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Record W4405362070 · doi:10.1115/ipc2024-132967

Prediction of Ductile Fracture Arrest in Natural Gas Pipelines Using the Crack Tip Opening Angle

2024· article· en· W4405362070 on OpenAlexaff
Chris Bassindale, Xin Wang, W. R. Tyson, Su Xu

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2024
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNatural Resources CanadaCarleton University
Fundersnot available
KeywordsPipeline transportMaterials scienceNatural gasFracture (geology)Structural engineeringComposite materialMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract In this paper, an analytical equation is developed to predict fracture arrest CTOA requirements for gas pipelines pressurized with natural gas that undergo single phase decompression using finite element (FE) analysis. The FE model is briefly reviewed. Dynamic pipe fracture simulations were performed to determine fracture resistance curves (crack velocity as a function of pressure at constant CTOA) for modern materials and geometries. American petroleum Institute (API) standard X80 and X100 steels were examined. Pipe diameters of 914 mm, 1219 mm, and 1422 mm, and diameter to thickness ratios ranging from 50 to 100 were considered. The commercial finite element code ABAQUS 2017x-explicit was used to generate the models and solve the analyses. The constant CTOA model was implemented through a user subroutine in conjunction with shell elements. Backfill effects were modelled using smooth particle hydrodynamics and the flap pressure profile was modelled using experimental data from previous burst tests. A CTOA-modified Two-Curve approach was used to determine the arrest CTOA for a given material and geometry. An equation for specification of the arrest CTOA as a function of steel grade and design parameters (thickness, diameter, and material properties) is presented for a gas that undergoes single-phase decompression. The proposed equation was validated through comparison with experimental data.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.500

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)0.0000.000

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 head0.235
Teacher spread0.217 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueVolume 3: Operations, Monitoring, and Maintenance; Materials and JoiningSame topicFatigue and fracture mechanicsFrench-language works237,207