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Record W3137021395 · doi:10.48336/4yfg-gn81

Modelling of trench effect on fatigue performance of steel catenary riser

2022· dissertation· en· W3137021395 on OpenAlexaff
Rahim Shoghi

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2022
Typedissertation
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCatenaryTrenchStructural engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The riser-seabed interaction resulting in a trench formed in the touchdown zone (TDZ) of steel catenary risers (SCR) has a significant influence on accumulated fatigue damage. Several studies have used different trench modeling approaches to investigate the trench effect on fatigue performance of SCR. However, contradictory observations have been reported with no coherent agreement on the beneficial or detrimental effect of the trench on fatigue. In this study, the significance of trench geometry in fatigue damage evaluation was investigated. Using boundary-layer methods (BLM) and numerical approaches, a meaningful relationship was observed between the trench geometry in different zones and the peak fatigue damage. A new set of rules was proposed for the qualitative assessment of the overall trend of the trench effect on the variation of fatigue damage. The proposed assessment rules were validated by performing comprehensive numerical fatigue analysis. A comparison with samples of published experimental and numerical studies was also conducted. The developed geometrical model and the set of rules for qualitative assessment of the trench effect on fatigue were used to re-assess the majority of the key published studies. The proposed methodology resulted in a more coherent agreement between the published studies. It was observed that for the near, far, or out of the plane direction of the vessel excursions, the ultimate fatigue damage might be slightly increased or decreased depending on the probability of occurrence in different geographical locations. Instead, the trench effect appeared in the form of significant shifting of the peak damage point towards the opposite direction of the low-frequency vessel excursions. This implied that the case dependency of the trench effect on fatigue response in different geographical locations with various environmental loads was a potential source for the contradictory results reported in previously published studies. Moreover, the study revealed the significance of riser flexural rigidity and its relation with TDP oscillation on the trench surface and the fatigue damage accumulation, consequently. The peak fatigue damage depending on the trench profile was analytically obtained and showed a good agreement with numerical models. The effect of seabed soil stiffness on the fatigue performance of SCR was compared with the contribution of the trench profile in the touchdown zone. The conducted research revealed several significant trench effect on the fatigue performance of SCR and provided an in-depth insight into this challenging problem.

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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.035
GPT teacher head0.250
Teacher spread0.215 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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