A loading model for an OWC caisson based upon large-scale measurements
Bibliographic record
Abstract
Wave energy is one of the most promising marine energy resources in terms of the scale of the resource, but there remains little technology convergence and costs remain at near-prohibitive levels. Of many wave energy converter (WEC) concepts that have been developed over the years, the oscillating water column (OWC) stands out for its simplicity and low maintenance cost. Quite some experience of actual OWC operation has been gained to date from small, stand-alone pilot schemes. One way to reduce costs is the integration of an OWC-WEC into a breakwater, enabling some degree of cost-sharing between energy and harbour or coastal defence functions. A major problem encountered during the design of an OWC-WEC scheme remains the uncertainty in the wave loads, with their critical influence upon capital cost. A model to estimate forces acting on an OWC chamber in a caisson breakwater is proposed in this paper. Horizontal forces on the front (curtain) wall and the rear (in-chamber) wall are predicted. In addition, and unlike a conventional caisson breakwater, vertical forces acting on the caisson chamber ceiling will have considerable effect on sliding and overturning characteristics of the breakwater structure. The proposed model enables the prediction of chamber pressures which in turn influence the chamber vertical force. The new model has been compared with results from large scale physical model measurements from tests carried out in the very large wave channel, GWK, in Hannover (Germany). Forces under both regular and irregular wave conditions were measured. The comparisons show that the model fits well with the test results to the factor of 1 ± 0.2 for the regular wave cases and to the factor of 0.8 ± 0.2 for irregular wave cases. This model will enable the structural design of caisson breakwater-integrated OWCs to be approached with uncertainties reduced to those comparable with conventional caisson design.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".