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Record W2966731641 · doi:10.36909/jer.v9i1.8745

Experimental modeling of refrigerants at high ambient temperature

2021· article· en· W2966731641 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Engineering Research · 2021
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantAir conditioningEnvironmental scienceOzone layerMontreal ProtocolOzone depletionGlobal-warming potentialSolverOzoneMeteorologyThermodynamicsHeat exchangerComputer scienceGreenhouse gas

Abstract

fetched live from OpenAlex

High ambient temperature is well known to have an adverse impact on air conditioners; it lowers their cooling capacity along with their coefficient of performance. Moreover, air conditioning plays a significant role in damaging the environment as it contributes to both global warming and ozone depletion. In the past few decades, the Montreal agreement was introduced to protect the ozone layer from substances that contribute to its depletion. Developing countries are now facing the challenges of phasing out HCFCs such as R22 in the air conditioning sector. The aim of this study is to find an alternative refrigerant that can replace R22 after its phase out. The new refrigerant must have low environmental impacts and should be able to withstand operation in high ambient temperature countries. In this study, the hydrocarbon refrigerant R290 and the hydrofluorocarbon R410A were selected as alternatives. Furthermore, experimental testing was carried out on the baseline refrigerant R22 and R410A using a 7kW ducted split unit at three outdoor conditions: 35oC, 48oC, and 52oC. From the experimental results, a model based on R22 and R410A was developed using Engineering Equation Solver (EES). The model was successful in verifying the experimental results within a 15% error range, and was tested again with external data from (Eltalouny & Nielsen, 2016) [10]. After confirming its validity, the model was simulated to predict the performance of the alternative refrigerant R290 under the same experimental conditions and input data. To test the model’s validity on R290, results from (Eltalouny & Nielsen, 2016) were used. The model succeeded in predicting the COP and power consumption. The refrigerants demonstrated deterioration in their performance at elevated temperatures compared to their performance at a typical operating regime. With proper safety measures, and enhancement to the compressor efficiency, R290 can be considered as a promising alternative. 

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.366

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 integrity0.0000.001
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 head0.034
GPT teacher head0.304
Teacher spread0.269 · 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