Development and Comparison of a Heat-Transfer Correlation for Supercritical Refrigerants
Bibliographic record
Abstract
The objective of this paper is to act as a collection of multiple different heat-transfer correlations and to check their accuracy when compared to experimental data obtained in supercritical-pressure refrigerants (R-22 and R-134a). This paper is also intended to collect as much relevant data of heat transfer in supercritical refrigerants as possible for future research. The experimental data have been retrieved from graphs within a wide range of operating parameters. This study is in support of potential use of supercritical refrigerants as modeling fluids instead of supercritical water. The use of refrigerants as modelling fluids instead of water will allow to decrease costs and technical difficulties during experiments at supercritical pressures and widen operating ranges, because the critical parameters of refrigerants are significantly lower than those of water. The research was completed by collecting graphed data from several different experimental series using both R-22 and R-134a data. The advantage of comparing different refrigerants for determining correlation accuracy is to increase the predictability for other potential experiments using refrigerants. All data are taken from bare-tube experiments to produce a relative baseline for heat-transfer characteristics. These experiments have been performed within the following range: Inner tube diameter ranging between 4.4 mm to 13 mm, pressure ranging between 4.3 MPa to 5.5 MPa, and at a number of various mass and heat fluxes. Sixteen potential heat-transfer correlations have been selected and used in this assessment. The correlation by Watts and Chou [1] and Cheng et al. [2] were shown to have the lowest root-mean-square error. Other correlations with the reasonable accuracy include Mokry et al. [3] and Swenson et al. [4] correlations. However, it was decided to develop a new correlation based on these refrigerant data in an attempt to increase the prediction accuracy. Therefore, based on the Mokry et al. [3] correlation a modified correlation was developed, which generalized the experimental Freon data with higher accuracy than the know correlations. This correlation is intended to create a basis for further study on the use of refrigerants as modeling fluids. While Freon has similar properties to water at supercritical conditions, the different molecular properties causes factors to affect each fluid differently. For refrigerants at supercritical conditions, the factors that seem to have the most effect are the dynamic viscosity and density of a fluid.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".