Detecting the high/low-speed large-scale structures by a machine learning perspective in turbulent boundary

Abstract

The current developments in machine learning have facilitated the application of data-driven strategies for predicting the evolution of vortex structures in turbulent boundary layers. This study combines the attached eddy hypothesis with the extreme gradient boosting model to forecast large-scale high/low-speed motion based on a series of input signals. The performance of the trained model, as quantified by the percentage of accurately predicted high/low-speed regions, exhibits variability concerning the deviation between the input velocity fluctuation and the predicted output value at z/δ=0.016, where ‘z’ represents the wall-normal height and ‘δ’ indicates the boundary layer thickness. Our findings underscore the significant potential of machine learning in predicting high/low-speed regions within large-scale motion.