Multivariate Image Analysis for Core Monitoring in PWRs
Bibliographic record
Abstract
Abstract In pressurized water reactor (PWR) it is crucial for the operator to monitor the reactor parameters at the same time, such as temperature, pressure, boron concentration, control rod position, coolant density, etc., in order to make proper decision. However, the huge size of data reading from the different instrumentations, in addition to the limited human ability to visually detect, interpret, assess add a lot of uncertainty to the operator qualitative and quantitative analysis of the reactor performance. Therefore, this paper proposes the utilization of radial thermal flux maps (Neutron Images) technique, positioned on the reactor core as a sensitive monitoring technique for all changes of the reactor parameters as a result of the position of the control rods changing. Hence, the features contained in these neutron images are extracted (Multivariate image analysis and regression) via Principal Component Analysis (PCA), and Cluster Analysis (Dendrogram). To determine the effectiveness of the suggested technique in determining the location of the control-rods, several simulations are run. The 3D TRITON FORTRAN-code was utilized to simulate the radial thermal neutron flux of the Westinghouse 2775-MWth PWR benchmark at 100% thermal power generation. The SIMCA software programme is used to develop, test, and generalise the PCA model. Additionally, clustering analysis (CA) is carried out using the statistics software programme Minitab in order to demonstrate the effectiveness of the suggested method.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".