An Artificial Neural Network (ANN) Model to Predict Critical Heat Flux (CHF) in a CANDU Fuel Element Simulation (FES) with Various Nonuniform Axial Heat Flux Shapes and Flow Liner Creep Profiles
Bibliographic record
Abstract
• Conventional ANNs used in CHF prediction can potentially face overfitting issues. • Overfitting is more pronounced in the problems involving various training features. • The introduced ANN model reduced overfitting over a wide mass flux range. • Benchmark against a TensorFlow model reveals the new model’s superior robustness. • The cases with nonuniform heat flux/annulus particularly benefit from this model. A classification ANN model was developed to predict critical power in a CANDU Fuel Element Simulation (FES) with various Axial Heat Flux Distributions (AFDs) and flow liner creep profiles. The ANN model employs 29 input features to model the AFDs and liner creep profiles and was trained by 433 test data. The classification ANN model was benchmarked against a standard regression ANN model developed with TensorFlow and the results are presented in this paper. The two models delivered roughly the same level of accuracy with a Root Mean Square Error (RMSE) of ∼ 2.5 %; however, the methodology used in the classification model seems to be able to alleviate overfitting and create a more tangible robustness in comparison with the regression model, albeit at the cost of a longer solution time. It is therefore recommended that the presented classification model be used in conjunction with typical regression models to attain more reliability, especially in problems including many features and small training datasets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".