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Record W2056507881 · doi:10.1115/omae2008-57065

Benchmarking of VIV Suppression Systems

2008· article· en· W2056507881 on OpenAlexaff
Kenneth J. Schaudt, Christopher John Wajnikonis, Don Spencer, Jie Xu, Steve Leverette, Rodney Masters

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsReynolds numberDragWakeParticle image velocimetryWind tunnelSplitterFinVortex-induced vibrationMechanicsPhysicsVortexEngineeringStructural engineeringAcousticsMarine engineeringAerospace engineeringTurbulenceOptics

Abstract

fetched live from OpenAlex

A new form of Vortex-Induced Vibration (VIV) suppression device, the AIMS Dual-fin Flow Splitter (ADFS), has been developed, tested and benchmarked against bare-pipe, 5d and 15d pitch strakes and conventional teardrop fairings. Testing included high-mode number in-situ tests as well as low Reynolds number (<300,000) and high Reynolds number (<1.9 million) forced and free tank tests. Finally, wind tunnel tests and in-water Particle Image Velocimetry (PIV) were used to test the hypothesis that the dual-fin flow splitter replaces the oscillating wake of a blunt body with a stable, attached circulation behind the body and between the fins. Such a replacement was hypothesized to result in reduced drag, and the elimination of almost all VIV. The paper will describe the testing program and results, and present the incorporation of the test results into riser models.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.180
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations11
Published2008
Admission routes1
Has abstractyes

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