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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 ≤ R blanket ≤ 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)O 2 , with various mixtures of Th and DU, and refuelled periodically online. Although using blankets made of pure DUO 2 or ThO 2 are viable options to analyze, mixing DUO 2 with ThO 2 can help alleviate any potential proliferation concerns, since any 233 U produced from breeding will be denatured by the presence of 238 U in (DU, Th)O 2 . 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 and 232 U in the irradiated fuel will be <0.01 wt%/initial heavy metal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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 it