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Record W4402454959 · doi:10.11159/htff24.210

Cooling With Magnets-Proving The Concept Of Cascaded Caloric Heat Pipes

2024· article· en· W4402454959 on OpenAlexvenueno aff
Schipper Jan, Bartholomé Kilian, Unmüßig Sabrina, Jürgen Wöllenstein

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetCaloric theoryNuclear engineeringMechanical engineeringComputer scienceAutomotive engineeringEnvironmental sciencePhysicsMaterials scienceEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Heating and cooling make up a large portion of our total energy consumptions. Today most use cases are being addressed by conventional vapor compression systems. These vapor compression systems rely on harmful or explosive refrigerants and typically achieve less than 50% of Carnot efficiency. An alternative option are magneto caloric systems. These use solid state refrigerants that exhibit a temperature change corresponding to a magnetic field change. As shown in a recent study by Schipper et al. [1] these types of systems have the potential to be more efficient than conventional vapor compression systems. One type of magneto caloric cooling system is the active magneto caloric heat pipe. Unlike other concepts it uses evaporation and condensation as a heat transfer method. This has proven to lead to increase the specific cooling power by almost an order of magnitude and thereby lowering system costs [2]. However, the original prototype achieved only a temperature difference less than 2 K and 42 W of cooling power. For this study a cascaded system consisting of seven sequential caloric segments connected by check-valves was build. With this design we were able to increase the maximum temperature difference to 11.5K and the maximum cooling power to 130W. The design was studied using both ethanol as well as methanol as the heat transfer fluid.

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 score0.012
Threshold uncertainty score0.630

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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