Analysis of relaminarizing turbulent boundary layer under a favorable pressure gradient with machine learning

Abstract

Relaminarization process of an accelerated turbulent boundary layer was analyzed using neural network. The neural network model was constructed and trained from the streamwise fluctuating velocity data with the aid of a hot-wire anemometer. Keras, a deep learning library in python, was used to build the model. Two types of data were used for the training data: turbulent and laminar. From the created model, the predicted probability of turbulent or laminar flow was obtained for the streamwise and wall-normal fluctuating velocities in the whole region within the relaminarization process. From the predicted probabilities, the relaminarization process was examined. The predicted probability of turbulence for both the streamwise and wall-normal fluctuating velocities decreased downstream. During the relaminarization process, range of the predicted probability of turbulence differed between streamwise and wall-normal components. The difference in directional components between the training and the test data cause a difference in range and ambiguity of the predicted probability. The predicted probability was able to extract the characteristics of the turbulence within the minimum eddy. The distribution of the predicted probability of turbulence for the streamwise fluctuating velocity differed from that of Normalized compression distance of streamwise fluctuating velocity whose reference position was within turbulent region.