Neutron Absorbers in CANDU Natural Uranium Fuel Bundles to Improve Operating Margins
Bibliographic record
Abstract
Safety margins are particularly tight in natural uranium-fuelled CANDU reactors which are refueled on-power. During on-power refueling, the insertion of xenon-free fresh fuel bundles into the reactor core affects the reactor’s excess reactivity in such a way that this could lead to temporary power derating. It is desirable from a fuel management perspective, and to maintain safety margins to eliminate this xenon-free effect and any other power ripples such as the subsequent plutonium reactivity peak. A redesign of the CANDU NU fuel bundle with an appropriate combination of elements, with some including neutron-absorbers, could well address the issue of the xenon-free initial portion of the bundle’s irradiation and also lower the plutonium-peak that occurs shortly thereafter. This may improve the fuel utilization (by further optimizing the fuelling strategy) and provide improved safety margins (by lowering the maximum channel and bundle powers). The use of neutron-absorbers in fuel design and manufacturing has been a regular practice in Light Water Reactor fuels for more than three decades. In CANDU applications, neutron absorbers have also been considered for the conceptual Advanced CANDU Reactor and the Low Void Reactivity fuel designs, for which the fissile content is made of low enriched uranium (LEU) or MOX fuels. The application to CANDU natural uranium (NU) fuel, however, especially as burnable poisons, is a relative novel approach. The reason for this is that the neutron economy in natural uranium-fuelled CANDU reactors is a prime concern, thus the addition of extra neutron absorbers is generally shunned. In our proposed application of burnable poisons to existing CANDU NU fuel design, because of low excess reactivity for NU fuel, the amount of neutron-absorber is expected to be restricted to small quantities and in a manner whereby the poison effect is restricted to the initial period of excess reactivity of a newly inserted fuel bundle. This implies that the impact on neutron economy would be relatively minimal, but the fuel performance would be significantly improved. Small amounts and appropriate mixtures of neutron absorbers were selected (approximately 500 mg of absorbers in a CANDU fuel bundle having a nominal weight of 24 kg). Preliminary results indicate that the fuelling transient and the subsequent reactivity peak can be lowered to improve the reactor’s operating margins. A parametric study using the Los Alamos National Laboratories’ MCNP 5 and Atomic Energy of Canada Limited’s WIMS-AECL 3.1 codes is presented in this paper. Details of this project and future work are also to be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".