Numerical study on discharge characteristics of atmospheric dielectric barrier discharges by integrating machine learning

Abstract

In recent years, with the development of gas discharge technology at atmospheric pressure, the application of low temperature plasma has drawn widespread concern in pollution prevention, disinfection, sterilization, energy conversion and other fields. Atmospheric dielectric barrier discharge is widely used to produce low-temperature plasmas in various applications, which is usually numerically investigated by fluid models. The unique advantages of machine learning in various branches of physics have been discovered with the advancement of big data processing technology. Recent studies have shown that artificial neural networks with multiple hidden layers have a pivotal role in the simulation of complex datasets. In this paper, a fully connected multilayer BP network together with a universal hidden layer structure is developed to explore the characteristics of one or more current pulses per half voltage cycle of atmospheric dielectric barrier discharge. The calculated data are used as training sets, and the discharge characteristics such as current density, electron density, ion density, and electric field of atmospheric dielectric barrier discharge can be quickly predicted by means of artificial neural network program. The computational results show that, for a given training set, the constructed machine learning program can describe the properties of atmospheric dielectric barrier discharge with almost the same accuracy as the fluid model. Also, the computational efficiency of the machine learning is much higher than that of the fluid model. In addition, the use of machine learning programs can also greatly extend the calculation range of parameters. Limited discharge parameter range is considered a major challenge for numerical calculation. By substituting a relatively limited set of training data obtained from the fluid model into the machine learning, the discharge characteristics can be accurately predicted within a given range of discharge parameters, leading to the generation of an almost infinite set of data, which is of great significance for studying the influence of discharge parameters on discharge evolution. The examples in this paper show that the combination of machine learning and fluid models can greatly improve the computational efficiency, which can enhance the understanding of discharge plasmas.