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Record W2317225100 · doi:10.1115/omae2012-84269

Time-Domain VIV Prediction of Marine Risers

2012· article· en· W2317225100 on OpenAlexaff
Peter Ma, Wei Qiu, Don Spencer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDrilling riserLift (data mining)Added massMarine engineeringMooringFinite element methodReynolds numberDragForcing (mathematics)Time domainVibrationSubmarine pipelineScale (ratio)Structural engineeringEngineeringGeologyMechanicsComputer scienceMechanical engineeringAerospace engineeringPhysicsGeotechnical engineeringAcoustics

Abstract

fetched live from OpenAlex

Vortex Induced Vibration (VIV) of marine risers poses a significant challenge as the offshore oil and gas industry moves into deep water. A time-domain analysis tool has been developed to predict the VIV of marine risers based on a forcing algorithm and by making full use of the available high Reynolds number experimental data. In the formulation, the hydrodynamic damping is not treated as a special case but simply an extension of the experimentally derived lift curves. The forcing algorithm was integrated into a mooring analysis program based on the global-coordinate based finite element method. At each time step, the added mass, lifting force and drag force coefficients and their corresponding loads are computed for each element. Validation studies have been carried out for a full-scale rigid riser segment and a model-scale flexible riser. The numerical results were compared with experimental data and solutions by other programs.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.176
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 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

Citations7
Published2012
Admission routes1
Has abstractyes

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