Heat Transfer Correlation for Supercritical Carbon Dioxide Flowing in Vertical Bare Tubes
Bibliographic record
Abstract
Canada among many other countries is in pursuit of developing next generation (Generation IV) nuclear-reactor concepts. One of the main objectives of Generation-IV concepts is to achieve high thermal efficiencies (45–50%). It has been proposed to make use of SuperCritical Fluids (SCFs) as the heat-transfer medium in such Gen IV reactor design concepts such as SuperCritical Water-cooled Reactor (SCWR). An important aspect towards development of SCF applications in novel Gen IV Nuclear Power Plant (NPP) designs is to understand the thermodynamic behavior and prediction of Heat Transfer Coefficients (HTCs) at supercritical (SC) conditions. To calculate forced convection HTCs for simple geometries, a number of empirical 1-D correlations have been proposed using dimensional analysis. These 1-D HTC correlations are developed by applying data-fitting techniques to a model equation with dimensionless terms and can be used for rudimentary calculations. Using similar statistical techniques three correlations were proposed by Gupta et al. [1] for Heat Transfer (HT) in SCCO2. These SCCO2 correlations were developed at the University of Ontario Institute of Technology (Canada) by using a large set of experimental SCCO2 data (∼4,000 data-points) obtained at the Chalk River Laboratories (CRL) AECL. These correlations predict HTC values with an accuracy of ±30% and wall temperatures with an accuracy of ±20% for the analyzed dataset. Since these correlations were developed using data from a single source - CRL (AECL), they can be limited in their range of applicability. To investigate the tangible applicability of these SCCO2 correlations it was imperative to perform a thorough error analysis by checking their results against a set of independent SCCO2 tube data. In this paper SCCO2 data are compiled from various sources and within various experimental flow conditions. HTC and wall-temperature values for these data points are calculated using updated correlations presented in [1] and compared to the experimental values. Error analysis is then shown for these datasets to obtain a sense of the applicability of these updated SCCO2 correlations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".