Refueling Considerations for <sup>99</sup> Mo Production in a CANDU Reactor
Bibliographic record
Abstract
Operating CANDU reactors have the potential to produce significant quantities of molybdenum-99 (99Mo) because of their ability to be refueled online, high thermal neutron flux, and fuel design flexibility. A new molybdenum-producing fuel bundle (MPB), previously designed for CANDU reactors, has as its principal attribute that it is neutronically and thermal hydraulically equivalent to the standard 37-element fuel bundle typically used in CANDU reactors. Given that the typical irradiation time for MPBs is 20 days while the typical refueling period for a channel is on average 6 months, the refueling strategy needs to be adjusted to accommodate the shorter irradiation time of MPBs.This study evaluates a new refueling strategy suitable for employing the new MPBs in the core. A full-core, three-dimensional model is constructed in the diffusion code DONJON, and a fueling strategy for achieving the desired weekly yield of 99Mo is developed. The adequacy of the proposed refueling scheme is evaluated using a series of time-average calculations, which show that a small increase in the core reactivity (<0.4 mk) can be expected when irradiating a set of four MPBs in three different fuel channels in the inner region of the core. The small increase in the core reactivity can be managed by slightly increasing the discharge burnup in the non-MPB-bearing fuel channels, thus also improving slightly the fuel utilization in the reactor.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".