ASSESSMENT OF FAST-SPECTRUM BLANKET LATTICES FOR BREEDING FISSILE FUEL FROM THORIUM AND DEPLETED URANIUM IN AN EXTERNALLY DRIVEN SUB-CRITICAL GAS-COOLED PRESSURE TUBE REACTOR
Bibliographic record
Abstract
To ensure long-term nuclear energy security, it is advantageous to consider the use of externally driven sub-critical systems for producing fissile fuel to supply fleets of thermal-spectrum reactors as an alternative to using fast-spectrum or thermal-spectrum breeder reactors. Computational/analytical neutronics and heat transfer studies have been carried out for gas-cooled fuel bundle lattices with mixtures of fertile thorium and depleted uranium (DU) that could be used in the blanket region of a sub-critical fast reactor driven either by a fusion reactor in a hybrid fusion-fission reactor (HFFR) system, or an accelerator-based spallation neutron source in an accelerator driven system (ADS). The HFFR or ADS concept envisioned is one with a simple cylindrical geometry. The annular-cylindrical blanket is approximately 10 m long, can be made 2–5 m thick (1.0 m ≤ Rblanket ≤ 3.0 m to 6.0 m), and is filled with a repeating square lattice of pressure tubes filled with 0.5 m long fuel bundles that are made of (DU,Th)O2, with various mixtures of Th and DU, and refuelled periodically online. Although using blankets made of pure DUO2or ThO2are viable options to analyze, mixing DUO2with ThO2can help alleviate any potential proliferation concerns, since any233U produced from breeding will be denatured by the presence of238U in (DU, Th)O2. Lattice calculations demonstrate that the total fissile content in the fuel after an extended period of burnup (50 MWd/kg) will be approximately the same, regardless of the mixture of DU and thorium used, and that the content of americium and232U in the irradiated fuel will be <0.01 wt%/initial heavy metal.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".