Effect of Lattice-Level Homogenization and Group Condensation on Calculated Kinetics Parameters of a Natural-Uranium-Fueled Equilibrium CANDU Core
Bibliographic record
Abstract
Modern analysis of nuclear reactor transients uses space-time reactor kinetics methods. In the Canadian nuclear industry, safety analysis calculations use almost exclusively the improved quasi-static (IQS) flux factorization method. The IQS method, like all methods based on flux factorization, relies on calculating effective point-kinetics parameters, which dominate the time behavior of the flux, using adjoint-weighted integrals. The accuracy of the adjoint representation influences the accuracy of the effective kinetics parameters.Routine full-core calculations are not performed using detailed models and transport theory, but rather using a cell-homogenized model and two-group diffusion theory. This work evaluates the effect of homogenization and group condensation on the calculated effective kinetics parameters of an equilibrium CANDU core.Results show that homogenization combined with group condensation introduces a positive bias of ˜5% in the effective delayed neutron fraction over a wide range of discharge burnups. Homogenization alone induces a positive bias of only ˜2%.The bias in the effective generation time is <1% for all studied discharge burnups, and its effect on the results of a positive-reactivity transient is found to be negligible, with differences being caused solely by the effective delayed neutron fraction bias. The fractional delayed neutron fraction bias for the equilibrium core is found to be very close to that for a fresh-fuel core. However, because of the lower effective delayed neutron fraction of the equilibrium core, the effects of the bias are larger for the equilibrium core than for the fresh-fuel core. For a sample positive-reactivity transient, the maximum power is found to be underestimated by 9% for the fresh core and by 14% for the equilibrium core as a consequence of homogenization and group condensation.
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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 | 0.002 |
| 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) | 0.001 | 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".