MétaCan
Menu
Back to cohort
Record W2771013656 · doi:10.5539/mer.v7n2p18

Numerical Analysis of Flow Behavior in Vortex Tube for Different Gases

2017· article· en· W2771013656 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.146
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.056
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