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Record W2525121624 · doi:10.1139/cjce-2016-0321

Air pressure drop in a penstock during the course of intake-gate closure

2016· article· en· W2525121624 on OpenAlexafffundvenueabout
Michel-Olivier Huard, S. Samuel Li

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPenstockInflowOutflowAir entrainmentClosure (psychology)Pressure dropElectrical conduitHydroelectricityEnvironmental scienceAirflowDrop (telecommunication)EngineeringHydrology (agriculture)Geotechnical engineeringMeteorologyMechanicsCivil engineeringElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

In urgent situations, the intake gates of a hydroelectric power generating station must be closed to stop the inflow of water to the station’s penstock. During the course of gate closure, the air pressure in the penstock can drop drastically, posing safety risks. This paper aims to develop reliable methods for predicting pressure drop. The methods consider time-dependent water inflow, air entrainment in the penstock, air–water outflow from the penstock, and airflow down the air vent system. The methods are used to calculate time-dependent flow and pressure drop for two stations in Quebec, producing results in good comparison with field measurements. In the penstock, water jet intensifies in the first half of the closure time period, whereas air pressure drops in the last one third of the time period. Air entrainment is an important cause of pressure drop. The methods are useful for planning air-vent upgrades and safe closure operations.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.162
Teacher spread0.159 · 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

Citations4
Published2016
Admission routes4
Has abstractyes

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