Data-driven classification of sheared stratified turbulence from experimental shadowgraphs

We present a novel dimensionality reduction and unsupervised clustering framework for the classification and reduced-order modeling of density-stratified turbulence in laboratory experiments. Our method is applied to shadowgraph data collected in the `Stratified Inclined Duct' (SID) experiment, where a rich set of turbulent states arise in a sheared buoyancy-driven counterflow at Prandtl number Pr≈700, as a function of the Reynolds number (Re) and duct tilt angle (θ). By analyzing statistics of the morphology of density interfaces embedded within the turbulent flow, we identify a `skeleton' of distinct turbulent states underpinning the complex physics of the SID experiment. The ratio of time spent in each turbulent state varies gradually across the (Re, θ) parameter space, and at least two distinct routes to stratified turbulence are revealed.