PERFORMANCE IMPROVEMENTS FOR THORIUM-BASED FUELS IN PRESSURE-TUBE HEAVY-WATER REACTORS
Bibliographic record
Abstract
Lattice physics sensitivity studies have been performed with WIMS-AECL to quantify the impact of various design and operating parameters on the performance characteristics of thorium-based fuel concepts in pressure-tube heavy-water reactors. Fuels modeled included 37-element bundles with natural uranium oxide (for comparison), pure thorium oxide (blanket-type fuel) and 35-element bundles of mixed oxide with thorium and U-233. Key performance parameters evaluated included the lattice reactivity, exit burnup, coolant void reactivity (CVR), and fissile concentration. The effects of various design/operational parameters were evaluated, including calandria tube radius, moderator purity, coolant purity, zirconium enrichment, and temporary out-of-core fuel storage at zero power. Results demonstrated that removing the moderator around the blanket fuel can harden the neutron energy spectrum and increase the discharge fissile content from ∼1 wt% U-233 to ∼2 wt% U-233 at a low discharge burnup (5 MWd/kg). Increasing the moderator purity beyond the nominal value (99.83 at% D2O) can improve the discharge burnup by 6%, while increasing the coolant purity beyond the nominal value (99.0 at% D2O) can reduce the CVR by up to 1 mk (100 pcm, 0.001 Δk/k). Increasing the enrichment of Zr-90 in zirconium to 100% for all of the zirconium alloy structural materials used in the lattice can increase the discharge burnup by nearly 40%, while reducing the CVR by as much as 1.6 mk. Temporary out-of-core storage of partially burned thorium-based fuel for a single refueling period (∼70–90 days) to allow Pa-233 to decay to U-233 before core re-insertion could increase the lattice reactivity by 71 mk (7100 pcm) and the discharge burnup by as much as 7%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".