Wave Impact Experiment of a GBS Model in Large Waves
Bibliographic record
Abstract
For the design of offshore structures in harsh wave environments, model testing continues to be the recommended industry practice for determining wave impact forces on offshore structures. Accurate measurements of wave impacts in model tests have been a challenge for several decades. Transducers are required to accurately capture the short duration, high magnitude, and dynamic nature of impact loads. The structural model, transducers, and the transducer mountings need to be designed such that mechanical vibrations in the integrated transducer-mounting-structural model system do not contaminate the wave impact measurements. In this work, the dynamic oscillations in the measurements were controlled through the design and fabrication of transducers, their mounting and the GBS model. Wave crest probability distributions were developed that included fully nonlinear effects. These distributions were used as a benchmark to qualify the waves in the wave calibration tests. The highly stochastic nature of impact loads makes it challenging to obtain converged probability distributions of the maximum impact loads (i.e. forces or pressures) from model tests. To increase the confidence in the statistical values of wave impact loads, a large number of realizations were used for a given sea state. Variability of the maximum pressure due to wave basin effects (such as wait-time between tests) was examined with fifteen repeat tests using the same wave maker control signal. These tests provided insights into the random behavior of the impact loads.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".