Study on Specifics of Forced-Convective Heat Transfer in Supercritical Carbon Dioxide
Bibliographic record
Abstract
The appropriate description of heat transfer to coolants at the supercritical state is limited by the current understanding. Thus, this poses one of the main challenges in the development of supercritical-fluids applications for Generation-IV reactors. Since the thermodynamic critical point of water is much higher than that of carbon dioxide (CO2), it is more affordable to run heat-transfer experiments in supercritical CO2. The data for supercritical CO2 can be later scaled and used for supercritical water-based reactor designs. The objective of this paper is, therefore, to discuss the basis for comparison of relatively recent experimental data on supercritical CO2 obtained at the facilities of the Korea Atomic Energy Research Institute (KAERI) and Chalk River Laboratories (CRL) of the Atomic Energy of Canada Limited (AECL). Based on the available instrumental error, a thorough analysis of experimental errors in wall- and bulk-fluid temperatures and heat transfer coefficient was conducted. A revised heat-transfer correlation for the CRL data is presented. A dimensional criterion for the onset of the deteriorated heat transfer in the form of a linear relation between heat flux and mass flux is proposed. A preliminary heat-transfer correlation for the joint CRL and KAERI datasets is presented.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".