2023/05/06 by Adrien Lefauve, Lefauve, Adrien, Miles M. P. Couchman +1 · 1 citation
Earth and Planetary Sciences · Engineering · #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Meteorological Phenomena and Simulations #Tropical and Extratropical Cyclones Research
paper · pdf · doi:10.48550/arxiv.2305.04048
openalex publication_date 2023/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a dimensionality reduction and unsupervised clustering method for the automatic classification and reduced-order modeling of density-stratified turbulence in laboratory experiments. We apply this method to 113 long shadowgraph movies collected in a `Stratified Inclined Duct' (SID) experiment, where turbulence is generated by instabilities arising from a sheared buoyancy-driven counterflow at Reynolds numbers Re ≈ 300-5000, tilt angles θ=1^∘-6^∘ and Prandtl number Pr ≈ 700. The method automatically detects edges representative of discrete density interfaces, extracts a low-dimensional vector of statistics representative of their morphology, projects these statistics onto a two-dimensional phase space of principal coordinates, and applies the OPTICS clustering algorithm. Five clusters are detected and interpreted physically based on their typical interface morphology and an examination of representative frames, revealing distinct types of turbulence and mixing: laminarizing, braided, overturning, granular and unstructured, as well as some intermediate types. The ratio of time spent in each cluster varies gradually across the (Re,θ) space. At intermediate values of Re θ, intermittent turbulence cycles between clusters in phase space and reveals at least two distinct routes to stratified turbulence. These insights demonstrate the potential of this method to reveal the underlying physics of complex turbulent systems from large experimental datasets.