Development of a computational scheme based on the DRAGON5 code for the neutronic study of VVER-type reactor rods and assemblies
Bibliographic record
Abstract
Open source modeling of VVER-type reactors could become a medium-term objective in Eastern Europe. As the deterministic code DRAGON5 could meet such a need, we confronted DRAGON5 against a stochastic reference code, SERPENT2. Our validation comprises 7 cells and 4 assemblies from the Khmelnitsky-2 reactor in Ukraine, within a wide range of heterogeneity levels in fuel composition. Two calculation schemes have been developed and compared. The first, the ALAMOS scheme, is highly discretized in energy and spatial resolution, while the second, the REL2005-like scheme, is calculated in two levels (one highly discretized in energy and the other highly discretized in space). In the majority of cases studied, both schemes offer satisfactory accuracy (e.g. less than 300 pcm in k eff ), although there are difficulties related to energy deposition with gadolinium-poisoned fuel. While showing significantly poorer results than the ALAMOS scheme, the REL2005-like scheme offers lower computation times and major avenues for improvement remain to be explored. This work offers a first step towards the simulation of VVER-type reactors in DRAGON5, and paves the way for full-core simulations. • 7 cells and 4 assemblies of the Khmelnitsky-2 VVER have been simulated in DRAGON5. • At assembly level, two different calculation schemes have been developed. • DRAGON5 calculations were compared with SERPENT2 reference results. • The results obtained open the way to full-core simulation of VVER-type reactors.
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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 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".