Efficiency and Survivability of a Floating Oscillating Water Column Wave Energy Converter Moored to the Seabed: An Overview of the EsflOWC MaRINET2 Database
Bibliographic record
Abstract
Floating oscillating water column (OWC) type wave energy converters (WECs), compared to fixed OWC WECs that are installed near the coastline, can be more effective as they are subject to offshore waves before the occurrence of wave dissipation at a nearshore location. The performance of floating OWC WECs has been widely studied using both numerical and experimental methods. However, due to the complexity of fluid–structure interaction of floating OWC WECs, most of the available studies focus on 2D problems with WEC models of limited degrees-of-freedom (DOF) of motion, while 3D mooring effects and multiple-DOF OWC WECs have not been extensively investigated yet under 2D and 3D wave conditions. Therefore, in order to gain a deeper insight into these problems, the present study focuses on wave flume experiments to investigate the motion and mooring performance of a scaled floating OWC WEC model under 2D wave conditions. As a preparatory phase for the present MaRINET2 EsflOWC (efficiency and survivability of floating OWC) project completed at the end of 2017, experiments were also carried out in advance in the large wave flume of Ghent University. The following data were obtained during these experimental campaigns: multiple-DOF OWC WEC motions, mooring line tensions, free surface elevations throughout the wave flume, close to and inside the OWC WEC, change in the air pressure inside the OWC WEC chamber and velocity of the airflow through the vent on top of the model. The tested wave conditions mostly include nonlinear intermediate regular waves. The data obtained at the wave flume of Ghent University, together with the data from the EsflOWC tests at the wave flume of LABIMA, University of Florence, provide a database for numerical validation of research on floating OWC WECs and floating OWC WEC farms or arrays used by researchers worldwide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".