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Record W4390938567 · doi:10.1139/cgj-2023-0406

A novel three-dimensional SCR motion simulator for modelling the catenary riser–seabed interaction in a centrifuge

2024· article· en· W4390938567 on OpenAlexvenueno aff
Charles Wang Wai Ng, Yi Le, Sina Baghbanrezvan, Paul Van Laak

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
FundersResearch Grants Council, University Grants Committee
KeywordsCentrifugeCatenarySeabedWave loadingGeotechnical engineeringSurgeMarine engineeringBearing (navigation)EngineeringStructural engineeringGeologyComputer scienceSubmarine pipeline

Abstract

fetched live from OpenAlex

Steel catenary risers (SCRs) provide a cost-effective solution for deepwater oil and gas production. However, SCRs are susceptible to potential fatigue failure due to the cyclic motions of floating platforms. Previous studies on the physical modelling of cyclic SCR–seabed interactions have primarily focused on either the continuous cyclic motion of an SCR or a single rest period between two SCR motion packets. However, our understanding of the development of seabed trenches and excess pore pressure and their effects on SCR fatigue during multiple episodes of SCR motion and soil reconsolidation remains limited. This study presents a newly developed model container capable of modelling three-dimensional SCR motions including heave, surge, sway, and vortex-induced vibration in a geotechnical centrifuge. A centrifuge test is conducted to investigate the vertical cyclic SCR–seabed interaction, considering five vertical cyclic motion packets with intervening periods of reconsolidation. The results indicate that ignoring the effects of reconsolidation leads to an overestimation of the fatigue life of an SCR. In this test, the SCR fatigue life is reduced by 18%–23% after five episodic SCR motion packets and intervening reconsolidation.

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: none
Teacher disagreement score0.826
Threshold uncertainty score0.488

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 integrity0.0000.001
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 head0.020
GPT teacher head0.220
Teacher spread0.200 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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