Sensitivity analysis of plunger-type wavemakers with water current
Bibliographic record
Abstract
The inclusion of current in a water channel is a critical requirement for recreating accurately scaled ocean environments in a laboratory. In this paper, the effect of a uniform current on the theoretical model of a plunger-type wavemaker has been investigated through a variance-based global sensitivity analysis. The output of the wavemaker model is represented by the ratio of wave amplitude to stroke amplitude. Therefore, the sensitivity analysis evaluates the influence of all uncertain input parameters on the output variance of the model. In addition to the water current, the uncertain input parameters for the wavemaker model were established as the wave frequency, wedge angle, mean wedge depth, water height, and node points on the wavemaker boundary. To account for a range of limitations for both the plunger and the water channel in which it oscillates, the sensitivity analysis was performed for a broad distribution of each parameter. The analysis determined that the wave frequency had the highest influence on the output variance of the wavemaker model. For a uniform water current, the first order and total effect sensitivity indices were estimated as and, respectively. Although the sensitivity of the model to the current was relatively low compared to the wave frequency, there exists an impact due to the interaction of the current with the other parameters. Therefore, it was established that the inclusion of the current in the plunger-type wavemaker model is essential for application of the model to an experimental setup.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".