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Record W3106749966 · doi:10.2478/ijame-2020-0054

Mechanical Vapour Compression Refrigeration System: Review Part 1: Environment Challenge

2020· article· en· W3106749966 on OpenAlexaboutno aff
Raid Ahmed Mahmood, Omar Mohammed Ali, M. M. Noor

Bibliographic record

VenueInternational Journal of Applied Mechanics and Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantRefrigerationAir conditioningVapor-compression refrigerationOzone layerEnvironmental scienceProcess engineeringMontreal ProtocolGlobal warmingOzoneEngineeringMechanical engineeringHeat exchangerClimate changeMeteorologyEcologyGeography

Abstract

fetched live from OpenAlex

In Australia and others developed countries, concerns about global warming have increased, and these concerns influence the use of refrigerants as working fluids in mechanical vapour compression refrigeration systems. One of the most important aspects of refrigerant selection is to reduce its impact on the environment and the ozone layer. This paper provides a comprehensive review of various theoretical and experimental studies which have been carried out on air conditioning and refrigeration applications to investigate the effect of refrigerants on the environment. The analysis in this paper reveals that alternative refrigerants are the most suitable working fluids that can be used in refrigeration systems to meet the needs of the environment. This study also suggests that natural types of refrigerants such as water, carbon dioxide, and hydrocarbon will play a significant role in protecting the environment and providing alternative friendly refrigerants to be used in refrigeration and air conditioning systems.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations16
Published2020
Admission routes1
Has abstractyes

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