Modeling Cavitation in Converging–Diverging Nozzle Using Computational Fluid Dynamics and Machine Learning Model

Abstract Cavitation occurs when the pressure drops below the saturation pressure. In this study, computational fluid dynamics (CFD) is used to model the cavitation behavior in the Venturi tube under high pressure and to investigate the impact of geometric parameters on steam generation. In recent years, there has been a shift toward exploring machine learning as an alternative to traditional CFD. This work aims to establish an artificial neural network (ANN) using numerical analysis results to predict flow characteristics for various geometrical shapes of nozzles. This including the prediction of pressure drop and steam generation. The final results demonstrate a high accuracy in prediction.