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Transcritical CO2 Refrigeration System in Tropical Region

2015· book-chapter· en· W2501192898 on OpenAlexaboutno aff
D. K. Gupta, Mani Sankar Dasgupta

Bibliographic record

VenueAdvances in mechatronics and mechanical engineering (AMME) book series · 2015
Typebook-chapter
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantRefrigerationVapor-compression refrigerationTranscritical cycleEnvironmental economicsEnvironmental scienceNatural (archaeology)Process engineeringComputer scienceEngineeringGeographyEconomicsMechanical engineeringGas compressor

Abstract

fetched live from OpenAlex

Environmental concerns and enactments of Montreal and Kyoto Protocol for sustainable growth is a welcome impetus for researchers towards a quest for ecologically safe and natural refrigerants and cost effective designs of refrigeration systems. Carbon dioxide (CO2) is one such natural refrigerants that, although was abandoned once due techno-economic reason, has been receiving tremendous attention and is viewed as a strong candidate for long term alternative of synthetic refrigerants. The commercial success of CO2 as a refrigerant and its universal acceptance, however demands cost effective and widely accepted technology operable under various environmental conditions. In this chapter, the use of CO2 as refrigerant in trans-critical vapor compression system is discussed in detail along with its unique challenges associated with operating in tropical region. Further the opportunities for using these systems in tropical region with specific systematic modification are explored. Discussions on component design and system level performance analysis are also included in the chapter.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.003

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 head0.206
Teacher spread0.197 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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