Numerical analysis of permafrost heat transfer for small module reactor installation in northern areas
Bibliographic record
Abstract
Permafrost degradation amplified by climate change is one of the key issues to consider when attempting to install a small modular reactor (SMR) in remote towns and communities of northern Canada. If the thermal disturbance of permafrost occurs, the ground's strength may be significantly reduced, resulting in structural settlement and stability problems. Therefore, when constructing an SMR on permafrost soils or bedrocks, local permafrost conditions must be protected around the foundations. In the present work, a permafrost heat transfer model has been developed, including the mechanisms of the transient heat conduction, convection and phase change between the solid (ice) and liquid (water) in a porous medium (subsurface soil or sand), to predict the ground temperature variation with depth. The model was assessed by comparing against the available analytical solutions, and then applied to an underground SMR structure (using the Russian-ELENA design) and a civic building foundation (Igloo Church in Inuvik) to forecast the influence of construction heating and seasonal change on the thawing fronts, especially below the structure foundations. The climate data of Inuvik, Northwest Territories, Canada was used as typical weather conditions of northern areas. The numerical results concluded that there is no significant difference of the thawing front penerations adjacent to the underground SMR structure between the summer and winter times, except the active layer. This study will help to ensure the long-term performance of SMR structures under changing environmental conditions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".