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Record W1992691136 · doi:10.1115/ipc2014-33647

How Safe Failure Pressure Ratios are Related to %SMYS

2014· article· en· W1992691136 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsLimitingReliability engineeringReliability (semiconductor)Pipeline (software)Anomaly (physics)Pipeline transportConfusionComputer scienceEnvironmental scienceEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Majority of the pipeline operators manage corrosion using inline inspection (ILI). ILIs enable operators to choose which anomalies should be excavated and, on the flip side, which anomalies are safe to remain on the pipe. If the remaining anomalies grow to a critical size before the next ILI cycle there will be in-service failure. This is avoided by forecasting the growth of the measured defects appropriately and assessing them for future integrity. When an ILI is performed on a pipeline the ILI vendor reports the anomaly sizes, burst pressures and the Failure Pressure Ratios for each anomaly. Failure Pressure Ratio (FPR) for an anomaly is defined as the burst pressure divided by the Maximum allowable operation pressure (MAOP). In order to avoid ruptures, operators will excavate anomalies that have a limiting value of FPR. These limiting values are also referred to as response criteria, excavation criteria, safe failure pressure ratios, or safety factors. As the FPR depends heavily on the operating stress (or %SMYS) the limiting FPR value, below which we respond, should also depend on %SMYS. However, currently there is inconsistency in the use of response criteria in the industry. Some utilize the same response criteria for all pipe irrespective of %SMYS. This study shows that using the same limiting FPR does not provide the same level of safety for different %SMYS scenarios. It shows that the size of remaining anomalies in the lower %SMYS pipe would be significantly larger than in higher %SMYS pipe, leading to lower reliability in pipelines that have lower %SMYS. For gas pipelines, these lower %SMYS pipelines are often in higher location class pipelines with higher consequences of failure. In other words, even though the FPR value is the same for two anomalies in two different %SMYS pipes, the two anomalies would have very different probabilities of failure, where one anomaly could be safe while the other is not. This paper examines the ranges of FPR and the safe response criteria as a function of %SMYS. It examines the remaining anomaly sizes in different %SMYS pipe as allowed by current standards. The effect of the uncertainties on the response criteria due to measurement errors, material and geometric properties and model errors are also examined. It examines the response criteria in different jurisdictions. The effect of using different assessment equations, such as Modified B31G or RSTRENG, on the response criteria is also discussed. In order to obtain consistent safety, the consideration of %SMYS and consequences in defining a response criterion is discussed. Many sets of ILI data, which have pipe in different %SMYS, have been assessed in order to examine practical ranges of FPR with %SMYS. The practical implications of different response criteria are studied and discussed.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.325

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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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