Analyzing Wave Dragon Under Different Wave Heights Using Flow-3D: A Computational Fluid Dynamics Approach
Bibliographic record
Abstract
Wave energy is an increasingly attractive renewable energy source due to its potential and predictability. Various Wave Energy Converters (WECs) have been developed, including attenuators, overtopping devices, and point absorbers. The Wave Dragon, an overtopping device, is a floating structure anchored to the seabed with a mooring system. It uses two reflectors to guide incoming waves into a central reservoir, where the captured water flows through turbines to generate electricity. This study enhances the realism of Wave Dragon simulations by modeling it as a moving structure with moorings, addressing key gaps in prior research. Real-time wave data from the Caspian Sea, collected over a year, were used to develop a 3D model and analyze the device’s performance under varying wave conditions. Four significant wave heights (Hs) of 1.5, 2.5, 3.5, and 4.5 m were tested. The results demonstrate that higher wave heights increase water flow through the turbines, leading to higher energy output, with monthly energy generation recorded as 16.03, 25.95, 31.45, and 56.5 MWh for the respective wave heights. The analysis also revealed that higher wave heights significantly increase pressure forces on the Wave Dragon, from 2.97 × 105 N at 1.5 m to 1.95 × 106 N at 4.5 m, representing a 6.5-fold increase. These findings underscore the potential of Wave Dragons to enhance renewable energy production while ensuring structural robustness in varying wave conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".