Development of Local Heat Transfer Models for the Safety Assessment of Prismatic Modular High Temperature Gas-Cooled Reactor Cores
Bibliographic record
Abstract
This paper presents a model developed for determining fuel particle and fuel block temperatures of a prismatic core modular reactor during both normal operation and under fault conditions. The model is based on multi-scale modeling techniques and has been qualified by comparison with finite element simulations for both steady state and transient conditions. Further, a model for determining the effective conductivity of the block fuel elements — important for heat removal in loss of flow conditions — is presented and, again, qualified by comparison with finite element simulations. A numerical model for predicting conduction heat transfer both within and between block fuel elements has been developed which, when coupled with the above multi-scale model, allows simulations of whole cores to be carried out whilst retaining the ability to predict the temperatures of individual coolant channels and individual coated particles in the fuel if required. This ability to resolve heat transfer on length scales ranging from a few meters down to a few microns within the same model is very powerful and allows a complete assessment of the fuel and structural temperatures within a core to be made. More significantly, this level of resolution facilitates interactive coupling with neutronics models to enable the strong temperature/reactivity feedbacks, inherent in such cores, to be resolved correctly.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".