Comparison of Existing Supercritical Carbon Dioxide Heat Transfer Correlations for Horizontal and Vertical Bare Tubes
Bibliographic record
Abstract
This paper presents a thorough analysis of ability of various heat transfer correlations to predict wall temperatures and Heat Transfer Coefficients (HTCs) against experiments on internal forced-convective heat transfer to supercritical carbon dioxide conducted by Koppel [1], He [2], Kim [3] and Bae [4]. It should be noted the Koppel dataset was taken from a paper which used the Koppel data but was not written by Koppel. All experiments were completed in bare tubes with diameters from 0.948 mm to 9 mm for horizontal and vertical configurations. The datasets contain a total of 1573 wall temperature points with pressures ranging from 7.58 to 9.59 MPa, mass fluxes of 400 to 1641 kg/m2s and heat fluxes from 20 to 225 kW/m2. The main objective of the study was to compare several correlations and select the best of them in predicting HTC and wall temperature values for supercritical carbon dioxide. This study will be beneficial for analyzing heat exchangers involving supercritical carbon dioxide, and for verifying scaling parameters between CO2 and other fluids. In addition, supercritical carbon dioxide’s use as a modeling fluid is necessary as the costs of experiments are lower than supercritical water. The datasets were compiled and calculations were performed to find HTCs and wall and bulk-fluid temperatures using existing correlations. Calculated results were compared with the experimental ones. The correlations used were Mokry et al. [5], Swenson et al. [6] and a set of new correlations presented in Gutpa et al. [7]. Statistical error calculations were performed are presented in the paper.
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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) | 0.001 | 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".