Sensitivity Analysis and Experimental Validation of Plunger-type Wavemakers Modelled with a Steady Flow
Bibliographic record
Abstract
The inclusion of a steady flow in a water channel is a critical requirement for recreating accurately scaled ocean environments in a laboratory.In this thesis, the effect of a uniform flow on the theoretical model and experimental performance of a plungertype wavemaker has been investigated.Through a variance-based global sensitivity analysis, the influence of all input parameters on the output variance of the theoretical model was investigated.The analysis determined that the wave frequency had the highest influence on the wavemaker model.For a uniform flow, the first order and total effect sensitivity indices were estimated as 0.74±0.30%and 6.84±0.16%,respectively.Although the sensitivity of the model to the flow was relatively low compared to the wave frequency, there exists an impact due to the interaction of the flow parameter with the remaining model parameters.Therefore, it was established that the inclusion of the flow in the plunger-type wavemaker model is essential for application of the model to an experimental system.To investigate the performance of an experimental plunger-type wavemaker, an ultrasonic sensor was used to measure the generated wave profile for a variety of testing conditions based on the results of the sensitivity analysis.It was observed that as the difference between the experimental results and the theoretical model increased, both frequency and flow increased.Therefore, a corrected model was suggested and the operational range of the experimental system was established.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".