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Record W4321084178 · doi:10.1115/1.4056931

Dynamic Stress Analysis of Multisegment Curved Pipes Subjected to the Passage of an In-Line Inspection Tool

2023· article· en· W4321084178 on OpenAlexafffund
Hamid Mostaghimi, Sina Rezvani, Ronald Chune, Ron Hugo, Simon S. Park

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsAlberta EnergyUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPipeline transportStructural engineeringFinite element methodStress (linguistics)Pipeline (software)Line (geometry)VibrationSpan (engineering)Boundary value problemEngineeringGeotechnical engineeringMechanical engineeringAcousticsMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract Pipelines are susceptible to degradation over time due to different types of defects caused by environmental and loading conditions. In-line inspection (ILI) is a preventive examination method widely used to monitor the degradation of pipelines. The passage of an ILI tool through a segment of a pipeline with loose boundary condition can generate significant dynamic stress within the pipe. When pipelines pass through excavated sites, bridges, water, and bog, or have free-span segments, they are at a greater risk of dynamic stress. This research aims to study the effects of passing an ILI tool through pipelines consisting of straight and curved segments. A three-dimensionial finite element (FE) model based on the Timoshenko beam theory is developed to model the vibration response of curved pipes during the passage of an ILI tool. Lab-scale experiments are performed to verify the simulation results of the developed FE model. The developed model is further verified through the FE analysis performed in abaqus™ implicit. A comparison of the simulation and experimental results shows that the proposed FE model effectively and accurately predicts the dynamic stress and dynamic displacements of multisegment pipes during the passage of an ILI tool.

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: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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