Full-Scale Fairing Qualification Tests
Bibliographic record
Abstract
Production risers as well as drilling risers are often exposed to ocean currents. Vortex-induced vibrations (VIVs) have been observed in the field and can cause fatigue failure and excessive drag on the riser. In order to suppress VIV, fairings are often used. This paper presents qualification tests for two types of fairings: the short-crab claw (SCC) fairings and the AIMS dual flow splitter (ADFS) fairings. The short-crab claw fairing design is a novel design patented by the Norwegian deepwater project (NDP). As will be detailed in this paper, both the SCC and ADFS designs offer very low drag, completely suppress VIV, and are effective even when they are in tandem. A model test campaign was undertaken in the 200-m towing tank facility at the ocean, coastal, and river engineering in St. John's, NF, Canada. A rigid pipe with a diameter of 0.3556 m (14 in) was utilized for the experiments. This corresponds to prototype size for a production riser and a 1:3.8 scaled model for a 1.3716 m (54 in) drilling riser. Given that these tests were conducted at prototype scale, they were used to qualify the fairings for field deployment. Both fairings (SCC and ADFS) were very effective in suppressing VIV and reducing drag. The ADFS fairings are most effective for a span to diameter ratio of 1.75. For all fairing geometries, it was found that a small taper increases the fairing effectiveness considerably.
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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 | 0.001 | 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) | 0.008 | 0.002 |
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".