Investigation of a SPH-based sub-cell homogenization for PHWR using a multi-cell model
Bibliographic record
Abstract
Superhomogenization (SPH) has gained interest in the industry as a possible method to overcome the inherent limitations of standard homogenization (SH) for full nuclearreactor-core neutronics calculations because its implementation does not require any changes to existing computer codes. Previous work found that single-cell SPH applied to Pressurized Heavy Water Reactors (PHWR) yields virtually no improvement compared to single-cell standard homogenization. This work attempts to improve those results by accounting for neutron leakage across cell boundaries by performing SPH-based homogenization using a 3??3 multi-cell model. The method is evaluated using a 5??5 lattice-cell model and comparing results for single-cell SH, multi-cell SH, single-cell SPH and multi-cell SPH. Results show that multi-cell SPH produces better results than single-cell SPH and multi-cell SH produces better results than single-cell SH. However, multi-cell SPH offers no improvement compared to multi-cell SH, just as single-cell SPH offers no improvement over single-cell SH.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".