Predictive analysis of flow control in high-speed complex flow field based on machine learning

The flow control actuator is the core of the active flow control technology. The design level and performance of actuator directly determine the application direction and effect of active flow control. In order to obtain the action law of the flow control actuator, a large number of experiments are needed to study the influence of excitation parameters on control effect parameters, and the experimental cost is large. In this paper, the experimental data of jet shock control in reverse plasma synthesis are used, and the Gaussian process regression model in machine learning is used to obtain the mapping law from the actuator parameters （head cone diameter, cavity volume, discharge capacitance and outlet diameter） to the control effect parameters （maximum out of body distance）. We compare the prediction effects of Gaussian process regression under various kernel functions, and analyze the influence of actuator parameters on control effect parameters by using the characteristic importance analysis method. The results show that for this small sample problem, Gaussian process regression with the quadratic polynomial kernel function Poly2 obtains the highest accuracy; in characteristic importance analysis, the head cone diameter has the greatest influence on the maximum separation distance, followed by discharge capacitance and cavity volume. The influence of these two parameters is similar, and the influence of the outlet diameter is the least. The work of this paper can provide some guidance for the setting of various parameters of the actuator in the flow control experiment of the high-speed complex flow field.