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Record W1977746833 · doi:10.1063/1.1536016

Ideal magnetocaloric effect for active magnetic regenerators

2003· article· en· W1977746833 on OpenAlexafffund
Andrew Rowe, John Barclay

Bibliographic record

VenueJournal of Applied Physics · 2003
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic and transport properties of perovskites and related materials
Canadian institutionsUniversity of Victoria
FundersNatural Resources Canada
KeywordsMagnetic refrigerationBrayton cycleRegenerative heat exchangerRefrigerantThermodynamicsHeat transferThermodynamic cycleMaterials scienceMechanicsMagnetic fieldHeat exchangerMagnetizationPhysics

Abstract

fetched live from OpenAlex

The active magnetic regenerator (AMR) uses a magnetic solid as a thermal storage medium and as a working material in a refrigeration cycle. Thermodynamically coupled to a heat transfer fluid, the regenerator produces a cooling effect and generates a temperature gradient across the AMR. The coupling between the heat transfer fluid and the magnetic refrigerant is a key aspect governing the operating characteristics of an AMR. To increase our understanding of AMR thermodynamics, we examine the entropy balance in an idealized active magnetic regenerator. A relation for the entropy generation in an AMR with varying fluid capacity ratios is derived. Subsequently, an expression describing the ideal magnetocaloric effect (MCE) as a function of temperature is developed for the case of zero entropy generation. Finally, the link between ideal MCE and refrigerant symmetry is discussed showing that an ideal reverse Brayton-type magnetic cycle cannot be achieved using materials undergoing a second-order magnetic phase transition.

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 score0.016
Threshold uncertainty score0.574

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)0.0010.000

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations37
Published2003
Admission routes2
Has abstractyes

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