Portable Fast Neutron Computed Tomography System for Void Fraction Measurements
Bibliographic record
Abstract
Subchannel analysis codes have been a valuable thermalhydraulic safety analysis tool for fuel bundle analysis by providing key metrics such as the deterioration of heat transfer from the fuel to the coolant, called the critical heat flux (CHF). To predict the occurrence and location of CHF accurately, experimental data of the void distribution are required to validate the code models. However, there is a lack of such local phase measurements for full-scale bundle geometries, in particular for CANDU geometries, which significantly impacts the modelling of two-phase flow in subchannel codes. A portable fast neutron computed tomography (FNCT) imaging system is developed to measure the steam-water phase distributions within a full-scale heated bundle at an experimental facility. The system is built to address the need for more local measurement techniques to improve the prediction accuracy of safety analysis codes for nuclear reactor design and licensing. Specifically, the nuclear industry in Canada has identified a major impediment in adopting new and accurate predictive methodologies is the lack of detailed phase-field measurements in realistic full-scale fuel assembly geometries under prototypical full-scale reactor conditions. This research involves the modelling, design, development, assembly, and demonstration of the functionality of a portable FNCT imaging system to measure the void fraction distribution in a full-scale heated bundle at a thermalhydraulic test facility. The system uses a modern fast-neutron deuterium-deuterium (D-D) fusion generator coupled with state-of-the-art silicon photomultiplier (SiPM) detectors. Key design parameters such as the resolution and void fraction prediction capabilities have been determined to be within theoretical predictions. A first application of machine learning to void fraction imaging has been done to enhance imaging capabilities and increase the effectiveness of void fraction prediction under limited scan time constraints. This study provides a fully operational industry-grade tool for use in advanced thermalhydraulic measurements.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".