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Record W4366827295 · doi:10.3390/en16093610

Diffusion Absorption Refrigeration Systems: An Overview of Thermal Mechanisms and Models

2023· article· en· W4366827295 on OpenAlexaff
Baby-Jean Robert Mungyeko Bisulandu, Rami Mansouri, Adrian Ilinca

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à MontréalUniversité du Québec à Rimouski
Fundersnot available
KeywordsAbsorption refrigeratorRefrigerationRenewable energyScope (computer science)Vapor-compression refrigerationProcess engineeringGreenhouse gasFossil fuelGeothermal gradientEnvironmental scienceGeothermal energyComputer scienceSystems engineeringRefrigerantEnvironmental economicsEngineeringMechanical engineeringWaste managementHeat exchangerElectrical engineeringPhysicsEconomics

Abstract

fetched live from OpenAlex

The energy transition, originating in the limitation of fossil resources and greenhouse gas (GHG) emission reduction, is the basis of many studies on renewable energies in different industrial applications. The diffusion absorption refrigeration machines are very promising insofar as they allow the use of renewable resources (solar, geothermal, waste gas, etc.). This technology is often considered an alternative to vapor compression systems in cooling and refrigeration applications. This paper aims to overview the thermal mechanisms related to modeling system energy sources and highlight the primary methodologies and techniques used. We study and analyze the technology’s current challenges and future directions and, finally, identify the gaps in the existing models to pave the way for future research. The paper also gives a classification of absorption refrigeration systems (ARS) to position and limit the scope of the study. The paper will help researchers who approach the various aspects to have a global synthetic analysis of the mechanisms characterizing the modeling of energy sources of absorption refrigeration machines.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.256

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 head0.038
GPT teacher head0.288
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations20
Published2023
Admission routes1
Has abstractyes

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