Correlations of Flow Boiling Heat Transfer of R-134a in Minichannels: Comparative Study
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Bibliographic record
Abstract
R-134a is one of the most widely used refrigerants, and minichannel refrigeration systems with R-134a have rapidly developed in many fields, such as home, automobile and aircraft air conditioning systems, for high efficiency operations to save energy and space. A number of correlations for flow boiling hear transfer have been proposed. There is some literature to evaluate existing correlations for R-134a flow boiling heat transfer in minichannels. However, they were only based on the authors own experimental data. Therefore, results are often not consistent, even controversial. Our efforts are devoted to develop a better flow boiling heat transfer correlation for R-134a in minichannels, and this paper presents the first part of our efforts: A comparative study of existing correlations for flow boiling hear transfer of R-134a in minichannels. From 9 published papers, 1158 data points of flow boiling heat transfer of R-134a in minichannels are collected. Eighteen flow boiling heat transfer correlations, including almost all well-known ones, are reviewed and compared with the data collected. It is found that no correlation has satisfactory accuracy. The best one has a mean absolute relative deviation above 36%. It is interesting to note that among the six best correlations, one was developed for pool boiling and two were developed for conventional channels, and most of correlations developed specially for minichannels do not work quite well. More efforts should be made to better understand the mechanism of flow boiling heat transfer in minichannels for developing better correlations. Key words: R-134a; Flow boiling; Heat transfer; Correlation; Minichannel
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it