Sensitivity Analysis of CFD Code FLUENT-12 for Supercritical Water Heat Transfer in Vertical Bare Tubes
Bibliographic record
Abstract
The ability to use FLUENT 12 or other CFD software to accurately model supercritical water flow through various geometries in diabatic conditions is integral to research involving coal-fired power plants as well as Supercritical Water-cooled Reactors (SCWR). The cost and risk associated with constructing supercritical water test loops are far too great to use in a university setting. Previous work has shown that FLUENT 12, specifically realizable k-ε model, can reasonably predict the bulk and wall temperature distributions of externally heated vertical bare tubes for cases with relatively low heat and mass fluxes. However, sizeable errors were observed for other cases, often those which involved large heat fluxes that produce deteriorated heat transfer (DHT) regimes, which involves a reduction in the rate of heat transfer between the wall and the fluid, and therefore a rise in wall temperature. These errors were believed to be caused by FLUENT’s over-estimation of changes in thermal physical properties of the fluid which occur around the pseudocritical point. The goal of this research is to gain a more complete understanding of how FLUENT 12 models supercritical water cases and where errors can be expected to occur. One control case is selected where expected changes in bulk and wall temperatures occur and they match empirical correlations’ predictions, and the operating parameters are varied individually to gauge their effect on FLUENT’s solution. The model used is the realizable k-ε, and the parameters altered are inlet pressure, mass flux, heat flux, and inlet temperature. As a result of this work, under supercritical conditions, the only finite limitations found in the k-ε model are in the mass flux which is predicted correctly above 300 kg/m2s, and in the temperature and related physical properties of water where they exceed the limitation of the fluid properties in the fluid database.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".