Evaluation of a numerical wave modelling tool for studying the overtopping of rubblemound breakwaters
Bibliographic record
Abstract
Wave overtopping of rubblemound breakwaters is a complex physical process which influences the functional efficiency and structural stability of the structure. The mean overtopping discharge resulting from design wave and water level conditions is often an important consideration affecting the selection of breakwater profile and crest height. The volume of water which passes over the crest of a breakwater depends on the structure geometry and composition, the nearshore bathymetry, the water level, and the seastate conditions. Physical modelling at large scale has traditionally provided a robust and reliable means to support the design of rubblemound breakwaters. Recent advancements in computational fluid dynamics and computing power have led to increasing efforts to use numerical modelling as a complementary tool to physical modelling for breakwater design applications. This paper compares measurements from a physical model study of rubblemound breakwaters conducted by the National Research Council of Canada (NRC) with numerical simulations produced by the numerical model IH2VOF. The skill of the IH2VOF model in predicting free-surface elevations and mean wave overtopping discharges is assessed. The comparisons are conducted for a range of seastate conditions, water levels, and breakwater geometries. The findings demonstrate that IH2VOF offers a viable tool to complement physical model testing for rubblemound breakwater design applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".