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Record W2750930862 · doi:10.3390/e19090464

Exergy Analysis of a Parallel-Plate Active Magnetic Regenerator with Nanofluids

2017· article· en· W2750930862 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEntropy · 2017
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsHydro-QuébecUniversité de Sherbrooke
Fundersnot available
KeywordsNanofluidExergyRegenerative heat exchangerExergy efficiencyMaterials scienceHeat exchangerHeat transferThermodynamicsWorking fluidMechanicsHeat transfer coefficientVolume fractionNanofluids in solar collectorsCoefficient of performancePressure dropNuclear engineeringHeat pumpComposite materialThermalEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper analyzes the energetic and exergy performance of an active magnetic regenerative refrigerator using water-based Al2O3 nanofluids as heat transfer fluids. A 1D numerical model has been extensively used to quantify the exergy performance of a system composed of a parallel-plate regenerator, magnetic source, pump, heat exchangers and control valves. Al2O3-water based nanofluids are tested thanks to CoolProp library, accounting for temperature-dependent properties, and appropriate correlations. The results are discussed in terms of the coefficient of performance, the exergy efficiency, and the cooling power as a function of the nanoparticle volume fraction and blowing time for a given geometrical configuration. It is shown that while the heat transfer between the fluid and solid is enhanced, it is accompanied by smaller temperature gradients within the fluid and larger pressure drops when increasing the nanoparticle concentration. It leads in all configurations to lower performance compared to the base case with pure liquid water.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.318

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 head0.007
GPT teacher head0.210
Teacher spread0.202 · 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