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Record W2793761112 · doi:10.1103/physreve.97.052145

Current fluctuations in quantum absorption refrigerators

2018· article· en· W2793761112 on OpenAlexafffund
Dvira Segal

Bibliographic record

VenuePhysical review. E · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarnot cycleChillerAbsorption refrigeratorRefrigerator carCoefficient of performanceHeat exchangerThermodynamicsNoise (video)Absorption (acoustics)Heat transferWork (physics)RefrigerationDissipationPhysicsHeat pumpComputer scienceOptics

Abstract

fetched live from OpenAlex

Absorption refrigerators transfer thermal energy from a cold bath to a hot bath without input power by utilizing heat from an additional "work" reservoir. Particularly interesting is a three-level design for a quantum absorption refrigerator, which can be optimized to reach the maximal (Carnot) cooling efficiency. Previous studies of three-level chillers focused on the behavior of the averaged cooling current. Here, we go beyond that and study the full counting statistics of heat exchange in a three-level chiller model. We explain how to obtain the complete cumulant generating function of the refrigerator in a steady state, then derive a partial cumulant generating function, which yields closed-form expressions for both the averaged cooling current and its noise. Our analytical results and simulations are beneficial for the design of nanoscale engines and cooling systems far from equilibrium, with their performance optimized according to different criteria, efficiency, power, fluctuations, and dissipation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.360
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations46
Published2018
Admission routes2
Has abstractyes

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