Observing a dynamical skeleton of turbulence in Taylor-Couette flow experiments.

Recent work shows that recurrent solutions of the equations governing fluid flow play an important role in structuring the dynamics of turbulence. Here, an improved version of an earlier method (Krygier et al. 2021 J. Fluid. Mech. 923, A7 and Crowley et al. 2022 Proc. Natl Acad. Sci. USA 119, e2120665119) is used for detecting and analyzing intervals of time when turbulence 'shadows' (spatially and temporally mimics) recurrent solutions in both numerical simulations and laboratory experiments. We find that all the recurrent solutions shadowed in numerics are also shadowed in experiment, and the corresponding statistics of shadowing agree. Our results set the stage for experimentally grounded dynamical descriptions of turbulence in a variety of wall-bounded shear flows, enabling applications to forecasting and control. This article is part of the theme issue 'Taylor-Couette and related flows on the centennial of Taylor's seminal Philosophical Transactions paper (part 1)'.

Correlations between the leading Lyapunov vector and pattern defects for chaotic Rayleigh-Bénard convection.

We probe the effectiveness of using topological defects to characterize the leading Lyapunov vector for a high-dimensional chaotic convective flow field. This is accomplished using large-scale parallel numerical simulations of Rayleigh-Bénard convection for experimentally accessible conditions. We quantify the statistical correlations between the spatiotemporal dynamics of the leading Lyapunov vector and different measures of the flow field pattern's topology and dynamics. We use a range of pattern diagnostics to describe the flow field structures which includes many of the traditional diagnostics used to describe convection as well as some diagnostics tailored to capture the dynamics of the patterns. We use the ideas of precision and recall to build a statistical description of each pattern diagnostic's ability to describe the spatial variation of the leading Lyapunov vector. The precision of a diagnostic indicates the probability that it will locate a region where the Lyapunov vector is larger than a threshold value. The recall of a diagnostic indicates its ability to locate all of the possible spatial regions where the Lyapunov vector is above threshold. By varying the threshold used for the Lyapunov vector magnitude, we generate precision-recall curves which we use to quantify the complex relationship between the pattern diagnostics and the spatiotemporally varying magnitude of the leading Lyapunov vector. We find that pattern diagnostics which include information regarding the flow history outperform pattern diagnostics that do not. In particular, an emerging target defect has the highest precision of all of the pattern diagnostics we have explored.