Tsunami-Induced Bores Propagating over a Canal, Part II: Numerical Experiments Using the Standard k-ε Turbulence Model
Bibliographic record
Abstract
This companion paper presents the results of a series of numerical experiments examining the effects of a mitigation canal on the hydrodynamics of a tsunami-like turbulent bore proceeding over a horizontal bed. The hydraulic bores were generated by a dam-break setup which employed impoundment depths of do = 0.20 m, 0.30 m, and 0.40 m. The bore propagated downstream of the impoundments in the flume and interacted with a canal with varying geometry located downstream. The bore then left the flume through a drain located further downstream of the canal. In this study, the effect of the canal depth on the specific momentum and specific energy of hydraulic bores passing over a rectangular canal is numerically studied. The canal width was kept constant, at w = 0.6 m, while the canal depths were varied as follows: d = 0.05 m, 0.10 m, and 0.15 m. The time history of mean flow energy during the bore’s passing over the mitigation canal indicates that the jet stream of the maximum mean flow energy is controlled by the canal depth. The time required to dissipate the jet stream of the maximum vorticity, the turbulent kinetic energy, and the energy dissipation rate all increased as the canal depth decreased. The effect of canal orientation on the bore hydrodynamics was also numerically investigated, and it was found that the impulsive momentum and specific energy reached the highest values for canal orientations of 45 and 60 degrees. For the same canal depth, the highest peak specific momentum occurred with the highest degree of canal orientation (θ = 60°).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".