yielded by the well-trained DNN program with great accuracy only in several seconds, and the predicted evolutionary discharge trends are consistent with the previous simulations and experimental observations. Additionally, the constructed DNN shows good generalization performance for multiple input attributes, which indicates a great potential promise for vastly extending the range of discharge parameters. This study provides a useful paradigm for future explorations of machine learning-based methods in the field of atmospheric LTP simulation without high-cost calculation.