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Record W2062161772 · doi:10.1115/omae2004-51477

FEM Model for Pipeline Analysis of Ice Scour: A Critical Review

2004· review· en· W2062161772 on OpenAlexafffund
Ibrahim Konuk, Abdelfdettah Fredj

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsGeological Survey of Canada
FundersNatural Resources Canada
KeywordsFinite element methodPipeline (software)StiffnessShell (structure)Pipeline transportGeotechnical engineeringStructural engineeringEngineeringGeologyMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents results from two different Finite Element (FE) pipeline ice-scour models employing pipe and shell elements that incorporate large deformations and metal plasticity. The main objective of this paper is to investigate the effects and implications of some of the main pipeline design parameters on the response of the pipeline determined by using Winkler models and soil displacements that are based on an empirical scour function commonly used in recent literature. The current model is two dimensional in terms of deformed pipe geometry and incorporates temperature and pressure stiffness effects. A detailed study of the soil displacements underneath and around the scour and a three-dimensional continuum based ice-soil-pipe interaction model is being presented in a different paper. The paper discusses the limitations and implications of the Winkler modeling and compares results obtained using different Winkler spring models. It illustrates the effects of pipe temperature (and pressure), pipe burial depth, and scour width. A comparison of pipe response using shell and pipe elements is also presented. This paper presents results from the FE models for a typical gathering pipeline. The pipe is taken to be a 16 inch diameter and 0.75 inch wall thickness API 5L X65 Specification line pipe.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.047
GPT teacher head0.333
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2004
Admission routes2
Has abstractyes

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