AN ARTIFICIAL NEURAL NETWORK APPROACH TO INVESTIGATE CAVITATING FLOW REGIME AT DIFFERENT TEMPERATURES

The aim of the present study is to implement an useful approach to predict and on-line monitoring the cavitating flow and to investigate the influence of the different parameters on the phenomenon by the application of Artificial Neural Network (ANN). A three-layer Elman neural network was designed, using as inputs the power spectral density distributions of dynamic differential pressure fluctuations, recorded downstream and upstream the restricted area of the orifice. The results show that the designed neural networks predict the cavitation patterns successfully comparing with the cavitation pattern by visual observation. The Artificial Neural Network underlines also the impact that each input has in the training process, so it is possible to identify the frequency ranges that more influence cavitation regimes and the impact of fluid temperature.