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Dynamic Analysis of a Deep Water Marine Riser using Bond Graphs

2016· article· en· W2585473327 on OpenAlexaff
Geoff Rideout, Stephen Butt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDrilling riserStructural engineeringTension (geology)Beam (structure)VibrationBendingPlanarBond graphEngineeringCompression (physics)MechanicsMaterials scienceComputer sciencePhysicsMechanical engineeringAcousticsDrillingMathematicsComposite material

Abstract

fetched live from OpenAlex

This paper describes a lumped segment model of a deep water riser using the bond graph method. The model allows calculation of the dynamic response, and resulting normal stress, of the riser pipe due to bending, tension and compression. Marine risers are subjected to diverse dynamic loads such as the force exerted by the waves and the vessel's motion. The cyclic nature of these loads will induce fluctuating stresses that, after a certain time, will result in failure by fatigue. Therefore, the dynamic analysis of the riser's response is very important for the prediction of the fatigue life. In this paper, the riser is modeled as a beam with both lateral and axial degrees of freedom. The beam is divided into lumped segments that are modeled as planar rigid bodies joined together by springs representing the pipe's compliance in shear, tension, and bending. An analysis of the common external loads is made. A 16-inch diameter marine riser for deep water conditions is modeled to verify the usefulness of the lumped model technique, and then simulated using the software 20-Sim©. Results show that the external axial loads significantly affect not only the axial response but also the lateral vibrations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.997

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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.

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

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