Large‐eddy simulation of the tidal‐cycle variations of an estuarine boundary layer
Bibliographic record
Abstract
The estuarine boundary layer affected by a horizontal density gradient exhibits temporal evolution over a tidal cycle, in a manner similar to the diurnal cycle of the ocean surface mixed layer. A large eddy simulation (LES) model is developed to investigate the physics controlling the growth of the boundary layer during the flood tide and restratification during the ebb tide. Turbulent kinetic energy, momentum and salt fluxes, bottom stress, and energy dissipation rates calculated from the LES model all show a strong flood‐ebb asymmetry. Analysis of the turbulent kinetic energy (TKE) budget shows a primary balance between shear production and dissipation in the well‐mixed boundary layer over the tidal cycle. However, TKE transport term is found to be important across the edge of the boundary layer during the flood tide so turbulent energy generated in the bottom boundary layer can be transferred to the stratified pycnocline region. Tidal straining leads to a small and weakly convective region inside the boundary layer during the flood tide but the strain‐induced buoyancy flux does not make a significant contribution to the turbulence generation. Additional LES runs are conducted by switching off the baroclinic pressure gradient term in the momentum equation and the tidal straining term in the salinity equation to show that the baroclinic pressure gradient is the main mechanism responsible for generating the flood‐ebb mixing asymmetry.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".