Analysis of Computational Fluid Dynamics Code FLUENT Capabilities for Supercritical Water Heat-Transfer Applications in Vertical Bare Tubes
Bibliographic record
Abstract
In this paper, the computational fluid dynamics (CFD) code FLUENT was used to predict wall-temperature profiles inside vertical bare tubes with supercritical water (SCW) as the cooling medium, to assess the capabilities of FLUENT for SCW heat-transfer applications. Numerical results are compared to experimental data and current one-dimensional (1D) models represented by existing heat-transfer empirical correlations. Wall-temperature and heat-transfer coefficients were analyzed to select the best model to describe the fluid flow before, at, and after the pseudocritical region. k−ϵ and k−ω turbulent models were evaluated in the process, with variations in the submodel parameters such as viscous heating, thermal effects, and low-Reynolds-number correction. Results of the analysis show a fit of ±10% for wall temperatures using the SST k−ω model within the deteriorated heat-transfer regime and less than ±5% within the normal heat-transfer regime. The accuracy of the model is higher than any empirical correlation tested in the mentioned regimes and provides additional information about the multidimensional effects between the bulk-fluid and wall temperatures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".