Fueling Study of a CANDU Reactor Using Fuels Containing Burnable Neutron Absorbers
Bibliographic record
Abstract
Trace amounts of burnable neutron absorbers (BNAs) were used to tailor the reactivity of the 37-element, natural uranium (NU) fuel bundle used in CANDU reactors. The BNAs of interest included Gd2O3 and Eu2O3, which were added to the fuel in variable quantities and combinations. The fuel lattice was modeled using the WIMS–AECL 3.1 code, and core simulations were conducted using the Reactor Fuelling Simulation Program (RFSP). The fuel model assumes an equivalent and uniform distribution of BNAs in the CANLUB layer of each fuel element.The incorporation of BNAs is designed to improve CANDU reactor operating margins during on-power refueling by eliminating the fueling transient (FT) and reducing the magnitude of the plutonium peak (PP) that is characteristic of NU fuels. By adding an optimal combination of “fast-burning” and “slow-burning” BNAs, the FT and PP can be selectively reduced, and a significantly flatter trend in the burnup-dependent evolution of fuel reactivity can be achieved.The results of the study indicate that by adding ~150 mg [~8 parts per million (ppm)] of Gd2O3 and ~300 mg (~15 ppm) of Eu2O3 per fuel bundle, the best gain in the operating margins of a 2650-MW(thermal) (480-channel) model CANDU reactor can be achieved. Based on the simulation of refueling events, it was shown that the magnitude of average postrefueling channel power ripples can be reduced by an average of 100 kW and a maximum of 220 kW for powers observed immediately after refueling. This reduction in postrefueling powers was also shown to allow the average liquid zone controller level to decrease from ~48% to 10%. This decrease implies a potential relief on overpower protection (an operating margin).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".