Performance Evaluation of Leading Edge Tubercles Applied to the Blades in a 2-D Compressor Cascade

In the present paper, the performance of compressor blades modified with leading edge tubercles was evaluated and compared with that of a baseline profile at a high subsonic Mach number in a 2-D cascade. Specific tubercle geometries were selected based on an extensive literature survey and a Self-Organizing Map analysis. The compressor blade geometry of a popular aero-engine was reverse-engineered using laser-scanning. Baseline and tubercled compressor blades were 3-D printed and tested. Two sinusoidal tubercle shapes based on different amplitudes and wavelengths and one with a power law profile were selected. A 2-D compressor cascade was designed and commissioned to test these blades at high subsonic Mach number in the transonic wind tunnel at Royal Military College of Canada. Surface flow visualizations were performed with oil for observing and locating compressor blade stall for different sets of blades. Flow direction and the total pressure at the cascade exit were measured using a 5-hole, fast-response, traversing probe. Compressor blade performance was measured and compared with various tubercled blades at various angles of incidence, while maintaining periodicity at the inlet and exit planes. Total pressure loss coefficients were calculated for all 4 blades and compared for 6 positive angle of incidence. Power series tubercled profile resulted in slight improvements in the loss coefficient at 0° incidence and none of tubercled geometry compromized performance at the design point. The baseline blade stalled at 8° and tubercles were capable of delaying stall at this condition. Power series profile outperformed the baseline at all angle of incidence (AOI) with significant improvements at 8° AOI. Power series tubercled profiles performed better than other tubercled geometries at almost all AOI except 10° where sinusoidal tubercled profiles performed better. The presence of smaller valley and broader peaks is attributed with the performance improvement, supported by the flow visualization results.