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Single-phase air parallel ejectors: An experimental and numerical study

2023· article· en· W4317726070 on OpenAlexafffund
Charles P. Rand, Michel Poirier, Sébastien Poncet

Bibliographic record

VenueInternational Journal of Refrigeration · 2023
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaHydro-QuébecUniversité de Sherbrooke
KeywordsInjectorRefrigerationFlexibility (engineering)Cooling capacityWork (physics)Test benchComputer scienceEntrainment (biomusicology)SimulationAutomotive engineeringMechanical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Ejector-based refrigeration systems (ERS) raise a lot of interest, given their lower electrical consumption compared to conventional refrigeration systems. However, a single ejector suffers from a lack of capacity flexibility, while the commercial success of this technology often relies on its ability to handle the fluctuating heating load availability and cooling load requirements. To that end, this work proposes the use of multiple ejectors having similar performance curves that could operate individually or simultaneously, according to the needs. To investigate the suitability of the proposed solution, a test bench equipped with two ejectors having different capacities, and a turbulence model , are used. The results show that the performance of the two ejectors is exactly the same, whether they are used separately or simultaneously. To the best of the author’s knowledge, it is shown here for the first time that, when several ejectors are used simultaneously, they each retain their performance, without any disturbance of their entrainment ratio or critical outlet pressure. This important new finding paves the way for the design of ERS for variable capacity applications, such as solar cooling systems .

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.368

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 communication0.0000.001
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 head0.027
GPT teacher head0.322
Teacher spread0.295 · 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 designBench or experimental
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

Citations10
Published2023
Admission routes2
Has abstractyes

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