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Record W2594825061 · doi:10.3744/snak.2016.53.6.514

A Study on the Riser Fatigue Analysis Using a Quarter-modal Spectrum

2016· article· en· W2594825061 on OpenAlexaboutno aff
Sang‐Woo Kim, Seung Jae Lee, Sol Mi Choi

Bibliographic record

VenueJournal of the Society of Naval Architects of Korea · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsModalVibrationFrequency domainModal analysisStructural engineeringTime domainQuarter (Canadian coin)AcousticsEngineeringMaterials scienceMathematicsPhysicsComputer scienceComposite materialMathematical analysis

Abstract

fetched live from OpenAlex

Oil and gas production riser systems need to be designed considering a wide band quarter-modal analysis which contains low-, wave-, VIV(Vortex induced vibration) frequencies. The VIV can be separated into cross-flow(CF) and in-line(IL) components. In this study, the various idealized tri- and quarter-modal spectra are suggested to analyze fatigue damage on the production riser system. In order to evaluate the fatigue damage increment caused by the IL's motion, tri- and quarter-modal spectral fatigue damages are calculated in time domain. And the fatigue damage calculated from two different modal spectra are compared quantitatively. Then the suitability of existent wide band fatigue damage models for quarter modal spectrum was evaluated by comparison of frequency domain calculation and time domain calculation. The result show that although spectral density of IL motion is not remarkable in quantity, the effect on the fatigue damage is significant and existent fatigue damage models are not adequately estimating damage by quarter-modal spectra.

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)0.001
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 score0.115
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.252
Teacher spread0.229 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

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