The Wave Focusing Effect of a Parabolic Wall
Bibliographic record
Abstract
The energy generation efficiency of a wave energy conversion system is in general proportional to the capacity of wave energy capture of the system. In various wave energy conversion systems, a configuration with parabolic shape has shown advantages in wave capture dynamics. This paper presents an experimental investigation into the wave focusing and elevation in a parabolic wall area with a Laser Wave Height Measurement equipment named as IVP Ranger SC386. In the experiment, the tested waves were described by a dimensionless factor WF which consists of wave parameters and the parabolic wall size. The WF increases with wave relative size to the model. The tested wave obliquities to the parabolic wall were 10 and 20 degrees in addition to the normal incident waves. A tube with an inner diameter 7.5cm, representing a chamber for oscillatory water columns compressing air, was mounted at the focus area. The elevations of wave height inside the tube with a sealed and an open top, as two different cases, were also measured. Furthermore the wave forces acting on the parabolic wall were measured using load cells. The analysis of the experimental results revealed that the parabolic wall was able to significantly elevate wave heights by up to 2.5 times. Within 10 degrees the wave obliquity effect can be neglected for both forces acting on the parabolic wall and wave height elevated by the parabolic wall. A prediction equation for focusing wave height was developed from the experimental results and the parabolic focusing principle.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".