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Novel Parameters for the Performance Evaluations of Leading Edge Tubercles on Airfoils

AIAA 2020-2691
Session: Low Speed, Low Reynolds Number Aerodynamics II
Published Online:

Researchers have tested tubercles with different amplitude and wavelength combina- tions on a range of low-speed airfoils. However, a systematic approach has never been used for the optimization of tubercles. In this study, tubercles are optimized using an articial neural network known as Self-Organizing Maps (SOM). Data were extracted using reverse engineering from published tubercle research and used for training the SOM. A new vari- able, a Reynolds number based on hydraulic diameter ReDh, is introduced for the tubercle classification directly relating performance. In addition, post-stall operability another new parameter was introduced for tubercle performance assessment. Based on the SOM re- sults, new tubercle geometries were selected for 2 new proof of concept tests to perform further investigation. Stall angle improved due to the reduction of amplitude, wavelength and ReDh, validating the predictions of SOM. However, the one tubercle geometry resulted in lower lift curve slope in the pre-stall region and a reduced CLmax in comparison to the baseline, possibly a result of drastic reduction in tubercle wavelength. In the post-stall regions, the new tubercle geometry showed improvements over the baseline unmodified airfoil.