High-efficiency deep geological repository system for spent nuclear fuel in Korea with optimized decay heat in a disposal canister and increased thermal limit of bentonite
Bibliographic record
Abstract
To use nuclear energy sustainably, spent nuclear fuel, classified as high-level radioactive waste and inevitably discharged after electricity generation by nuclear power plants, must be managed safely and isolated from the human environment. In Korea, the land area is limited and the amount of high-level radioactive waste, including spent nuclear fuels to be disposed, is relatively large. Thus, it is particularly necessary to maximize disposal efficiency. In this study, a high-efficiency deep geological repository concept was developed to enhance disposal efficiency. To this end, design strategies and requirements for a high-efficiency deep geological repository system were established, and engineered barrier modules with a disposal canister for pressurized water reactor (PWR)-type and pressurized heavy water reactor type Canada deuterium uranium (CANDU) plants were developed. Thermal and structural stability assessments were conducted for the repository system; it was confirmed that the system was suitable for the established strategies and requirements. In addition, the results of the nuclear safety assessment showed that the radiological safety of the new system met the Korean safety standards for disposal of high-level radioactive waste in terms of radiological dose. To evaluate disposal efficiency in terms of the disposal area, the layout of the developed disposal areas was assessed in terms of thermal limits. The estimated disposal areas were 2.51 km2 and 1.82 km2 (existing repository system: 4.57 km2) and the excavated host rock volumes were 2.7 Mm3 and 2.0 Mm3 (existing repository system: 4.5 Mm3) for thermal limits of 100 °C and 130 °C, respectively. These results indicated that the area and the excavated volume of the new repository system were reduced by 40–60% compared to the existing repository system. In addition, methods to further improve the efficiency were derived for the disposal area for deep geological disposal of spent nuclear fuel. The results of this study are expected to be useful in establishing a national high-level radioactive waste management policy, and for the design of a commercial deep geological repository system for spent nuclear fuels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".