Multi-physics DONJON5 reactor models for improved fuel cycle simulation with CLASS
Bibliographic record
Abstract
This work investigates reactor model biases and their consequences in nuclear scenario simulations. Usually, the models for Pressurized Water Reactors are based on infinite 2D assembly depletion simulations, but recent work has shown the importance of 3D complete core simulation for uncertainty reduction. The consideration of a whole core leads to new reactor parameters in the simulations that may bring additional biases. The fuel temperature distribution is one of them, and previous work considered isothermal reactors, leading to probable uncertainties in spent fuel inventory at reactor discharge. To quantify those biases and their propagation in a full scenario simulation, new advanced reactor models have been developed, based on neutronics and thermal-hydraulics couplings at the core level performed with DONJON5. Results show that the plutonium isotopic quality of spent fuel is biased for an isothermal core, with values systematically higher than for multi-physics calculations. In order to propagate those discrepancies in fuel cycle simulations that involve plutonium recycling in PWR MOX fuels, the coupling between CLASS and DONJON was renewed in order to add new fuel parameters such as the fuel temperature in the core burn-up simulation. A new methodology for data interpolation from lattice calculation has been implemented that allows acceptable computational time for DONJON5 calculations that are done within the fuel cycle simulation performed by CLASS. Comparison between isothermal and multi-physics reactor models for advanced scenario simulations performed with CLASS shows that the isothermal hypothesis leads to biases up to 10% for plutonium inventory in the UOX spent fuel stockpile, comparable with biases associated with other reactor parameters such as the loading pattern.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".