SAINT: A Computational Framework for Time-Dependent Neutron-Transport
Bibliographic record
Abstract
SAINT (Space-Time Analysis with Implicit Neutron Transport) is a newly developed general computational framework for high-fidelity, multigroup space-time kinetics analyses. It is based on using an implicit time-discretization scheme to reduce the time-dependent neutron transport equation to a series of time-independent, fixed-source transport problems that can be solved directly by any static neutron transport solver without the need for code modifications. SAINT can thus be used with any angle and spatial discretization approach. Results presented here use the collision-probability method as implemented by the transport code DRAGON. To demonstrate its functionality and to verify its results, SAINT is applied to the two-dimensional cases of Phase I, the kinetics phase, of the OECD/NEA C5G7-TD benchmark. The test cases consist of control-rod insertions and moderator voiding in four fuel assemblies of a miniature light-water reactor with each assembly consisting of a 37×37 arrangement of fuel pins. The transient total core fission rates for the various test cases are found to be within +2% of the values available from the MPACT transport code. During maximum rod insertion and moderator voiding, the distribution in the fission rates of the individual fuel pins (normalized to steady state) are found to vary by as much as 18% from their steady-state values for the rod-insertion cases and by 28% for the moderator-voiding cases.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".