Simulation of dynamic response of Self-Powered-Inconel-Neutron-Detector lead cables using a semi-empirical model
Bibliographic record
Abstract
The work presented here was devoted to the modelling and simulation of the \ndynamic response of lead cables of Inconel self-powered neutron detectors in a CANDU \npower reactor. The main goal was to develop a semi-empirical dynamic model of the \nInconel lead-cables in Ontario Power Generation???s Darlington Nuclear Generation \nStation (NGS) able to simulate the lead cables??? response to arbitrary neutron-flux \ntransients. A secondary goal was to compare lead-cable dynamic characteristics \nevaluated in the Darlington reactor to lead-cable characteristics previously evaluated in \nAECL???s NRU reactor. \nA Simulink model of the lead cable was developed. The model???s parameters \nwere obtained by fitting simulation results to measured lead-cable signals acquired \nduring reactor shutdown. The functionality of the Simulink model was demonstrated for \narbitrary neutron flux transients and simulation results were found to agree within 1.2% \nwith measurements for reactor trip transients. At the same time, differences between the \ndynamic characteristics (e.g. prompt fraction) of lead cables in a power reactor \n(Darlington) and research reactor (NRU) were identified. A tentative explanation of \nthose differences was formulated. A comprehensive elucidation of the reasons for the \nobserved differences will have to be addressed by future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".