Application of Data Analytics to Identify Over-Rotor Temperature and Pressure Wavelets Corresponding to Tip Clearance Variations

The gap that exists between the rotating blades and stationary casing in an unshrouded turbine blade row has a significant effect on the stage work extraction and efficiency. In the pursuit of understanding the mechanisms by which the tip gap leakage flow influences turbine efficiency and blade tip heat transfer, many researchers have implemented over-rotor fast-response pressure and temperature sensors to examine the tip leakage flow characteristics. The present study investigates this tip leakage flow using time-resolved pressure and temperature probes in conjunction with capacitive tip clearance sensors. These sensors were applied in a one-stage continuous-duration turbine operating at engine-relevant operating conditions. A magnetic bearing was used to move the centerline of the turbine rotor assembly to vary the local blade tip clearance. The method of varying blade tip clearance is advantageous because it allows for the study of over-rotor pressure and temperature response without requiring operating condition or coolant flow rate changes. Discrete Wavelet Transformation (DFT) and machine learning techniques were utilized to identify pressure and temperature wavelets correlated to blade tip clearance. This analysis reveals the capability of applying an over-rotor pressure or temperature sensor to quantify blade tip clearance. Additionally, these wavelets may provide information about how the tip leakage flow field responds to tip clearance variations.