Experimental Measurement of Wave Field Variations around Wave Energy Converter Arrays
Bibliographic record
Abstract
Wave energy converters (WECs) inherently extract energy from incident waves. For wave energy to become a significant power provider in the future, large farms of WECs will be required. This scale of energy extraction will increase the potential for changes in the local wave field and coastal environment. Assessment of these effects is necessary to inform decisions on the layout of wave farms for optimum power output and minimum environmental impact, as well as on potential site selection. An experimental campaign to map, at high resolution, the wave field variation around arrays of 5 oscillating water column WECs and a methodology for extracting scattered and radiated waves is presented. The results highlight the importance of accounting for the full extent of the WEC behavior when assessing impacts on the wave field. The effect of radiated waves on the wave field is not immediately apparent when considering changes to the entire wave spectrum, nor when observing changes in wave climate due to scattered and radiated waves superimposed together. The results show that radiated waves may account for up to 50% of the effects on wave climate in the near field in particular operating 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".