Computational Fluid Dynamic Simulations of Heat Transfer From a 2 × 2 Wire-Wrapped Fuel Rod Bundle to Supercritical Pressure Water
Bibliographic record
Abstract
Within the Generation-IV International Forum, Canadian Nuclear Laboratories (CNL) led the conceptual fuel bundle design effort for the Canadian supercritical water cooled reactor (SCWR). The proposed fuel rod assembly for the Canadian SCWR design comprised of 64-elements with spacing between elements maintained using the wire-wrap spacers. Experimental data and correlations are not available for the fuel-assembly concept of the Canadian SCWR. To analyze the thermalhydraulic performance of the new bundle design, CNL is using computational fluid dynamics (CFD) as well as the subchannel approach. Simulations of wire-wrapped bundles can benefit from the increased fidelity and resolution of a CFD approach due to its ability to resolve the boundary layer phenomena. Prior to the application, the CFD tool has been assessed against experimental heat transfer data obtained with bundle subassemblies to identify the appropriate turbulence model to use in the analyses. In the present paper, assessment of CFD predictions was made with the wire-wrapped bundle experiments performed at Xi'an Jiaotong University (XJTU) in China. A three-dimensional CFD study of the fluid flow and heat transfer at supercritical pressures for the rod-bundle geometries was performed with the key parameter being the fuel rod wall temperature. This investigation used Reynolds-averaged Navier–Stokes turbulence models with wall functions to investigate the behavior of flow through the wire-wrapped fuel rod bundles with water subjected to a supercritical pressure of 25 MPa. Along with the selection of turbulence models, CFD results were found to be dependent on the value of turbulent Prandtl number used in simulating the experimental test conditions for the wire-wrapped fuel rod configuration. It was found that the CFD simulation tends to overpredict the fuel wall temperature, and the predicted location of peak temperature differs from the measurement by up to 65 deg.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".