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Analysis of Water Hammer Attenuation in Applications with Varying Valve Closure Times

2014· article· en· W2032496072 on OpenAlexaff
E. Yao, G. Kember, David Hansen

Bibliographic record

VenueJournal of Engineering Mechanics · 2014
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWater hammerAttenuationMechanicsClosure (psychology)Flow (mathematics)GeologyMathematicsPhysicsOptics

Abstract

fetched live from OpenAlex

A multiple scales asymptotic analysis is developed to describe the attenuation of a water hammer pressure wave initiated by a time-varying valve closure. The analytical results expose a simple rule-of-thumb relationship between water hammer pressure wave attenuation and the periodic average of the absolute flow velocity that is predicted by a quasi-steady friction model. The effect of flow reversals on the pressure wave attenuation is examined through comparison with a similar method applied to the water hammer generated during flow establishment, wherein flow reversals do not occur and there is a nonzero net flow. Although the analytical description is based on the assumption that the water hammer is generated by a sudden valve closure, its practical usefulness is extended by using the numerical solution as a guide to demonstrate its validity for a range of valve closure durations. A qualitative upper limit on closure times to which the analytic results may be applied is also found. All results are numerically verified using the method of characteristics.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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 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

Citations33
Published2014
Admission routes1
Has abstractyes

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