Validation of a CFD tool for studying the interaction of extreme waves with offshore gravity-based structures
Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) is a potentially flexible and cost-effective approach to study the interaction of waves with offshore structures. However, extensive validation is required to determine whether CFD modelling can be used to complement or even replace a physical modelling approach. Ocean, Coastal, and River Engineering (OCRE) portfolio of the National Research Council of Canada (NRC) previously conducted a series of physical hydraulic model tests to assist in designing an offshore Natural Gas processing platform to safely and optimally withstand extreme wave conditions forecasted for the deployment site. For three different wave headings (0°, 33° and 90°), the model was tested using a combination of long-crested regular and irregular waves as well as short-crest irregular waves for conditions associated with return periods up to 10,000 years. The model platform was tested in several different configurations, including the steel gravity sub-structure (SGS) alone, and also with various other components (such as superstructure consisting of solid or grated decks, and wave deflectors). A large quantity of high quality data on wave run-up, airgap, forces, moments, and pressures was obtained. The present paper validates the OpenFOAM® CFD toolbox for use in numerical modelling of this wave-rigid structure interaction problem. The interaction of long-crested regular waves with the structure is modelled. Global forces, overturning moments, pressures, and water levels are compared with results from the physical model. The present CFD model successfully predicts a large majority of experimental results with a high level of accuracy and proves to be a viable option for the prediction of the interaction of extreme waves with offshore gravity-based structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".