AI-based approach to predict nozzle instability in Pelton turbine model test.

Abstract

Over past experience on reduced scale physical models of Pelton turbines, it has been noticed that a few models exhibited disturbed performance curves over a given domain of speed factor and discharge factor under specific test head values, while the same performance curves were smooth at other test heads. Cross-checking of different model component combinations helped identify that those disturbances occur with specific nozzles independently from other model components. The experienced disturbances have been understood to be consequences of instabilities attributed to the nozzle.Attempts to understand root causes and predict such instabilities with physics-based approaches first proved unsuccessful. The proposed research presents the analysis performed on an available model test results database to predict the occurrence of instability thanks to neural network classifiers, the so-called AI-based approach.A model test campaign including a nozzle known to be subject to instabilities but not part of the database used to train the classifier has been designed and conducted. The agreement between the operating domain with predicted disturbance and the operating domain with measured disturbance is finally discussed.