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Record W3124921614 · doi:10.1115/ipc2020-9705

Full Scale Test Validation of Fatigue Crack Growth Rate of Flaws in ERW Pipe

2020· article· en· W3124921614 on OpenAlexaboutno aff
Sanjay Tiku, Morvarid Ghovanlou, Aaron Dinovitzer, Mark Piazza, T. A. Jones

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsParis' lawPipeline (software)Structural engineeringWeldingFracture mechanicsFracture (geology)Growth rateStress (linguistics)Materials scienceStress concentrationCrack closureEngineeringMetallurgyMechanical engineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

Abstract While the general fracture mechanics principles and methodologies for calculating fatigue lives are well documented and validated, their application in the prediction of pipeline system fatigue lives differed from field experience. The source and magnitude of the conservatism inherent in the calculated fatigue life estimates are an important element when establishing integrity management programs. Of particular interest are the fatigue life estimates used in integrity management programs for electric resistance welded (ERW) pipeline systems that may have pipe seam anomalies oriented along the pipe axis. BMT Canada Ltd (BMT) was contracted by Pipeline Research Council International (PRCI) to develop a pipeline material fatigue crack growth database and conduct full scale cyclic pressure fatigue tests to develop improved crack growth rate parameters. A pipeline material fatigue crack growth database was developed using 185 fatigue crack growth rate tests on 45 pipeline materials ranging in grade from X46 to X70 and in vintage from 1937 to 2014. The database included fatigue crack growth rate tests on 18 pipe body base materials (BM) and 27 ERW weld seam materials at two different, stress ratios (R), of R-ratio = 0.1 and R-ratio = 0.6. The sampled crack growth rates observed in the pipeline steels, tested in the project were 2 to 3 times lower than the crack growth rates recommended in BS 7910. This paper presents the proposed power (Paris) law fatigue crack growth equation parameters, C and m, developed in the study. Two full-scale cyclic pressure tests were carried out to validate the use of recommended crack growth rate parameters. Axial flaws were machined in the pipe body and weld center line (WCL). Fifty-one (51) flaws of different lengths and depths were machined. The crack growth rates were monitored during the cyclic pressure tests by recording crack mouth opening displacement (CMOD). The calibration curves for correlating CMODs with crack depths were developed and validated against finite element (FE) analysis. The fatigue crack growth rates observed in the full-scale tests were then compared with existing BS 7910 and API 579 formulations. The comparison confirmed that the BS 7910 approach results in very conservative estimates of fatigue crack growth rates for axial flaws. The BS 7910 stress intensity factor formulation overestimated the bulging correction for axially oriented flaws. The API 579 fracture mechanics-based fatigue crack growth formulation combined with crack growth rate parameters developed in this program provided improved estimates for fatigue life. The fatigue crack growth rates for line pipe and ERW weld seams developed in this project were shown to be less conservative and better predictors for fatigue crack growth and represent a valuable tool for pipeline integrity management. The use of this information will enable pipeline operators to focus remedial actions on features that have the lowest estimated fatigue lives.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.211
Teacher spread0.193 · 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 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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