Preliminary Investigation of Heat-Transfer Correlation for Upward Flow of CO2 at Supercritical Pressure
Bibliographic record
Abstract
The development of Generation IV nuclear reactors is an ongoing worldwide interdisciplinary effort. Canada is involved with the development of the SuperCritical Water-cooled Reactor (SCWR). One of the numerous engineering challenges associated with this development is to ensure intensive and stable heat transfer to SuperCritical Water (SCW) in a core. It is very important to develop sophisticated theoretical models to improving understanding of physical processes behind forced-convective heat transfer and its deterioration in near-critical region. However, not all turbulent models implemented in Computational Fluid Dynamics (CFD) codes are applicable to heat transfer at supercritical pressures. Additionally these codes should be first tuned on the basis of experimental data and, after that, used in similar calculations. Therefore, there is still a great reliance on 1D heat-transfer correlations for preliminary calculations. Performing experiments in SCW is extremely expensive due to the high temperatures and pressures involved. Therefore, it is reasonable to study the general properties of SuperCritical Fluids (SCF) by running experiments with modelling fluids; for example, carbon dioxide, which is widely used as a modeling fluid. In view of this, there is a need to compile a large database that will include experimental data on forced-convection heat-transfer to SuperCritical (SC) CO2 within a wide range of operational parameters. To date, a part of this database has been produced. It contains the bulk-fluid and wall temperatures of CO2 flowing upward in a vertical bare tube. CO2 is at supercritical pressure and the bulk-fluid and wall temperatures are below or above the pseudocritical temperature at the inlet of the test-section. Therefore, the objectives of this paper are: 1) to propose a correlation in a standard non-dimensional form, which will generalize data within ±25%; 2) to compare this correlation with the most recent, as well as previous 1D correlations for SC CO2 and SCW (appropriate scaling is performed).
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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) | 0.001 | 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 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".