2D Fuel Assembly Study for a Supercritical-Water Cooled Small Modular Reactor
Bibliographic record
Abstract
Burnable poisoning and fuel enrichment zoning are two techniques often combined in order to optimize the fuel assembly behaviour during the burnup cycle. In the present work, these two techniques will be applied to the 2D optimization of the fuel-assembly conceptual design for the supercritical water-cooled reactor developed in the framework of the Joint European Canadian Chinese development of Small Modular Reactor Technology project, funded within the Euratom Research and Training programme 2019-2020. The initial configuration of the fuel assembly does not include any burnable absorbers and uses a homogeneous fuel enrichment of 7.5% in 235U. The infinite multiplication factor, k∞, starts from approximately 1.32 and drop, almost linearly, to 1.0 after a burnup of 40.0 MWd·kg−1. The uniform enrichment is, however, responsible for a pin-power peaking factor that with fresh fuel starts from 1.32 and reduces to 1.08 at the end of the burnup cycle. A simplified analytical model is developed to assess the effect of different lumped burnable absorbers on the time dependence of the assembly k∞. It is shown that using an adequate number of B4C rods, positioned in the outer wall of the fuel assembly, together with a suitable distribution of 6 different 235U enrichments, it allows for obtaining an assembly k∞ factor that starts from 1.11 at beginning of cycle and remains quite constant over a large fraction of the burnup cycle. Moreover, the pin-power peaking factor is reduced to 1.03 at beginning of cycle and keeps almost unchanged until the end of burnup cycle.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".