A Study of Superhomogenization Applied to PHWR Lattices
Bibliographic record
Abstract
Abstract To reduce computational expenses, full-core production-type neutronics calculations are customarily performed using a simplified core-model whereby large regions of the core, called nodes, are assumed to be homogeneous. The process of generating the few-group homogenized-node macroscopic cross sections is called lattice homogenization. The simplest homogenization method is standard homogenization (SH) and full-core models based on it do not usually reproduce heterogeneous-core calculations too closely. To improve agreement between node-homogenized core results and heterogeneous-core results, advanced homogenization techniques are used. Such techniques tend to use additional parameters besides homogenized macroscopic cross sections. Superhomogenization (SPH) is an advanced lattice homogenization method, which has been developed initially for light-water-reactor (LWR) lattices whereby fuel elements are arranged in a rectangular array. It has the advantage of not requiring any modification to the full-core diffusion code for its implementation. For LWRs, SPH establishes neutronic equivalence between detailed-geometry heterogeneous fuel-pin cells and homogenized fuel-pin cells by adjusting homogenized multigroup macroscopic cross sections and diffusion coefficients. This work investigates the possible use of the SPH methodology for pressurized heavy-water reactor (PHWR) lattices whose fuel pins are arranged in concentric rings rather than in a rectangular array. Results for single-node (SN) as well as multinode (MN) lattice-calculation models are presented. Results show that, with proper region definition, the SPH methodology can be used for PHWR lattices but that improvement in homogenization accuracy is only marginal compared with SH when comparing results for the same type of lattice model (SN or MN).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".