Variation of Burnable Neutron Absorbers in a Heavy Water–Moderated Fuel Lattice: A Potential to Improve CANDU Reactor Operating Margins
Bibliographic record
Abstract
A CANDU lattice cell has been modeled using the Los Alamos National Laboratory’s MCNP 6 code and Atomic Energy of Canada Limited’s WIMS-AECL 3.1. Models for the CANDU 37-element fuel bundle have included a CANLUB coating, as a carrier for the neutron absorbers. The objective is to improve CANDU reactor operating margins by adding small amounts (~1 g) of neutron absorbers to each fuel element.For CANDU natural uranium fuel bundle design, the results indicate that (a) the fueling transient (due to the xenon-free effect) could be significantly reduced using gadolinium oxide (Gd2O3), with no significant impact on fuel burnup, and (b) the reactivity peak (due to plutonium production) could be reduced using europium oxide (Eu2O3), with minimal impact on fuel burnup. An appropriate mixture of Gd2O3 and Eu2O3 that will improve operation and safety margins while having a minimal impact on fuel burnup is determined.Reactivity and power calculations for various mixtures of Gd2O3 and Eu2O3 are reported here. It is concluded that ~180 mg Gd2O3 and ~1000 mg Eu2O3 (~4.9 ×10−3 wt% per bundle) are sufficient to suppress the refueling transient and lower the axial plutonium peak, with a 0.27% burnup penalty (which is a small impact).Fuel safety and performance are always important topics for a nuclear utility. This approach of a relatively simple application of burnable poisons to existing CANDU natural uranium fuel design offers the benefits of improving fuel utilization and safety margins.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".