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Density Effects on Dilution and Height of Vertical Fountains

2015· article· en· W1503598358 on OpenAlexafffund
Nadeem Ahmad, R. E. Baddour

Bibliographic record

VenueJournal of Hydraulic Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFroude numberDilutionBuoyancyMechanicsMeteorologyMaximum densityTurbulenceAtmospheric sciencesDensity of airRelative densityEnvironmental scienceHydrology (agriculture)Flow (mathematics)MathematicsGeologyChemistryGeotechnical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

The effects of relative density difference on the dilution and height of vertical negative buoyant jets were investigated experimentally. These effects, which are related to fluid density, occurred in addition to the buoyancy effects commonly parameterized by the densimetric Froude number. The density effects have generally been ignored in previous investigations and in practice on the basis of Boussinesq’s approximation. The density effects could, however, be relevant in connection to hypersaline discharges from desalination plants and dense discharges from oil and potash mining industries. A series of seven upward fountain experiments were performed in the laboratory to carry out this investigation. The flow was turbulent, the relative density difference varied from 0.0014 to 0.064, and the densimetric Froude number varied from 2.13 to 15.7. The results showed that an increase in the relative density difference reduced both the minimum return dilution and maximum height of the fountains. The observed maximum reduction in dilution was 48%, and the maximum reduction of height was 19%. The results also demonstrated that upward fountains are dynamically similar to downward fountains.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.387

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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