Computational Assessment of Wave Stability Against Submerged Permeable Breakwaters: A Hybrid Finite Element Method Approach
Bibliographic record
Abstract
Wave propagation, a phenomenon involving the transfer of energy over time, is a significant area of study, particularly with respect to sea waves.Due to their unique geometrical properties and inhomogeneous minimum amplitude, sea waves pose distinct challenges for numerical solutions.This research focuses on the analysis of wave stability against various water velocities and breakwater distances from the coastline.The study employs a hybrid approach, utilizing the Finite Element Method (FEM) to determine the movement of fluid elements through a porous, submerged breakwater.The concept of permeability in breakwaters is integral to this analysis.Permeable breakwaters permit a certain proportion of seawater or wave water to pass through, while absorbing or reflecting the remaining component of the waves.Understanding the permeability of breakwaters can enhance the design effectiveness and efficiency, whilst providing insight into potential impacts on coastal ecosystems.The results of the study demonstrate that the distance of the breakwater from the incoming wave influences both the amplitude and speed of the wave.Specifically, a greater distance between the wave and the breakwater results in a decrease in wave height, thereby increasing the stability of the simulation.For example, the directional and speed components of the movement at [x, y, t] for the first amplitude [20,2,15] was found to be 0.12515m, the second amplitude [15, 2, 15] 0.13161m, and the third amplitude [10, 2, 15] 0.13097m.This demonstrates that the breakwater's distance significantly influences wave stability, an important factor to consider in future breakwater design and implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".