Direct Numerical Simulation of Differential Scalar Diffusion in Three-Dimensional Stratified Turbulence
Bibliographic record
Abstract
Abstract The potential for differential turbulent transport of oceanic temperature (T) and salinity (𝒮) is explored using three-dimensional direct numerical simulations of decaying stratified turbulence. The simulations employ a realistic molecular diffusion coefficient for T, and one for a “salt” scalar S that is 10 times smaller. Initially, a uniformly stratified medium is disturbed by a turbulent burst whose initial energy is assigned a range of values. In each instance, transports of T integrated over the subsequent decay of the burst exceed those of S. The more energetic cases occupy parameter ranges similar to, and exhibit spectral characteristics that are essentially indistinguishable from, those of direct observations of turbulence in the stratified ocean interior. In these cases, the turbulent diffusivity of T exceeds that of S by 6%–22%. These simulations underestimate the degree of differential diffusion between T and salinity 𝒮 (which has a molecular diffusivity 100 times less than T); thus at the Reynolds numbers attained by the simulations these results constitute lower bounds for differential diffusion associated with sporadic turbulence in the ocean. The simulation results are consistent with previous laboratory and two-dimensional numerical experiments and suggest that the assumption of equal turbulent diffusivities for T and 𝒮, commonly used in circulation modeling and in interpreting oceanic mixing measurements, should be reconsidered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".