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Record W1997182328 · doi:10.1115/ipc2014-33579

Large Scale Test Apparatus to Test External Leak Detection Technologies

2014· article· en· W1997182328 on OpenAlexafffund
Chris Apps, Istemi F. Ozkan, Tania Rizwan, Marzie Derakhshesh, Scott Medynski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsNational Research Council Canada
FundersWestern Economic Diversification Canada
KeywordsLeak detectionPipeline transportLeakTrenchRobustness (evolution)Computer scienceReliability (semiconductor)Reliability engineeringEngineeringMechanical engineeringLayer (electronics)Materials science

Abstract

fetched live from OpenAlex

When it comes to evaluating traditional computational leak detection technologies pipeline operators have a suite of simulated testing methods available. In the last several years however External Leak Detection Technologies have become more mature and potentially could provide operators with another layer of leak detection with more sensitivity than seen in traditional methods. The challenge with these technologies is in the evaluation of their sensitivity, reliability, and robustness. ENBRIDGE INC (Enbridge) and C-FER Technologies 1999 Inc. (C-FER) begun a comprehensive study to assess the state-of-the-art external, continuously distributed sensors for leak detection in early 2012. Initially, a technology review was undertaken to identify commercial, off-the-shelf technologies with the potential to detect small leaks of oil from buried pipelines. From this literature review, four technologies were identified; Distributed Temperature Sensing (DTS), Distributed Acoustic Sensing (DAS), Vapor Sensing Tubes (VST), and Hydrocarbon Sensing Cables (HSC). All four methods require proprietary materials and technology, which have had limited independent testing efforts to date. To evaluate these four leak detection methods and their vendors in an objective way, Enbridge and C-FER initiated the design and construction of a large-scale External Leak Detection Experimental Research apparatus (ELDER) that can accommodate a full-size segment of pipeline within a trench, at the same scale used in pipeline construction in North America. An instrumented pipe segment is buried in the trench with sensing cables laid alongside. The apparatus generates leaks with controlled variables including rate, pressure and temperature, and at various locations to accurately represent pipeline leaks. This paper summarizes the literature review on the four selected leak detection technologies that were identified as candidates for large-scale evaluation. The discussion will also include features of the ELDER apparatus, and re-engineered pipeline construction techniques that were required to accurately represent a full-scale pipeline trench within a laboratory environment.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.450
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.223
Teacher spread0.215 · 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
Published2014
Admission routes2
Has abstractyes

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