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Record W2019920576 · doi:10.1139/s04-014

Understanding air–water mass transfer in rectangular dropshafts

2004· article· en· W2019920576 on OpenAlexvenueno aff
Hubert Chanson

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsAerationOutflowMass transferHydraulicsResidence time (fluid dynamics)MechanicsScalingEnvironmental scienceResidence time distributionFlow (mathematics)BubbleParticle (ecology)Mass fluxMass transfer coefficientFlux (metallurgy)Hydrology (agriculture)Materials scienceMeteorologyGeologyPhysicsGeotechnical engineeringGeometryEngineeringThermodynamicsWaste managementMathematics

Abstract

fetched live from OpenAlex

A dropshaft is a vertical structure connecting two channels with different invert elevations. Four configurations of rectangular dropshafts were investigated systematically to study the effects of outflow direction and pool depth on particle residence times and flow aeration. The best hydraulic design was that with 180° outflow direction and deep pool shaft. For that design, a full-scale study was conducted, the scaling ratio between prototype and model being 3.1:1. Although similar trends were seen in both model and prototype, scale effects were observed in terms of particle residence times and bubble swarm depths. In the prototype, detailed air–water flow measurements were performed in the shaft pool and the mass transfer equation was integrated using measured interfacial areas and particle residence times. The results demonstrate that the air–water mass transfer is the largest at low flow rates (regime R1) because of large residence times and significant interfacial area. Overall the present study provides new understanding of the basic mechanisms of air–water mass transfer in rectangular dropshafts. Key words: dropshaft, mass transfer, aeration, hydraulics.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.160
Teacher spread0.152 · 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

Citations8
Published2004
Admission routes1
Has abstractyes

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