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Record W3035627272 · doi:10.18462/iir.icr.2019.1256

Droplet injection at the diffuser outlet in an ejector-based refrigeration cycle working with R245fa.

2021· article· en· W3035627272 on OpenAlexaboutno aff
Mehdi Bencharif, Sébastien Poncet, Hakim Nesreddine

Bibliographic record

VenueInstitut International du Froid · 2021
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsInjectorDiffuser (optics)RefrigerationWork (physics)Coefficient of performanceHeat pump and refrigeration cycleThermodynamic cycleWorking fluidMechanicsThermodynamicsCooling capacityMixing (physics)Transcritical cycleMechanical engineeringEngineeringEnvironmental scienceRefrigerantGas compressorPhysics

Abstract

fetched live from OpenAlex

This work presents a combined experimental and thermodynamic approach used to investigate the influence of droplet injection on the performance of an ejector-based refrigeration cycle developed for Heating, Ventilation and Air-Conditioning applications. A thermodynamic model has been developed within Matlab for each component of the cycle with a particular emphasis on the ejector modeling, which is based on the constant pressure mixing assumption. It is validated against the experimental data obtained on the Hydro-Quebec’s test rig working with R245fa. The numerical model agrees fairly well with the experiments. The results show that injecting R245fa droplets at the end of the ejector diffuser at the condensing temperature has a significant impact on the performance of the ejector itself and of the whole cycle. It reduces especially and significantly the temperature of the overheated vapors at the outlet of the ejector leading to a significant improvement of the Coefficient of Performance (COP).

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 score0.303
Threshold uncertainty score0.414

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 head0.014
GPT teacher head0.215
Teacher spread0.202 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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