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Record W1923583191

R-134a와 R-152a 냉매를 이용한 자동차용 에어컨 시스템의 성능에 관한 실험적 연구

2006· article· ko· W1923583191 on OpenAlexaboutno aff
김종수, 이대웅, 유성연

Bibliographic record

Venue대한설비공학회 하계학술발표회 논문집 · 2006
Typearticle
Languageko
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantCondenser (optics)Air conditioningGas compressorGlobal-warming potentialEnvironmental scienceGlobal warmingAutomotive industryMontreal ProtocolWater chillerMeteorologyThermodynamicsEngineeringClimate changeOzone layerGreenhouse gasMechanical engineeringOzoneGeography
DOInot available

Abstract

fetched live from OpenAlex

Recently, as the climate of temperature change has happened worldwide. To solve this problem, Kyoto protocol was taken to regulate global warming on Feb. 2005 and each country is making efforts to prevent global warming. In the automotive industry, R-134a refrigerant is widely used most these days because it has zero ODP(Ozone Depletion Potential). But R-134a GWP(Global Warming Potential) is so high. Therefore, replacement refrigerant desperately is needed as a alternative refrigerant. So, R-152a is considered as one of the alternative refrigerants due to zero ODP and lower GWP against as required on Europe Committee. In this paper, performance of the air conditioning system between R-134a and R-152a is investigated experimentally. In the bench level, cooling capacity, condensing capacity, COP of automotive air conditioning system are evaluated by means of air velocity entering the condenser and compressor revolution speed with optimized charge refrigerant quantity. Result of this study, R-152a refrigerant shows the possibility as alternative refrigerant of current R-134a in automotive air conditioning 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

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)0.0100.006

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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Same venue대한설비공학회 하계학술발표회 논문집Same topicEngineering Applied ResearchFrench-language works237,207