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Performance enhancement of some alternatives to R-502

2000· article· en· W1976154530 on OpenAlexaff
Samuel M. Sami, Daniel E. Desjardins

Bibliographic record

VenueInternational Journal of Energy Research · 2000
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsAccumulator (cryptography)RefrigerantEvaporatorCondenser (optics)ThermodynamicsChemistrySuctionNuclear engineeringMaterials scienceMechanicsEngineeringHeat exchangerPhysics

Abstract

fetched live from OpenAlex

The results of an experimental study on the behaviour of some new alternatives to R-502 using heated suction accumulator are presented. The experimental set up was composed of a fully instrumented air–source heat pump with a capacity of 12000 BTUH and equipped with a heated suction accumulator. The refrigerant temperatures were varied at the evaporator entrance to simulate various extreme conditions encountered in air–source heat pump applications. The primary parameters observed during the course of this study were mass flux, heat flux, quality evaporator and condenser thermal capacities, power consumed and pressure ratios for the azeotropic refrigerant mixtures under investigation. The test results showed that a heated suction accumulator enhanced the evaporation of more volatile component of ternary azeotropic refrigerant mixtures. Thus, increasing the mixture thermal capacity as well as the COP. Furthermore, experiments have also shown capacity increases of 27 per cent with a heat accumulator over an unheated accumulator at −15°C outside air temperatures. Copyright © 2000 John Wiley & Sons, Ltd.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.334
Teacher spread0.302 · 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 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

Citations4
Published2000
Admission routes1
Has abstractyes

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