Bold application of sNDM to REA in a SSCR core with azimuthal mesh
Bibliographic record
Abstract
Previously applied to REA (Rod Ejection Accident) in several PWR-like cores, the minimalistic Nodal Drift Method (NDM) has recently been generalized to sNDM (super NDM). Both developed and validated on a heat-up transient of the KRUSTY experiment made of a few homogeneous parts, sNDM basically feeds the one-group diffusion approximation with so-called corrective Surface Factors (SF) for internodal currents from MCNP F1 tallies. In order to specify its practical usefulness for exploratory design studies, sNDM at its turn is put to the demanding test of REA in a PWR-like core. The chosen test case is a 600 MWth D2O/H2O-cooled thorium-fueled Spectral Shift Control Reactor (SSCR) core retrieved from previous studies, whose main results on conversion (by our MC-based tool SMURE) and safety (by NDM) are first summed up. Enhanced MCNP core models at HFP, CZP and HZP (respectively Hot Full, Cold Zero and Hot Zero Power) are detailed that have been specially adapted to a 2D radial-azimuthal mesh of few large nodes towards an even simpler REA calculation by sNDM. Other settings, necessary at BOT (Beginning Of Transient from HZP), include fuel and coolant thermal feedbacks as well as the global conductance of a lumped thermal model. Last but not least, the special cases of a few SF found variable between BOT and EOL (End Of Launch at t = 0.05 s) are addressed by an iterative transient calculation based on their linear interpolation. This method is proven effective at the cost of accepting an irreducible discrepancy for the radial exchange rate of the ejected node, provided that a proper so-called global way of computing all SF is used. Finally, main transient results are given until EOT equilibrium (End Of Transient at t = 300 s), with various sanity checks (including a partial safety one).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".