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We propose a dimensionality reduction and unsupervised clustering method forthe automatic classification and reduced-order modeling of density-stratifiedturbulence in laboratory experiments. We apply this method to 113 longshadowgraph movies collected in a `Stratified Inclined Duct' (SID) experiment,where turbulence is generated by instabilities arising from a shearedbuoyancy-driven counterflow at Reynolds numbers $Re \approx 300-5000$, tiltangles $\theta=1^\circ-6^\circ$ and Prandtl number $Pr \approx$ 700. The methodautomatically detects edges representative of discrete density interfaces,extracts a low-dimensional vector of statistics representative of theirmorphology, projects these statistics onto a two-dimensional phase space ofprincipal coordinates, and applies the OPTICS clustering algorithm. Fiveclusters are detected and interpreted physically based on their typicalinterface morphology and an examination of representative frames, revealingdistinct types of turbulence and mixing: laminarizing, braided, overturning,granular and unstructured, as well as some intermediate types. The ratio oftime spent in each cluster varies gradually across the $(Re,\theta)$ space. Atintermediate values of $Re\,\theta$, intermittent turbulence cycles betweenclusters in phase space and reveals at least two distinct routes to stratifiedturbulence. These insights demonstrate the potential of this method to revealthe underlying physics of complex turbulent systems from large experimentaldatasets.

Data-driven classification of sheared stratified turbulence from experimental shadowgraphs