Machine learning assisted mechanism modeling for gas phase electrohydrodynamic system

In this paper, a hybrid physics-data driven model for electrohydrodynamic gas system (EHDGS) was developed by combining artificial neural network (ANN) with mechanism modeling method. ANN was used to correlate the relationship between the variables (electrode distance, diameter of grounding cylinder, applied voltage, electric field gradient, etc.) in a needle-cylinder EHDGS and the initial space charge density. The results showed that the ANN model of nine neurons can well predict the initial space charge density. The coefficient of determination (R2) reaches 0.9874, and the mean absolute error is as low as 0.0067. Subsequently, a hybrid mechanism model where the initial space charge density was predicted from the ANN model was constructed to simulate the needle-cylinder EHDGS. The experiment with the needle-cylinder EHDGS was carried out. The simulation results were in good agreement with the experimental data, demonstrating the reliability of the proposed hybrid model. The electric field distribution, space charge distribution, and flow field distribution behavior of the EHDGS were then analyzed in detail. The effects of key parameters on the flow characteristics of EHDGS were systematically studied, showing that higher voltage and shorter distance give higher flow rate up to 2.5 m/s. The diameter of the cylinder also significantly influences the breakdown voltage. Three dimensionless groups were defined and their effects on spatial charge density distribution were investigated. This study provides both insights and an efficient tool for the design and optimization of EHDGS.