Automated Doppler Broadening of Cross Sections for Neutron Transport Applications
Bibliographic record
Abstract
This dissertation discusses the research and development of new cross section temperature handling techniques for the SCALE computer code, which is developed and maintained at Oak Ridge National Laboratory. In particular, methods will be added to the KENO Monte Carlo code. Areas of interest include: neutron scattering off of heavy isotopes in the epithermal energy range, implementation of Doppler pre-broadening of continuous energy onedimensional cross-section data, implementation of interpolation on the continuous energy two-dimensional cross sections, and implementation of the direct S(α, β) [S alpha beta] method for thermal neutron scattering and interpolation on that data. Accurate cross section scattering off of heavy isotopes is crucial for accurate neutronics modeling and simulation of actual reactor cores during operational conditions. Scattering off of heavy isotopes such as U [Uranium 238] will have a greater impact on core neutronics behavior as reactor temperatures increase. Ensuring that the cross-section data used by Monte Carlo codes is obtained at the correct temperature will allow more accurate modeling of operating reactors that do not operate at current cross-section library temperatures. A review of the current approaches used for scattering and Doppler broadening was performed, and the current limitations of these approaches were examined. By examining the limitations of current approaches, new methods were developed and implemented into KENO. First, the Doppler Broadened Rejection Correction algorithm was added to KENO to allow for more accurate scattering off of heavy
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".