Development of a Heat Transfer Correlation for Supercritical CO2 Based on Multiple Data Sets
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Bibliographic record
Abstract
Currently, increase in thermodynamic efficiency of water-cooled Nuclear Power Plants (NPPs) can only be achieved by raising the coolant’s operating conditions above the supercritical point. The critical point of water is 22.06 MPa and 373.95°C, making supercritical water research very power-intensive and expensive. CO2 behaves in a similar manner once in the supercritical state, but at significantly lower pressure and temperature, since critical point of CO2 is 7.37 MPa and 30.98°C. The applications of supercritical CO2 research range from using it as a modelling fluid, to supercritical turbine applications in Liquid Metal Fast Breeder Reactors (LMFBRs), and use in a supercritical Brayton cycle. Therefore, it is of prime importance to model its behaviour as accurately as possible. For this purpose, experimental data of Koppel (1960), He (2005), Kim (2005) and Bae (2007) for CO2 were analyzed, and a new correlation was developed. The dataset consists of 1409 wall temperature points with pressures ranging from 7.58 to 9.58 MPa, mass fluxes from 419 to 1200 kg/m2s, and heat fluxes from 20 to 130 kW/m2. All runs take place in bare tubes of inner diameters from 0.948 to 9.00 mm in both vertical and horizontal configurations. The proposed correlation takes a wall-temperature approach to predicting the Nusselt number. This paper compares the new correlation with other work which has been done at the University of Ontario Institute of Technology by Mokry et al. (2009), as well as with correlations by Swenson et al. (1965) and Dittus-Boelter (1930). It was found that the new correlation has an overall RMS error of 13% for Heat Transfer Coefficient (HTC) values and 5% for calculated wall temperature values. The correlation can be used as a conservative approach to predict wall temperature values in Supercritical Water Reactor (SCWR) preliminary calculations, to predict heat transfer in secondary-loop turbine/ heat exchanger applications, as with the LMFBR, and to help validate scaling parameters used for water and other coolants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 it