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Record W3210706655 · doi:10.1080/1573062x.2021.1995764

Energy exchange analysis of a closed conduit transient mixed flow following an air pocket entrapment using a simplified shock-fitting approach

2021· article· en· W3210706655 on OpenAlexafffund
Arman Rokhzadi, Musandji Fuamba

Bibliographic record

VenueUrban Water Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsKinetic energyCabin pressurizationElectrical conduitMaterials scienceFlow (mathematics)Shock (circulatory)Transient (computer programming)Mechanical energyEnergy (signal processing)ThermodynamicsComposite materialMechanical engineeringClassical mechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The effects of different variables of closed conduit transient partially pressurized flows on the maximum air pressure are investigated from an energy exchange perspective. It was found that when the air length increases, the maximum pressure decreases because a larger portion of the driving energy will be stored as kinetic energy in the pressurized flow and is dissipated by the friction force. In contrast, when the water depth of the free-surface flow increases, the kinetic energy and dissipating energy of the pressurized flow decrease. Thus, the energy absorbed by the air pocket increases, and the maximum pressure increases. However, after a water depth ratio of 0.8, the maximum pressure decreases even though the kinetic energy and dissipating energy decrease. The reason is that the moving interface receives enough energy, and pressurization of the free-surface flow zone occurs. Note that this pressurization was experimentally observed by Hamam and McCorquodale (1982).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.019
GPT teacher head0.231
Teacher spread0.211 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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