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Record W2599896581 · doi:10.1115/1.4036373

Full-Scale Fairing Qualification Tests

2017· article· en· W2599896581 on OpenAlexaffabout
Yiannis Constantinides, Stergios Liapis, Don Spencer, Mohammed Islam, Kjetil Skaugset, Apurva Batra, Rolf Baarholm

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsOceans Limited (Canada)
Fundersnot available
KeywordsMarine engineeringEngineeringFuzeDragTowingStructural engineeringWinchFull scaleWakePayload (computing)Aerospace engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.221
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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