Multi-physics DONJON5 reactor models for improved fuel cycle simulation with CLASS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it