Applying Shapley Effect for Sensitivity Analysis During Reactor Transient
Bibliographic record
Abstract
Sensitivity analysis is a critical tool in reactor safety assessments, as it evaluates the impact of uncertainties in input parameters, identifies key factors, and highlights potential safety risks and measures. Conventional sensitivity methods, such as Spearman, Pearson, or Kendall, while straightforward, are typically limited to linear relationships and independent input parameters. Shapley values offer a more advanced, model-agnostic approach to sensitivity analysis, making them particularly valuable in scenarios with dependent parameters or nonlinear systems.This study not only applies variance-based sensitivity methods, including Sobol indices and Shapley values, but also introduces the development of a reduced-order model (ROM) based on deep neural networks (DNNs) combined with Shapley values for time-dependent reactor simulations. This approach addresses the computational challenges of traditional methods, especially in cases involving correlated parameters, providing a more efficient and accurate sensitivity analysis. Sensitivity indices are calculated for the TWIGL benchmark, with two-group cross sections as the input parameters and core power during the ramp reactivity insertion transient as the output.The results demonstrate that Shapley values, combined with the DNN-based ROM, yield robust, accurate, and physically meaningful indices, especially in models with dependent parameters where Sobol indices may lead to over- or underestimation and might even result in negative indices. This highlights the advantages of Shapley values for comprehensive and reliable sensitivity analyses in complex reactor simulations.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".