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Record W2999235517 · doi:10.1061/9780784482506.055

Rigid versus Flexible Pipe Material Surge Response: A Case Study for a Raw Water Pipeline

2019· article· en· W2999235517 on OpenAlexaff
Balpreet Singh, Djordje Radulj

Bibliographic record

VenuePipelines 2019 · 2019
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsHydraTek (Canada)
Fundersnot available
KeywordsWater hammerSurgePipeline (software)Water pipePipeline transportTransient (computer programming)CanalisationPipeNominal Pipe SizeStructural engineeringHead (geology)Materials scienceEngineeringMechanical engineeringPipingComputer scienceComposite materialGeologyElectrical engineering

Abstract

fetched live from OpenAlex

The fundamental equation of water hammer relates changes in velocity and head primarily through the acoustic wave speed of a pipeline. Elastic or flexible pipe materials (e.g., polyvinyl chloride) tend to have lower wave speeds than more rigid pipe materials (e.g., ductile iron) because the former have a greater ability to store excess water through expansion. One common misconception, however, is that rigid pipe materials will always produce a worse hydraulic transient (surge) response than flexible pipe materials. Through a case study involving the design of a 900 mm (36-in) diameter raw water pipeline, this paper presents an example of where the predicted surge performance was worse with flexible pipe material options than with rigid pipe material options. This paper presents an in-depth discussion of the following: modeling and system parameters that gave rise to this finding, predicted vapor cavity formation and collapse in numerical models, and the importance of not preferring one pipe material over another for surge design conditions without first analyzing and comparing their predicted performance.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.001

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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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