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Record W2771999338 · doi:10.1115/1.4038720

Improved Folias Factor and Burst Pressure Models for Corroded Pipelines

2017· article· en· W2771999338 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsMemorial University of Newfoundland
FundersResearch and Development Corporation of Newfoundland and Labrador
KeywordsPipeline transportCorrosionRock burstStructural engineeringWork (physics)Friction factorEngineeringMechanicsMaterials scienceGeotechnical engineeringPhysicsMechanical engineeringComposite materialTurbulence

Abstract

fetched live from OpenAlex

Burst pressure models are used for the fitness-for-purpose assessment of energy pipelines. Existing burst pressure models for corroded pipelines are unable to predict the pipe capacity correctly. In this paper, an improved burst pressure model is developed for corroded pipelines considering the burst pressure of flawless pipes and a reduction factor due to corrosion separately. The equation for the burst pressure of flawless pipe is revised based on the theory of the thick wall cylinder. A new model for the Folias factor is proposed for calculating the reduction factor. The new model for the Folias factor incorporates the depth of corrosion defect, whereas the existing models do not account for the effect of the defect depth. The authors' earlier work revealed that the Folias factor depends on the depth of defect. The proposed burst model reasonably predicts the burst pressures obtained from finite element (FE) analysis conducted in this study and the burst test results available in the published literature.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.259
Teacher spread0.240 · 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