Advanced Online Flux Mapping of CANDU PHWR by Least-Squares Method
Bibliographic record
Abstract
A least-squares method that solves both the core neutronics design equations and the in-core detector response equations on the least-squares principle is presented as a new advanced online flux-mapping method for CANada Deuterium Uranium (CANDU) pressurized heavy water reactors (PHWRs). The effectiveness of the new flux-mapping method is examined in terms of online flux-mapping calculations with numerically simulated true flux distribution and detector signals and those with the actual core-follow data for the Wolsong CANDU PHWRs in Korea. The effects of core neutronics models as well as the detector failures and uncertainties of measured detector signals on the effectiveness of the least-squares flux-mapping calculations are also examined.The following results are obtained. The least-squares method predicts the flux distribution in better agreement with the simulated true flux distribution than the standard core neutronics calculations by the finite difference method (FDM) computer code without using the detector signals. The adoption of the nonlinear nodal method based on the unified nodal method formulation instead of the FDM results in a significant improvement in prediction accuracy of the flux-mapping calculations. The detector signals estimated from the least-squares flux-mapping calculations are much closer to the measured detector signals than those from the flux synthesis method (FSM), the current online flux-mapping method for CANDU reactors. The effect of detector failures is relatively small so that the plant can tolerate up to 25% of detector failures without seriously affecting the plant operation. The detector signal uncertainties aggravate accuracy of the flux-mapping calculations, yet the effects of signal uncertainties of the order of 1% standard deviation can be tolerable without seriously degrading the prediction accuracy of the least-squares method. The least-squares method is disadvantageous because it requires longer CPU time than the existing FSM. Considering ever-increasing computer speed and the improved operational safety margin of CANDU reactors gained by accurate flux-mapping calculations, however, it is concluded that the least-squares method presents an effective alternative to the existing flux-mapping method for CANDU reactors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".