Computational Fluid Dynamics Modeling of a Pressurized Water Reactor Fuel Assembly to Estimate Loss Coefficients in Support of Subchannel Thermalhydraulics Modeling of Pressurized Water Reactor Small Modular Reactors With Advanced Fuels
Bibliographic record
Abstract
Abstract Pressure loss coefficients are generally required by subchannel and system thermalhydraulics codes. These coefficients are not readily available for small modular reactors (SMRs) featuring nonconventional designs and novel coolants. In this study, the pressure loss coefficients were obtained using three-dimensional (3D) computational fluid dynamics (CFD) modeling for an advanced water-cooled reactor. A representative light water fuel assembly used in the Organization for Economic Co-operation and Development (OECD)/National Research Council Canada (NRC) pressurized water reactor subchannel and bundle tests (PSBT) benchmark was selected for CFD modeling and simulation under various working conditions. The fuel assembly includes three types of pressurized water reactor (PWR) spacer grids: simple grid (SG), nonmixing vane grid (NMVG), and mixing vane grid (MVG). Turbulent flow through subchannels of both nonheated and heated rod bundles was simulated to predict recoverable and nonrecoverable pressure distribution along the length of the bundle. It was observed that vortices were generated at the tips of spacer grids, affecting the cross-flow in subchannels significantly. The estimated pressure loss coefficients were found to be influenced by the flow conditions (Reynolds number or the upstream flow history) and spacer grid configuration. Pressure loss coefficient values ranged from 1.14 to 1.80, depending on the spacer grid type, design, and flow conditions. The CFD method used in this study was demonstrated to have the potential to generate input parameters required for the subchannel analysis and optimization of fuel assembly designs and serve as a surrogate for empirical correlations.
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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) | 0.000 | 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 | 0.000 |
| 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".