Modeling Optimal Locations of Breakwaters to Mitigate Wind-Induced Waves in Bahar Al-Najaf Depression Using MIKE21
Bibliographic record
Abstract
The waves produced by the wind are the most noticeable and frequently the most significant waves in the range of waves at sea.Given the limited amount of knowledge on the mathematical modelling of the wind induced wave in lake or shallow water depth, considered to this point.In this paper, a briefly look at how these waves are formed by the wind in the Bahar Al-Najaf depression, as well as some of the essential properties that arise.A numerical model based on unstructured mesh is proposed to predicate and visualize wave conditions.MIKE21 spectral model base on mass balance equation was used as a numerical tool to find the optimal location of mitigation structures to dissipation the wave energy near the neighbor structures.The amount of variation between the simulated wave heights and those calculated using an empirical equation is less than 9%.Three schemes for breakwater has been tested to find the optimal location and orientation of breakwater.The results showed that the three schemes used for the breakwater gave varying results, both depending on its location and characteristics.The first scheme gave a covering area approaching 1.19 km 2 , and the second scheme gave approximately 0.889 km, and third scheme gave a covering area 1.11 km 2 .This study showed that the wave heights at the upstream and downstream were measured after the breakwater had been constructed, and they were found to be between 0.35 and 0.7 m.This implies a decrease of around 40% to 60% in comparison to the original heights of 1.25 to 0.9 m.All of these schemes are good to a certain extent in dispersing the energy by breaking the single pattern of the wave at rates that may reach 50% of the height of the arriving wave, the first scenario was the best and most efficient in terms of energy dispersion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".