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Record W7105665079 · doi:10.17632/9dy7sxyh46

The experimental data of performance mapping of propane thermoelectric sub-cooler

2025· dataset· W7105665079 on OpenAlexaboutno aff

Bibliographic record

VenueMendeley Data · 2025
Typedataset
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHeat exchangerCoefficient of performanceSubcoolingRefrigerationThermoelectric coolingThermoelectric effectCooling capacityThermoelectric generatorThermal

Abstract

fetched live from OpenAlex

As responsible for 7.8% of global greenhouse emissions, the heat pump and refrigeration sectors are important in achieving sustainable development goals [1]. To reduce emission levels, traditional environmentally harmful working fluids that cause ozone depletion, which are listed in the Montreal Protocol [2], are being phased out. Furthermore, energy efficiency studies were conducted to improve the performance of the standard cycle. One way to increase the performance of the cycle is to use subcooling methods, that is, further cooling the working fluid in the condenser/gas-cooler [3]. Among the alternatives, the thermoelectric subcooling method is promising because of easy implementation, serving both in heating and cooling with thermoelectric modules (TEM), and adaptability to different capacities. The main aim of this study is to create a performance mapping of a thermoelectric subcooler (TESC) under different operating conditions and generate comparative results of two different manufacturing methods, conventional drilling and welding, and 3-D printing, that were used on the R290 heat exchanger of the TESC. According to the results, it was shown that the 3-D printed R290 heat exchanger was superior to a conventionally manufactured R290 heat exchanger in terms of thermal performance. Results showed that 3-D printed TESC can provide 270 W of additional cooling capacity with a cooling coefficient of performance of 1.22, while simultaneously providing 490 W of heating capacity with a heating coefficient of performance of 2.22.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesMeta-epidemiology (narrow), Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0000.003
Open science0.0470.044
Research integrity0.0010.002
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 head0.100
GPT teacher head0.328
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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Same venueMendeley DataFrench-language works237,207