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Record W4404864387 · doi:10.46632/aae/2/1/5

Performance and Safety Analysis of Standardized Refrigeration Topologies Using WSM Method

2024· article· en· W4404864387 on OpenAlexaboutno aff

Bibliographic record

VenueAeronautical and Aerospace Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerationNetwork topologyNuclear engineeringComputer scienceReliability engineeringEnvironmental scienceEngineeringMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

After the discontinuation of ozone-depleting substances, various pieces of equipment such as air conditioners, water chillers, and medium-temperature commercial and home refrigeration systems used the refrigerant medium-pressure hydrofluorocarbon (HFC) R-134a. However, R-134a has a Global Warming Potential (GWP) of 1300, meaning it has a detrimental influence on the environment. It is now regulated by the Kigali Amendment to the Montreal Protocol (UNEP, 2016). Since the invention of the earliest vapor compression refrigeration system by Jacob Parkin in 1834, numerous chemical compounds have been tested as refrigerants. Ammonia, carbon monoxide, sulfur oxide, and methyl chloride were the main refrigerants used in the 1930s. Benzyl sodium chloride, propane, iso-butane, and freshwater were utilized to a lesser extent. However, the class of chemicals that includes chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) became the predominant types of refrigerants after the development of dichlorofluoromethane compounds in 1930 by Thomas Middleton and Albert Henna. The weighted sum technique is a decision-making process that considers numerous possibilities and factors before choosing the best option. A weighted collection of sums, or weighted mean voting ensemble, is a machine learning strategy that combines predictions from various models, with each model's contribution being weighed according to its capacity or level of expertise. The benefits of using this method include ease of use, especially when dealing with complex problems, and the ability to assign weights in a straightforward manner, even when finding solutions and goals becomes challenging within an all-in solution space. Critical Temperature, Critical Pressure, Saturated Pressure, and Liquid Density are important parameters for refrigerants such as R134a, R152a, R1234yf, R1234ze (E), and R1233zd (E). The results show that R1233zd (E) obtained the highest rank, while R1234yf received the lowest rank. According to the dataset, the Weighted Sum Method (WSM) indicates that R1233zd (E) is the best refrigerant, considering breathing rate and its top ranking.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.635

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 head0.009
GPT teacher head0.245
Teacher spread0.235 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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