Vertical wave-in-deck loading and pressure distribution on fixed horizontal decks of offshore platforms
Bibliographic record
Abstract
The risk assessment of wave-in-deck loading on fixed platform decks requires accurate prediction of both global and local loading. In this paper, the vertical loading generated on the bottom plate of a rigidly mounted box-shaped structure due to unidirectional regular waves is computed by means of two approaches. The first is a component-based approach based on Kaplan's method and the second is a computational fluid dynamics (CFD) approach based on the volume of fluid (VOF) method implemented in the commercial CFD code FLUENT. Different parameters including wave steepness and air gap are tested. The obtained results are validated against tank experiments. The study revealed that when the wave-in-deck events are measured globally and locally the load magnitude, its duration as well as its distribution is better evaluated and the uncertainty involved with these impulsive loads can be reduced. It was found that in many cases Kaplan's method underestimates the magnitude of the force in the upward direction. CFD force predictions were found to be in better agreement with the measured forces. Copyright 2014 by the International Society of Offshore and Polar Engineers (ISOPE).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".