A Combined CFD and Machine Learning Technique for Efficient Prediction of Flow Behavior in Venturi Nozzle

Cavitation is the physical phenomenon when liquid evaporates into gas when the pressure drops below the saturation pressure. This is observed in a wide variety of engineering processes and it can be used for flow control purposes. In this research, cavitation of a flow stream in the venturi nozzle is simulated using a CFD software and the results are analyzed. Cavitation may occur in the downstream of the throat region. Simulations were conducted to identify the important parameters that affect the flow behavior.



Machine learning is an efficient method that reduces the computational cost in many engineering problems. It has been shown that machine learning models can be used to speed up or even replace a part of CFD simulations. The primary goal is to maximize the steam quantity at the nozzle outlet for a given flow conditions. The main difficulty is that a large number of simulations is needed due to the highly non-linear nature of the problem. To reduce the computational cost, several artificial neural network models will be used to predict the cavitation efficiency based on the boundary conditions. The results are then verified against the simulated flow field. The ANN model is then combined with a genetic algorithm to find the optimized geometry.