Identifying the turbulent-boundary-layer interface in a transitional flow using a self-organizing map

An unsupervised machine-learning algorithm, the self-organizing map (SOM), is used to identify the turbulent boundary layer (TBL) and non-TBL regions in bypass transition. The data employed for the analysis are from an archived direct simulation publicly available in the Johns Hopkins Turbulence Databases (JHTDB, http://turbulence.pha.jhu.edu), stored using the new FileDB system. The data points in the entire flow domain are automatically classified into TBL and non-TBL regions by the SOM, based on their standardized velocity, velocity fluctuations, velocity gradients and their spatial locations. Thus the SOM identifies the turbulent-boundary-layer interface (TBLI) without the usual need for choosing thresholds on e.g. vorticity or velocity fluctuations. The TBLI is found to be a hyperplane in the input space. The SOM distinguishes the streaks in the laminar region and the weak free-stream turbulence from TBL region. Results from our approach are shown to be consistent with threshold-based methods in the special cases when those are applicable.