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Record W2995575594 · doi:10.1063/1.5138869

Transcritical CO2 refrigeration plants: Experimental campaign and model-based evaluations of new technologies

2019· article· en· W2995575594 on OpenAlexaboutno aff
Fabrizio Santini, Davide Di Battista, Carlo Villante, M Orlandi

Bibliographic record

VenueAIP conference proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerationComputer scienceTranscritical cycleEngineeringMechanical engineeringRefrigerantGas compressor

Abstract

fetched live from OpenAlex

The paper copes with a transcritical CO2 refrigeration plant: R744 (CO2) is an economic and natural refrigerant with a negligible impact on the environment and with good thermodynamic and safety properties compared to synthetic ones, and it is a sustainable alternative in commercial refrigeration of retail food. A specific 18 kWt test bench is available and under testing in the lab of the University of L’Aquila. Experimental tests were performed at different external temperatures between 8 °C and 33 °C: first results showed that COP (Coefficient Of Performance) decreases when external temperature grows up. Experimental data made it possible to calibrate a mathematical model of the test rig, useful to evaluate the effect of the possible introduction of new technologies: variation of receiver pressure, intercooled two-stage compression and expander in place of the high pressure throttling valve. All these technologies may be useful to improve plant efficiency and to promote new “green” installations in warmer climates, where food conservation standard in terms of goods cold-chain are increasingly demanding, especially considering EU and Global goals on HFC reduction targets (e.g. EU F-gas regulation - 2014 and Kigali amendment to the Montreal Protocol - 2016).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.527

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 head0.027
GPT teacher head0.271
Teacher spread0.244 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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