Non-Intrusive Detection of Rotating-Stall Occurring in the Turbine Quadrant of Francis-Type Pump-Turbines

The so-called rotating-stall (RS) is an undesired hydrodynamic phenomenon affecting the regular function of Francis-type pump-turbines. Stronger noise and vibration increase the risk of catastrophic failures and economic loses. In the past, such phenomenon was considered negligible and the search for a definitive solution was not a priority. Today, since pump-turbines have been recognized as important agents in the global ecological stage, the interest in RS is renewed. Scientist search for answers, mainly in two fields: computational models and laboratory testing; but neither has been successfully transferred to prototypes. In the first case, prediction methods have not reached the accuracy required to be considered in practical implementations; and in the second case, experimental approaches remain limited to reduced scale models and laboratory testing conditions. To compensate for the lack of reliable prediction methods, to simplify the conventional experimental arrangements, and of course, to extend the application range from scale models to prototypes, three non-intrusive RS-detection approaches have been developed. In fact, they allow detection of RS by examining shaft dynamics, structure and airborne noise with a reduced number of instruments, and specific signal processing. For validation purposes, a reduced scale pump-turbine model featuring RS is instrumented with accelerometers, proximity probes, microphones and pressure sensors; additionally, high-speed visualization and specific image processing techniques are used as well. The model is tested in turbine rotation. It is initially set to a high efficiency and RS-free operating point. Then, rotating speed is increased up to runaway and turbine-brake when RS develops. Pressure sensors and tuft visualization are employed to confirm the occurrence of the phenomenon whilst accelerometers, microphones and proximity probes are used to non-intrusively detect RS phenomena.

García, Manuel
Farhat, Mohamed
EAFIT University

 Record created 2011-12-13, last modified 2018-03-17

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