BULK DISSIPATION AND FLOW CHARACTERISTICS IN CUBE ARMORED BREAKWATERS
Bibliographic record
Abstract
The main function of a breakwater is dissipating wave energy. The breakwater dissipates energy by means of three mechanisms: (1) wave breaking over the slope; (2) wave propagation through the secondary layers and porous core; (3) interaction with the main armor layer. A revised dimensional analysis shows that relative water depth, h/L, and wave steepness, H/L, are key factors of breakwater performance. The product of (h/L) (HI/L) (hereinafter named as , alternate slope similarity parameter) can be applied to quantify the reflected and transmitted energy coefficients and the dissipation rate (Daz-Carrasco et al., 2020) and to identify the type of wave breaking and the domains of wave energy transformation (Moragues et al., 2020). The aim of this work is to analyze the dissipation term and its relation with the alternate slope similarity parameter , as well as correlate the flow characteristics (run-up, rundown) with the type of wave breaking and the bulk dissipation. For that purpose, former data (Clavero et al. 2020) and data from new tests have been analyzed. Whereas it is not clear that the use of different experimental techniques will give the same results in the laboratory, three different techniques for sea states selection have been taken into account in the new tests: (1) keeping constant h/L; (2) keeping constant H/L or Ir; and (3) varying h/L and H/L.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/oMZ05U0igCs
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".