Model for Predicting LISS Nozzle Proximity to CANDU Fuel Channels Using Eddy Current Measurements
Bibliographic record
Abstract
Horizontally oriented fuel channels (FCs) in CANadian Deuterium Uranium (CANDU) nuclear reactors are composed of an inner pressure tube (PT), surrounded by a larger diameter calandria tube (CT). A transmit-receive eddy current probe is used to measure gap between the two tubes from within the PT. Reactors also contain liquid injection shutdown system (LISS) nozzles, which are perpendicular to and beneath some rows of FCs. Due to fuel bundle weight and high operating temperatures (inlineFormula 2 -LpyC), FCs sag over their lifespan, approaching the LISS nozzles. In the case of LISS-CT contact, there is potential for CT damage. LISS nozzles that are close to FCs also compromise eddy current PT-CT gap measurements due to sensitivity of the probe to the additional conducting structure. Finite element method (FEM) software was used to model eddy current probe response from a nearby LISS nozzle. FEM simulation results, calibrated on only the gap response, were found to be in good agreement with previously collected experimental data. The FEM model was used to simulate a training dataset for a predictive LISS-PT algorithm based on a modified Gram-Schmidt multiple regression. This algorithm displayed excellent agreement with the same algorithm that was trained using experimental data, both having comparable root-mean-square error (RMSE) when applied to the same experimental validation data. The FEM model demonstrates the potential for examining historical in-reactor gap data, where calibrations that include the presence of a nearby LISS nozzle are not available, as well as providing cost savings by augmenting training data sets with simulated results.
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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".