Data-driven prediction of flow fields in a needle-ring-net electrohydrodynamic pump system

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In this paper, a data-mechanism hybrid modeling method for efficiently obtaining an electrohydrodynamic flow field is proposed. First, a backpropagation (BP) model with high accuracy is trained to get the value of essential parameter q0 for the mechanism simulation of flow fields. Subsequently, the mechanism model is used to generate a database for flow field reconstruction. Three machine learning algorithms, namely, BP neural network, random forest regression (RFR), and convolutional neural network (CNN), are employed to predict and reconstruct the flow behaviors of a needle-ring-net electrohydrodynamic pump. The RFR model demonstrates higher accuracy and precision in predicting velocity and pressure in the flow field compared to the BP and CNN models. The use of machine learning models for flow field prediction can significantly reduce the computational time while maintaining the computational accuracy. Additionally, an analysis assessing the impact of varying dataset sizes on the prediction accuracy of the model is conducted. The results indicate that the size of the dataset significantly influences the model predictive performance. Specifically, larger datasets are suggested to enhance both the accuracy and the generalization capabilities of the model. This observation highlights the critical role of dataset size in optimizing the performance of machine learning models for predictive tasks in engineering applications. These results offer important references for improving the design and optimization of electrohydrodynamic pumps.