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Record W1973799008 · doi:10.4043/24196-ms

A Nonlinear Dynamic Substructuring Approach for Efficient Detailed Global Analysis of Flexible Risers

2013· article· en· W1973799008 on OpenAlexaff
Michel Dib, Philip Cooper, Shankar Bhat, Arya Majed

Bibliographic record

VenueOffshore Technology Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsNonlinear systemSubstructureStructural engineeringBendingComponent (thermodynamics)ArmourResponse analysisComputer scienceEngineeringHysteresisMaterials science

Abstract

fetched live from OpenAlex

Abstract This paper presents a significant methodology advancement that addresses one of the industry's most challenging problems: the accurate prediction of detailed local stresses in unbonded flexible risers. Flexible risers exhibit highly nonlinear dynamic behavior due to the stick/slip interaction between the pipe wall layers in compliant systems that undergo large three-dimensional translations/rotations. Practical, accurate prediction of critical flexible pipe component responses requires an efficient method capable of incorporating detailed flexible pipe models into a global nonlinear dynamic analysis. Current industry practice is a two-step global/local approach involving a global nonlinear analysis with 1D centerline models, which may include bending hysteresis effects, followed by local analysis of a detailed model segment to the global results to predict critical response items such as armour wire stresses. A Nonlinear Dynamic Substructuring (NDS) framework is developed that expands the classical methods of dynamic substructuring and component-mode synthesis to geometrically and locally nonlinear problems. This evolution/integration of capabilities enables the computationally efficient inclusion of detailed flexible pipe models into, and recovery of detailed response/stress time-histories directly from, the global nonlinear analysis itself. The NDS methodology is benchmarked against published work involving the large deformations static and dynamic global analysis of a flexible riser. The full potential of the method is then demonstrated by efficiently incorporating 3D detailed flexible pipe substructure models, with bending hysteresis, into a global system nonlinear analysis and recovering stress time-histories in tensile armour layers. Introduction The accurate prediction of local stresses in flexible risers has been an industry objective for some time. Flexible pipe has a complex structure composed of multiple interacting pipe wall layers (Figure 1). The stick/slip friction hysteretic behavior of these layers is directly coupled to the global large displacements of the overall compliant system resulting in a highly nonlinear problem. A common approach for analyzing flexible risers is to conduct the global analysis with standard line elements utilizing the flexible pipe's unpressurized linear bending stiffness.

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)0.000
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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