Assessment of FLUENT Code as a Tool for SCW Heat Transfer Analysis
Bibliographic record
Abstract
Generation-IV SuperCritical Water-cooled Reactors (SCWRs) are expected to have high thermal efficiencies within the range of 45–50% owing to the reactor’s high pressures and outlet temperatures. The behavior of supercritical water however, is not well understood and most of the methods available to predict the effects of the heat transfer phenomena within the pseudocritical region are based on empirical one-directional correlations which do not capture the multi-dimensional effects and do not provide accurate results in regions such as the deteriorated heat transfer regime. Computational Fluid Dynamics (CFD) is a numerical approach to model fluids in multidimensional space using the Navier-Stokes equations and databases of fluid properties to arrive at a full simulation of a fluid dynamics and heat transfer system. In this work, the CFD code, FLUENT-12, is used with associated software such as Gambit and NIST REFPROP to predict the Heat Transfer Coefficients at the wall and corresponding wall temperature profiles inside vertical bare tubes with SuperCritical Water (SCW) as the cooling medium. The numerical results are compared with experimental data and 1-D models represented by existing empirical correlations. Analysis of the individual heat-transfer regimes is conducted using an axisymmetric 2-D model of tubes of various lengths and composed of different nodalizations along the heated length. Wall temperatures and heat transfer coefficients were analyzed to select the best model for each region (below, at and above the pseudocritical region). Two turbulent models were used in the process: k-ε and k-ω, with variations in the sub-model parameters such as viscous heating, thermal effects, and low-Reynolds number correction. Results of the analysis show a fit of ±10% for the wall temperatures using the SST k-ω model in the deteriorated heat transfer regime and less than ±5% for 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".