Study on Specifics of Forced-Convective Heat Transfer in Supercritical Carbon Dioxide
Bibliographic record
Abstract
The appropriate description of heat-transfer to coolants at supercritical state is limited by the current understanding. Thus, poses one of the main challenges in development of supercritical-fluids applications for the Generation–IV reactors. The objective of the paper is, therefore, to discuss the basis for comparison of relatively recent experimental data on supercritical carbon dioxide (CO2) obtained at facilities of the Korea Atomic Energy Research Institute (KAERI) and Chalk River Laboratories (CRL) of 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 is conducted. It is shown that rarely published data on instrumental errors tend to underpredict significantly actual experimental errors. A revised heat-transfer correlation for the CRL data is presented. A preliminary heat-transfer correlation for joint CRL and KAERI datasets is developed.
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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".