University of Cambridge > > G.K. Batchelor Laboratory lunchtime seminar > Unsupervised classification of experimental shadowgraphs of sheared stratified turbulence

Unsupervised classification of experimental shadowgraphs of sheared stratified turbulence

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I will present a method for unsupervised dimensionality reduction and clustering of a large experimental dataset of over 100 shadowgraph movies of shear-driven, salt-stratified turbulence in the Stratified Inclined Duct (SID) . The method automatically detects edges representative of discrete density interfaces, extracts a low-dimensional vector of statistics representative of their morphology, and applies a density-based clustering algorithm. Five clusters are detected and interpreted physically, revealing several classes of turbulence and mixing. These classes of turbulence on instantaneous frames are more quantitative, objective and subtle than previous human classifications of entire movies into flow regimes (wavy, intermittently turbulent, fully turbulent). The periods of time spent in each class vary gradually with the experimental input parameters (the Reynolds number and the tilt of the duct). We explain the potential of this approach to help reveal the state space dynamics of large turbulent datasets.

This talk is part of the G.K. Batchelor Laboratory lunchtime seminar series.

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