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Record W3015492427 · doi:10.18280/i2m.190101

Effect of Phase Change Material Eutectic Plates on the Electric Consumption of a Designed Refrigeration System

2020· article· en· W3015492427 on OpenAlexvenueno aff
Rachid Djeffal, Sidi Mohammed El Amine Bekkouche, Mustapha Samai, Zohir Younsi, Redouane Mihoub, Abdelaziz Benkhelifa

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2020
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerationEutectic systemPhase changePhase-change materialMaterials sciencePhase (matter)MetallurgyMechanical engineeringEngineeringChemistryEngineering physicsMicrostructure

Abstract

fetched live from OpenAlex

The main objective of this research is to design an improved refrigeration system incorporating a phase change material; experimental measurements have been carried out to reduce energy consumption. The adopted method was based on the comparison of two cases: the first corresponded to a classical cell and the second concerned an isolated experimental cell with phase change material (PCM) eutectic plates. An energy saving of up to 12.88% has been recorded. In the event of leaks, the operation will subsequently generate an over-consumption which was estimated at 22.03% compared to the initial consumption and 42.16% for a cell combined with eutectic plates. An incorrect choice of the thermostat temperature leads to unnecessary and expensive energy consumption. These materials have helped us to limit and sometimes almost avoid the stratification of temperature; on average, the temperature stratification was 0.68C per 23 cm. A better air circulation from the evaporator can be promoted with a correct food distribution; it is possible, therefore, to reduce significantly the over-consumption due to cooling/freezing of the food. For a programmable thermostat (-10C), a light over-consumption that can reach only 1.69% for bread and about 0.40% for water has been achieved.

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 score0.025
Threshold uncertainty score0.409

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 head0.039
GPT teacher head0.281
Teacher spread0.243 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueInstrumentation Mesure MétrologieSame topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207