Effect of Clad Fast Neutron Flux Distribution on Quarter-Core Fuel Performance Calculations with BISON
Bibliographic record
Abstract
As part of the Consortium for Advanced Simulation of Light Water Reactors (CASL), the Virtual Environment for Reactor Applications (VERA) is being developed to provide high-fidelity multiphysics simulations of nuclear reactor cores. Over the past year, a one-way coupling capability has been developed, taking power and temperature data from MPACT/CTF calculations and running standalone BISON fuel rod cases. This has allowed CASL contributors to gain insight into the fuel performance characteristics while a more tightly coupled methodology between MPACT, CTF, and BISON has been under development. Many of the initial analyses have focused on the Watts Bar Unit 1 core using a fast flux factor—the default approach—which defines the clad fast flux as a linear function of the local linear heat rate. While this approach is more valid when considering single rod cases in isolation, in larger problems such as full-sized reactors, the local linear heat rate does not directly correlate to the clad fast neutron flux. For example, variations in assembly enrichment can lead to larger neutron fluxes in neighboring assemblies that may be at lower enrichment and power. Similarly, considering axial effects, spacer grids suppress power locally, but the fast neutron flux is significantly less affected, and the approach can lead to an underestimation of the fast neutron flux at that time. A poor estimation of the clad fast flux values can affect the clad creep rate, fuel-clad gap, and clad stresses. Additionally, the fast flux factor has been considered constant throughout the life of the rod. As the cycle progresses, the spectrum will harden as burnable absorbers and 235U deplete, so the fast flux would be expected to increase as the spectrum hardens. The effect on quantities of interest are highlighted for the first cycle of the Watts Bar Unit 1 core, comparing the results from an explicit clad fast flux representation from MPACT to the constant fast flux factor approach used in previous analyses. Future work will consider multicycle effects to assess the impact at higher burnups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".