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Numerical investigation of the flow dynamics inside single fluid and binary fluid ejectors

2019· article· en· W2969235502 on OpenAlexaff
Mouhammad El Hassan, Nikolay Bukharin

Bibliographic record

VenueJournal of Physics Conference Series · 2019
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsSAIT Polytechnic
Fundersnot available
KeywordsComputational fluid dynamicsInjectorTurbulenceArgonMechanicsEntrainment (biomusicology)Fluid dynamicsSupersonic speedNozzleThermodynamicsFlow (mathematics)Materials scienceChemistryPhysicsAtomic physics

Abstract

fetched live from OpenAlex

Abstract The present study describes the flow dynamics inside a supersonic ejector using Computational Fluid Dynamics (CFD) modelling. Numerical prediction of both the mass flow rates and the wall static pressure profiles are validated using experimental measurements. Air-Air, Argon-Argon and Argon-Air cases are investigated in term of fluid mixing and ejector performance for the same boundary conditions and ejector geometry. It is found that the molar entrainment ratio is higher for Argon-Air as compared to the single fluid cases due to the higher molecular mass ratio between the primary and the secondary fluids. It is also found that the mass entrainment ratio is lower when Air is replaced with Argon as a primary fluid. New flow physics findings based on analysis of flow momentum and turbulence quantities distribution are provided in this paper to explain such behavior.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.012
GPT teacher head0.191
Teacher spread0.180 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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