Hierarchical network of thermal plumes and their dynamics in turbulent Rayleigh–Bénard convection
Bibliographic record
Abstract
The link between characteristic coherent structures and their statistical properties in turbulent flows remains largely unclear and is thus a central bottleneck for a better understanding of turbulent flows. Here, we demonstrate this link for the important problem of thermal convection. We show how the hierarchical plume network in the near-wall region of the flow, which becomes increasingly sparse with increasing distance away from the wall, is connected to the marginal stability of the thermal boundary layer and the resulting global heat transport. Our results, which are based on a series of direct numerical simulations for Rayleigh numbers up to [Formula: see text] in a relatively shallow layer, suggest a highly fluctuating thermal boundary layer that is composed of local building blocks in terms of plumes, which are the essential drivers of turbulent heat transport. These thermal plumes are found in a dynamically perpetual process of formation and aggregation that can be described, particularly well for Rayleigh numbers [Formula: see text], by a von Smoluchowski equation resulting in a gamma distribution of the local plume spacing, consistent with measurements. Similarity manifests with respect to the horizontal extension of the network, the vertical hierarchical plume clustering away from the wall and the number of plumes, over an order of magnitude of the thermal boundary layer thickness. Our findings suggest the dominance of dynamical local processes near the wall, rather than a global boundary layer instability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".