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Record W2771013656 · doi:10.5539/mer.v7n2p18

Numerical Analysis of Flow Behavior in Vortex Tube for Different Gases

2017· article· en· W2771013656 on OpenAlexvenueno aff
Kiran D. Devade, Ashok T. Pise, Atul R. Urade

Bibliographic record

VenueMechanical Engineering Research · 2017
Typearticle
Languageen
FieldEngineering
TopicRanque-Hilsch vortex tube
Canadian institutionsnot available
Fundersnot available
KeywordsVortex tubeVortexTube (container)TurbulenceMechanicsFlow (mathematics)Materials scienceWork (physics)Computational fluid dynamicsThermodynamicsPhysicsComposite material

Abstract

fetched live from OpenAlex

The vortex tube is an energy separation device that separates compressed gas stream into a low and a high temperature stream. Present work reports the flow behavior inside the vortex tube for different commonly used fluids with varied properties like Air, He, N2, CO2 and NH3. Flow behavior investigation for three-dimensional short straight-diverging vortex tube is done with CFD code (ANSYS 16.0). Different turbulent models, standard k-epsilon, Realizable k-epsilon and RNG k-epsilon are tested. Realizable k-epsilon model was then used for analysis. Flow behavior of gases with varied multi-atomic number is analyzed and compared with literature. The effect on temperature for N2 is found to be better, followed by He, CO2, Air and NH3. Energy separation for N2 is 46 % higher than all other gases. Energy separation and flow behavior inside vortex tube is analyzed and compared with literature.

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 score0.003
Threshold uncertainty score0.005

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.056
GPT teacher head0.348
Teacher spread0.292 · 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
Published2017
Admission routes1
Has abstractyes

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