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Record W3119591245 · doi:10.22059/ees.2020.241291

Numerical investigation of the solar activated carbon/methanol adsorption refrigeration system in Tehran’s climate

2020· article· en· W3119591245 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

VenueEnvironmental Engineering Science · 2020
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdsorptionRefrigerationMaterials scienceThermodynamicsThermalNuclear engineeringEnvironmental scienceRadiationPhotovoltaic thermal hybrid solar collectorNanofluids in solar collectorsMeteorologyProcess engineeringMechanicsChemistryEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Solar adsorption refrigeration systems are used to extract heat using solar radiation based on the adsorption phenomena. In these systems, the temperature of the solar collector plays an important role in the efficiency of a system. In this study, two different models, including the lumped and the distributed ones, are investigated to predict the temperature distribution in a specific solar collector. The operating conditions are the same for both cases. Moreover, for the solar radiation as one of the boundary conditions, the data for Tehran solar irradiation is used. The results of the temperature analysis show that the distributed model predicts a less maximum collector temperature than the lumped model which clearly results in a lower system performance. In addition, it can be concluded that because of using steel as a main material for the collector and its high thermal capacitance, it takes almost 3 days for the system to reach the periodic operating conditions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.168
Teacher spread0.161 · 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