Performance evaluation of a new signal processing system design to improve CANDU SDS1 trip response during large break LOCA events
Bibliographic record
Abstract
Performance of a recently developed signal processing system for CANDU (Canada Deuterium Uraniu) reactor shutdown system 1 (SDS1) is evaluated in this paper. The evaluation is carried out in MATLAB/Simulink software environment as well as with an existing power measurement and signal processing system. The new signal processing algorithm is obtained based on the synthesis of several first order low pass filters with different delayed time constants. Throughout this paper, a special attention has been paid to compare the new signal processing system with the existing one. The dynamic behavior of the new signal processing system in the practical large loss of coolant accidents (LLOCA) events has also been examined. Simulation results show that during the LLOCA event, the reactor trip time, as well as the peak power, is decreased remarkably. Through the simulation studies, it has convincingly demonstrated that the new signal processing system has significant advantages over the existing system in terms of the improved trip response and accommodation of the spurious trip immunity. This advantage will significantly enhance the safety margin, or will bring economical benefits to nuclear power plants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".