Evaluation of an approximate method for incorporating floating docks in harbor wave prediction models
Bibliographic record
Abstract
Computer models based on the two-dimensional (2-D) elliptic mild-slope equation are nowadays routinely used in harbor engineering applications. However, structures like floating breakwaters and docks, which are often encountered in the modeling domain, render the problem for locally three-dimensional model and hence are problematic to incorporate in a 2-D model. Tsay and Liu (Applied Ocean Research. 1983. Vol 5(1): 30–37) proposed a highly simplified but approximate approach that does not violate the overall two dimensionality of the problem. The validity of their approach is examined in detail, and it is found that although their approximation provides results with the correct trend, the actual solutions deviate considerably from the theoretical solutions. We have developed correction factors that may be used to produce more reliable results using the framework of Tsay and Liu. Application of the resulting method to a harbor in Alaska shows that docks in the harbor distort the wave field considerably and create a reflective pattern that has the potential to affect navigation safety in some areas. A by-product of this paper consists of plots of transmission coefficients for waves propagating past rectangular and cylindrical floating objects of infinite extent for a wide range of conditions encountered in practice. Such transmission coefficients are at present readily available in the published literature for selected cases only.Key words: wave, model, mild slope, equation, floating breakwater, dock, marina, harbor.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".