TRANSIENT EXPERIMENTS IN ZED-2 TO INVESTIGATE THE IMPACT OF LEAKAGE ON REACTOR PHYSICS PHENOMENA
Bibliographic record
Abstract
This paper describes an experimental approach where reactor kinetics experiments are used to study reactor physics phenomena that are normally investigated using static-measurement techniques. This approach provides validation data relating to these phenomena for a range of core reactivities, rather than only providing data at critical conditions. Sub-critical and super-critical transient measurements were performed in the ZED-2 reactor. The transients were analyzed using a point kinetics model to derive the reactivity states that induced the transients. The reactor physics phenomenon of interest for the current study is Coolant Density Induced Reactivity. Initial measurements were performed using an air-cooled (i.e., voided) ZED-2 lattice; the measurements were then repeated using the same lattice cooled with light water. These measurements yielded reactivity values for both coolant conditions in the lattice for a range of super-critical and sub-critical states. This investigation avoids the inherent assumption of static-measurement analyses that the bias in predicting criticality for the two coolant conditions is identical to the bias in predicting the phenomenon of Coolant Density Induced Reactivity itself. The measured reactivity values are compared with calculations employing the 3-D stochastic neutron transport reactor code MCNP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".