Methodology and preliminary verification of generating heterogeneous multigroup microscopic cross-section libraries for neutron transport codes based on OpenMC
Bibliographic record
Abstract
Advancements in reactor technology, particularly Generation IV and modular reactors, have introduced new challenges on the neutronics analysis due to their complex geometries and spectra. This study addresses these challenges by developing a methodology to generate heterogeneous multigroup microscopic cross-section libraries for three-dimensional neutron transport calculations using the OpenMC Monte Carlo code. The approach involves two-dimensional transport calculations in OpenMC for various fuel pins or supercells, generating multigroup microscopic cross-section libraries for isotopes relevant to burnup, temperature, and moderator density. These cross-sections are then post-processed and used in three-dimensional core neutron transport calculations with the CRANE deterministic code. This method combines the high accuracy of Monte Carlo methods with the computational efficiency of deterministic approaches. Preliminary 2D verification was conducted using benchmark problems, including PWR fuel assemblies from the VERA series, a fast reactor pin, a 3600 MWth subassembly, and a 1000 MWth metallic fuel core. Results indicate that the coupled OpenMC/CRANE method accurately captures reactivity and isotopic evolution during burnup, suggesting potential improvements in accuracy and efficiency for neutronic simulations of advanced reactor designs. • Developed a method for generating heterogeneous multigroup microscopic cross-section libraries. • Combined Monte Carlo accuracy with deterministic efficiency. • Conducted preliminary 2D verification with PWR and fast reactor benchmark problems. • Achieved accurate reactivity, isotopic evolution, and power distribution predictions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".