Heterogeneous Cores for Implementation of Thorium-Based Fuels in Heavy Water Reactors
Bibliographic record
Abstract
New reactor concepts to implement thorium-based fuel cycles have been explored to achieve maximum resource utilization. Pressure tube heavy water reactors (PT-HWRs) are highly advantageous for implementing thorium-based fuels because of their high neutron economy and online refueling capability. The use of heterogeneous seed/blanket core concepts in a PT-HWR where higher-fissile-content seed fuel bundles are physically separate from lower-fissile-content blanket bundles allows more flexibility and control in fuel management to maximize fissile utilization (FU) and conversion of fertile fuel. The lattice concept chosen was a 35-element bundle made with a homogeneous mixture of reactor-grade PuO2 (~67 wt% fissile) and ThO2, with a central zirconia rod to reduce coolant void reactivity. Several annular and checkerboard-type heterogeneous seed/blanket core concepts with plutonium-thorium–based fuels in a 700-MW(electric)–class PT-HWR were analyzed, using a once-through thorium cycle. Different combinations of seed and blanket fuel were tested to determine the impact on core-average burnup, FU, power distributions, and other performance parameters. WIMS-AECL Version 3.1 was used to perform lattice physics calculations using two-dimensional, 89-group integral neutron transport theory, while RFSP Version 3.5.1 was used to perform the core physics and fuel management calculations using three-dimensional two-group diffusion theory. Among the different core concepts investigated, there were cores where the FU was up to 30% higher than that achieved in a PT-HWR using natural uranium fuel bundles. There were cores where up to 67% of the Pu was consumed, cores where up to 43% of the energy was produced from thorium, and cores where up to 363 kg/year of 233U was produced in the discharged fuel.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".