Convolutional neural network use in fast neutron computed tomography for void fraction measurements
Bibliographic record
Abstract
Subchannel analysis codes are used for safety analysis and require suitable data to accurately model complex conditions that occur in fuel bundle geometries. To ensure accurate modelling of the complex two-phase flow phenomena in a reactor bundle, experimental data of the void distribution are required. Computed tomography provides a non-invasive method of measurement of the 2D or 3D distribution of void fraction within a bundle geometry. However, thick pressure vessels in full-scale conditions make it difficult to image bundle geometries and maintain contrast of the internal structures such as water/vapour when using photon-based sources. Fast neutron systems provide good penetration capability while maintaining sensitivity to the water-vapour contrast within the bundle but require long scan times (order of hours) because of the relatively poor detection efficiency and low source strength, which may preclude the application in full-scale thermal–hydraulic testing scenarios. Therefore, to effectively use Fast Neutron Computed Tomography (FNCT) in thermal-hydraulics safety experiments, the large scan times must be reduced, and the increased noise and decreased image fidelity that accompanies this reduction must be addressed. This challenge is addressed in this paper using convolutional neural networks (CNNs), a branch of machine learning that excels in image processing tasks. A Synthetic nuclear fuel bundle image reconstructions training dataset, including noise and blurring effects, was generated using a custom MATLAB and Python. The CNN model uses the dataset to create a mapping between these noisy reconstructed images and their respective ground truth image. In this work, the Residual U-Net model significantly improves image reconstructions, leading to more accurate measurements of subchannel void fraction compared to the initial noisy images. The model is able to predict the subchannel void fraction with a mean absolute percentage error (MAPE) of 3.2 % ± 2.4 % on a subchannel basis. Here, MAPE refers to the mean of the absolute differences between the predicted and true void fractions, expressed in percentage points. This means that a void fraction prediction of 50 % with a true value of 53 % results in a 3 % error, not a relative percentage of the void fraction itself. The void fractions were predicted within 8.43 % of the true values for 95 % of the subchannels, demonstrating that the majority of predictions are highly accurate.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".