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Record W2071247594 · doi:10.1002/er.1185

Investigation of the effects of vapour quality and oil concentration on performance of a swash plate compressor

2006· article· en· W2071247594 on OpenAlexaff
Shujun Wang, Junjie Gu, Tim Dickson

Bibliographic record

VenueInternational Journal of Energy Research · 2006
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsRefrigerantGas compressorSwashIsentropic processVapor-compression refrigerationVolumetric efficiencyMass flowMass flow rateMaterials scienceThermodynamicsChemistryAutomotive engineeringMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The main objective of the present study is to experimentally investigate the effects of vapour quality and oil concentration on the performance of a swash plate compressor for automotive air conditioning systems. R-134a is used as refrigerant. The compressor used is a typical automotive swash-plate-type compressor driven by a 10 hp variable-speed electric motor and lubricated by polyalkylene glycol (PAG) oil. The variables measured during the experiment are pressure, temperature, oil concentration, total mass flow rate and vapour mass flow at the inlet and outlet of the compressor. The experiment was performed at varying compressor speeds, compression ratios and vapour quality. The results revealed some unknown aspects of the compression process in an automotive air conditioning system. The vapour quality does not affect volumetric efficiency, but influences isentropic efficiency of the compressor. In the vapour quality range of 80–90%, isentropic efficiency decreases with increasing vapour quality. During the compression process, only a portion of the liquid refrigerant evaporates. However, at the outlet of the compressor, refrigerant/oil mixture never reaches steady state. The evaporation ratio decreases with increasing compressor speed, and with increasing vapour quality as well. Copyright © 2005 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 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 score0.063
Threshold uncertainty score0.133

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 communication0.0000.000
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 head0.036
GPT teacher head0.312
Teacher spread0.276 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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