Correlations of Flow Boiling Heat Transfer of R-134a in Minichannels: Comparative Study
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".