Comparative Study on Probability Distributions of Riser Responses
Bibliographic record
Abstract
The responses of a flexible riser caused by motions of a weather-vaning FPSO subjected to two cyclonic storm events are investigated numerically. The objective was to comparatively study probability distributions of the riser responses to determine their statistical properties and the distribution types appropriate for particular responses. This will contribute to a method for the prediction of the extreme responses of the flexible riser during cyclonic events. Simulations of the riser responses in two cyclonic storm events were conducted including effects of wind, waves and current. Several types of probability distributions were fitted to the generated datasets using maximum likelihood estimates. Distributions tested included Rayleigh, Normal, Lognormal, Weibull, Generalized Extreme Value, Gamma, Burr, Birnbaum-Saunders and Beta. In addition, Hermite moment models were fitted in an attempt to use a single probabilistic model for all responses. A comparison was conducted using the goodness of fit tests to determine the distribution that best represented the generated data. Hermite moment models based on four moments produced good fits across both storm cases and for the majority of responses. The findings of this study provide foundations for development of more bespoke distributions for riser responses. The implication of using appropriate parent distributions for short and long term response analysis and the association between parent and extreme value distributions in both time and frequency domain contexts are discussed.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".