Experimental investigation, modeling and prediction of transition from uniform discharge to filamentary discharge in DBD plasma actuators using artificial neural network

Abstract

The process of plasma discharge in dielectric barrier discharge (DBD) plasma actuators can occur under two different regimes, namely uniform discharge regime and filamentary discharge regime. When the discharge becomes filamentary, the induced flow velocity and consequently, the performance of the actuator starts to decrease. Therefore, it is crucial to prevent the transition to filamentary discharge. In this paper, a model is developed to predict the formation of filamentary regime. For this purpose, the full factorial design of experiments is applied to investigate the effects of geometrical variables and electrical variables on induced flow velocity and power consumption. Then, artificial neural network (ANN) is employed to develop two models for velocity and power consumption. The models are validated both experimentally and statistically. The models show that every variable has a different effect on the start of the filamentary discharge. Finally, the Sequential quadratic programming (SQP) optimization algorithm have been applied to obtain critical value for each variable, in which the plasma discharge begins to become filamentary for any given set of other variables. The results show that the predicted data are in good agreement with the experimental values. Thus, the ANN model can effectively identify the start of filamentary regime.