Bibliographic record
Abstract
In nuclear reactors, transients may happen, for example at the time of refueling, rod withdrawals or insertions, and during reactor accidents. Transient behavior may result from changes either in materials or in the geometry of the reactor core components. An enhanced understanding of these time-dependent changes in reactors may improve the reactor operation and reduce the probability of accidents. In spite of the importance of understanding the conditions that lead to transients, there are not many time-dependent reactor simulation codes available. This study is focused on modeling the sub-critical reactivity measurements in the ZED- 2 reactor, using the recently developed G4STORK computer code. The ZED-2 experiment measures the sub-critical state resulting from a step-wise reduction of the moderator level. G4-STORK is a time-dependent Monte Carlo code for reactor neutronics calculations based on the GEANT4 toolkit. G4-STORK has the ability to follow the evolution of the neutron population in time, including delayed neutrons, and to model the resulting changes in material and geometric properties of a reactor. The keff values calculated by G4-STORK were compared with the experimental measurements and with MCNP results. The comparison shows significant discrepancies with both MCNP and the experimental measurements. These discrepancies are increasing as the reactor becomes increasingly subcritical (from ∼20 mk to ∼40 mk). The recently developed G4-STORK code is still at an early stage of its development, and needs to be improved further to be used for transient analyses of the reactors. Given the flexibility of G4-STORK, there are many opportunity to improve and extend this code.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".