Mixing in a strongly stratified turbulent wake quantified by bulk and conditional statistics
Bibliographic record
Abstract
We analyze a dataset from a numerically simulated, temporally evolving turbulent wake (Zhou, Phys. Rev. Fluids , vol. 7, 2022, 104802) that exhibits spontaneous anisotropic layering under strong stratification, alongside significant spatiotemporal variability within the flow. This analysis focuses on the irreversible flux coefficient, $\varGamma$ , defined as the ratio between turbulent potential and kinetic energy dissipation rates. We find that the volume-averaged $\varGamma$ initially rises, reaches a plateau between 0.45 and 0.49 when the layering dynamics become dominant, and then decreases as viscosity plays a larger role. These peak $\varGamma$ values are consistent with those from prior simulations under strongly stratified conditions. Such efficient mixing occurs when the Ozmidov to Thorpe length scale ratio is between 0.37 and 0.52, consistent with numerical and field data reported by Mashayek et al. ( J. Fluid Mech. , vol. 826, 2017, pp. 522–552). To account for the coexistence of dynamically distinct regions within the flow, we perform conditional sampling of $\varGamma$ against a locally defined gradient Richardson number, ${\textit {Ri}}$ . This reveals a flux-gradient relation between $\varGamma$ and ${\textit {Ri}}$ that remains largely consistent over time. This relation features a large, approximately constant value of $\varGamma$ for ${\textit {Ri}}$ values greater than one, echoing the ‘constant-power’ scenario postulated by Balmforth et al. ( J. Fluid Mech. , vol. 355, 1998, pp. 329–358).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".