NEW REFERENCE APOLLO3® CALCULATION SCHEME FOR LIGHT WATER REACTORS – ANALYSIS OF THE BEAVRS BENCHMARK
Bibliographic record
Abstract
In the past few years, developments in the APOLLO3® deterministic code have mainly been devoted to Fast Reactor applications. In this paper, we investigate the possibility of using some of these methods to build an accurate two-step calculation scheme for commercial Pressurized Water Reactors, with application to the BEAVRS benchmark at hot zero power conditions of cycle 1. Our objective is to assess the performances of the best “standard” calculation currently possible with APOLLO3® and to have a starting point for the development of improved transport solvers and innovative calculation schemes. At the lattice level, we show that the subgroup method using the REL383 energy mesh, associated with a MOC flux calculation, provides accurate results on different clusters of 3x3 cells with UOX and MOX fuel, including a heterogeneity at the center (guide-tube full of water or with common absorbers Ag-In-Cd or B4C inserted, and mixed uranium-gadolinium oxide fuel). These good results have been confirmed on BEAVRS assembly, rods in and rods out. At the core level, 20-group 3D calculations with the MINARET Sn solver have been performed at the cell level to analyze BEAVRS Hot Zero Power results (reactivity, power map, and control rods worths). Results are rather satisfactory, considering the low computing cost, but the power map prediction needs to be improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".