Supergranule aggregation, i.e., the gradual aggregation of convection cells to horizontally extended networks of flow structures, is a unique feature of constant heat flux-driven turbulent convection. In the present study, we address the question if this mechanism of self-organisation of the flow is present for any fluid. Therefore, we analyse three-dimensional Rayleigh-Bénard convection at a fixed Rayleigh number Ra ≈ 2.0 × 10 5 across 4 orders of Prandtl numbers Pr ∈ 10 −2 , 10 2 by means of direct numerical simulations in horizontally extended periodic domains with aspect ratio Γ = 60. Our study confirms the omnipresence of the mechanism of supergranule aggregation for the entire range of investigated fluids. Moreover, we analyse the effect of Pr on the global heat and momentum transport, and clarify the role of a potential stable stratification in the bulk of the fluid layer. The ubiquity of the investigated mechanism of flow self-organisation underlines its relevance for pattern formation in geophysical and astrophysical convection flows, the latter of which are often driven by prescribed heat fluxes.

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Barriers to the turbulent transport of heat in a convection flow can be identified by Lagrangian trajectories that stay together for a long time and thus probe spatial regions in the bulk of the fluid flow that do not mix effectively with its surroundings. They form Lagrangian coherent sets which we investigate here in direct numerical simulations of three-dimensional Rayleigh-Bénard convection at three different Prandtl numbers. The analysis is based on 524, 288 massless Lagrangian tracer particles which are advected in the time-dependent flow. Clusters of trajectories are identified by the diffusion map approach, which quantifies the connectivity of trajectory segments by a diffusion process on the data, and a subsequent sparse eigenbasis approximation (SEBA) for cluster detection. The diffusion kernel contains a cutoff that is based on the time-averaged distance between mutual Lagrangian tracers in a time window. The numerical simulations are performed in a cell at an aspect ratio Γ = 16, at fixed Rayleigh number Ra = 10 5 , and Prandtl numbers Pr = 0.1, 0.7 and 7. The combination of diffusion map and SEBA leads to a significantly improved cluster identification that is compared with the large-scale patterns in the Eulerian frame of reference. We show that the Lagrangian coherent sets contribute significantly less to the global turbulent heat transfer for all chosen Prandtl numbers as the trajectories in the spatial complement. This is realized by monitoring local Nusselt numbers, a dimensionless measure of heat transfer, along the tracer trajectories.

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We explore the transport mechanisms of heat in two- and three-dimensional turbulent convection flows by means of the long-term evolution of Lagrangian coherent sets. They are obtained from the spectral clustering of trajectories of massless fluid tracers that are advected in the flow. Coherent sets result from trajectories that stay closely together under the dynamics of the turbulent flow. For longer times, they are always destroyed by the intrinsic turbulent dispersion of material transport. Here, this constraint is overcome by the application of evolutionary clustering algorithms that add a time memory to the coherent set detection and allows individual trajectories to leak in or out of evolving clusters. Evolutionary clustering thus also opens the possibility to monitor the splits and mergers of coherent sets. These rare dynamic events leave clear footprints in the evolving eigenvalue spectrum of the Laplacian matrix of the trajectory network in both convection flows. The Lagrangian trajectories reveal the individual pathways of convective heat transfer across the fluid layer. We identify the long-term coherent sets as those fluid flow regions that contribute least to heat transfer. Thus, our evolutionary framework defines a complementary perspective on the slow dynamics of turbulent superstructure patterns in convection flows that were recently discussed in the Eulerian frame of reference. The presented framework might be well suited for studies in natural flows, which are typically based on sparse information from drifters and probes.