ICONE19-43492 STUDY OF SELECTED TURBULENT MODELS FOR SUPERCRITICAL WATER HEAT TRANSFER IN VERTICAL BARE TUBES USING CFD CODE FLUENT-12
Bibliographic record
Abstract
Many of the available empirical correlations existing today cannot predict closely the effects of the heat transfer phenomena within the pseudocritical region, and some do not predict well enough heat transfer coefficients even outside of this region. In this study, the Computational Fluid Dynamics (CFD) code FLUENT-12 is used with associated software such as Gambit and NIST REFPROP to predict the Heat Transfer Coefficient and corresponding wall temperature profiles inside circular tubes cooled with SuperCritical Water (SCW), and to compare them with experimental data and various empirical correlations. In this paper, a numerical study of heat transfer to SCWflowing upwards in vertical bare tubes using the CFD-code FLUENT-12 is presented for comparison to 1-D models. A large dataset was collected within conditions similar to those of proposed SuperCritical Water-cooled Reactors (SCWRs) at the Institute for Physics and Power Engineering in Obninsk, Russia. This dataset includes 80 runs in a 4-m long, 10-mm ID vertical bare tube within a wide range of operating parameters including pressure at about 24 MPa, inlet temperatures from 320 to 350℃, mass flux ranges from 200 to 1500 kg/m^2s and heat fluxes up to 1250 kW/^m2. Wall and bulk-fluid temperatures measured along the 4-m heated length test section were below, at, or above the pseudocritical point. Further analysis of the individual heat-transfer regimes was conducted using an axisymmetric 2-D model of a tube with 10,000 nodes along the heated length. Wall temperatures and heat transfer coefficients were analysed for 1-m sections at a time to select the best model for each region (below, within and beyond the pseudocritical region), and to neutralize effects of the rest of the tube on that region. Two turbulent models were used in the process: k-ε and k-ω, with many variations in the sub-model parameters such as viscous heating, thermal effects, and low-Reynolds number correction. The results show a good fit within the most low/mid range operating conditions with noticeable deviations within the high range, primarily at the deteriorated heat-transfer regime with an overall better fit for the k-ε model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".