Application of Deep Learning to Predict Cavitation Flow in Centrifugal Pump

The hydrodynamic performance and cavitation development in centrifugal pump have a decisive impact on its energy conversion and performance. However, there are still bottlenecks when using current experimental methods and simulation algorithms in the real-time measurement and visual display of flow fields, and the high experimental and computational cost cannot be ignored. Here, we integrate computational fluid dynamics and experimental platform with our customized analysis framework based on a multi-attribute point cloud dataset and advanced deep learning network. This combination is made possible by our workflow to generate simulated data of flow characteristics of cavitation in the pump as the training and test dataset, complete the deep learning algorithm process and check the consistency with the experimental results. Deep learning framework models the multiphase flow system of centrifugal pump and completes the mapping from the structure of pump and working conditions to the flow to realize the high-performance prediction of bubble, pressure and velocity fields. Compared to the prevalent methods, the proposed deep learning framework shows superior performance in accuracy, computational cost, visual display and has the potential of generality to model the interaction between different fluids and impellers.