Turbulent characteristics and anisotropy in breaking surge waves: A numerical study
Bibliographic record
Abstract
Numerical simulations of breaking weak surge waves produced by the sudden removal of a gate were conducted to investigate turbulent characteristics generated by different mechanisms in the surge front. We conducted numerical studies using Large Eddy Simulation over a range of surge Froude numbers from 1.7 to 2.5, and a wide spectrum of tempo-spatial scales down to the Hinze scale was resolved. We established turbulent statistics by means of Favre-averaging where quantities were weighted by the instantaneous density. Our results demonstrated that the production of turbulent kinetic energy is mainly sourced at the toe, where the shear layer originates. Furthermore, the decomposition of production elements illustrated that the shearing action is the principal driver in the entire surge front. Herein, we also conducted intricate anisotropy analyses, including establishing characteristic shape maps by pointwise eigendecomposition of Reynolds stress tensors. Near the toe at the core of the mixing layer, prolate structures were evident that are mainly stretched in the streamwise direction. Moving from the mixing layer toward the free surface, however, the structure changes to a combination of prolate and oblate features, where the smallest principal stress is nearly in the spanwise direction. In a snapshot, our results illustrate a clear transition in anisotropy from the recirculating region to the mixing layer.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".