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Record W2010549399 · doi:10.1504/ijex.2008.016672

Exergy analysis of advanced transcritical CO<SUB align=right>2 air conditioning cycles

2008· article· en· W2010549399 on OpenAlexafffund
Wendy W. Yang, Amir Fartaj, David S.‐K. Ting

Bibliographic record

VenueInternational Journal of Exergy · 2008
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTranscritical cycleIntercoolerGas compressorEvaporatorExergyAir conditioningThermal expansion valveThermodynamicsEnvironmental scienceExergy efficiencyHeat exchangerRefrigerantSingle stageProcess engineeringMaterials scienceNuclear engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The energy and exergy balance methods were performed on two advanced transcritical CO2 air conditioning cycles presented in the literature. In addition to the basic transcritical CO2 air conditioning cycle which has five main components, compressor, gas cooler, evaporator, expansion valve and internal heat exchanger, two advanced cycles are investigated; both have two-stage compressor (low stage and high stage) and intercooler, with one of them has an additional Flash Gas Removal (FGR) chamber. The results demonstrated that the compressor-low stage has the largest exergy loss, while the intercooler has the smallest exergy loss in advanced transcritical CO2 air conditioning cycles. Moreover, it demonstrated that the FGR had a minimal effect on improving COP of the system.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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

Citations2
Published2008
Admission routes2
Has abstractyes

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