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Record W4389541334 · doi:10.17118/11143/20832

Energy and exergy analysis of possible alternatives to R134a in a vapourcompression refrigeration cycle of a water cooler unit

2023· article· en· W4389541334 on OpenAlexafffund
Mehdi Bencharif, Sergio Croquer Perez, Sébastien Poncet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsUniversité de Sherbrooke
FundersMitacs
KeywordsExergyRefrigerationVapor-compression refrigerationEnvironmental scienceCompression (physics)Unit (ring theory)Energy (signal processing)Process engineeringThermodynamicsRefrigerantGas compressorEngineeringMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

Abstract: In this paper, the energy and exergy performance of a vapour-compression refrigeration cycle of a water dispenser unit has been analyzed theoretically using different refrigerants as possible alternative substitutes to R134a. The selected low ?Global Warming Potential (GWP) ? refrigerants are: HydroFluoroOlefins (HFO) R1234yf and R1234ze(E), HydroCarbons (HC) R290 and R600a, and HydroFluoroCarbon (HFC) R152a. The process was evaluated based on the evaporator and condenser temperatures, which range between -10 and 5?C and between 30 and 45?C, respectively. The theoretical model based on the first and second laws of thermodynamics has been developed using the Matlab environment. The performances of the vapor compression cycle are discussed in terms of coefficient of performance, exergy destruction and exergy efficiency. The results show that the maximum COP achieved is 5.12 and 5.10 for R152a and R600a respectively, the highest total exergy destruction is about 82.82 W for R290, the highest exergy efficiency is about 55.12% for R600a.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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