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Related Experiment Videos

AM-GMSIS: An Efficient Reliability Evaluation Method for Passive Nuclear Safety Systems Based on Ensemble Neural

Tianrui Li1, Xinkun Xiao1, Shuai Wang1

  • 1School of Nuclear Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|May 29, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an adaptive metamodel-based Gaussian mixture subset simulation-importance sampling (AM-GMSIS) method to improve nuclear power plant reliability analysis. The novel approach enhances accuracy and efficiency for passive systems with low failure probabilities.

Area of Science:

  • Nuclear Engineering
  • Computational Science
  • Reliability Engineering

Background:

  • Traditional Monte Carlo methods struggle with computational efficiency and accuracy for nuclear power plant passive systems, especially those with small failure probabilities.
  • Assessing the reliability of systems with nonlinear responses and multimodal failure boundaries presents significant challenges.
  • Accurate reliability assessment is crucial for the safety and operational integrity of nuclear power facilities.

Purpose of the Study:

  • To propose and validate a novel adaptive metamodel-based Gaussian mixture subset simulation-importance sampling (AM-GMSIS) method.
  • To enhance the computational efficiency and accuracy of reliability analysis for nuclear power plant passive systems.
  • To accurately quantify the failure probability of a nuclear power plant's passive residual heat removal system.
Keywords:
functional reliabilityimportance samplingnuclear passive safety systemsmall failure probabilitysubset simulation

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Main Methods:

  • The AM-GMSIS method integrates subset simulation and importance sampling with an ensemble neural network (ENN) as a surrogate model.
  • A Gaussian mixture model and Bayesian information criterion are used for adaptive identification and coverage of multiple failure domains.
  • Active learning is driven by ENN prediction uncertainty and the U-function to improve surrogate accuracy near the failure boundary.

Main Results:

  • The AM-GMSIS method demonstrated accurate and stable reliability estimates for problems with strong nonlinearity and multi-failure domains.
  • The failure probability of a nuclear power plant's passive residual heat removal system under a station blackout accident was quantified as 3.57 × 10⁻⁶.
  • Sensitivity analysis identified key input parameters influencing the model response, highlighting practical engineering applications.

Conclusions:

  • The proposed AM-GMSIS method offers a significant advancement in the reliability assessment of nuclear power plant passive systems.
  • The framework provides accurate failure probability quantification and valuable insights through sensitivity analysis.
  • This approach holds potential for improving the engineering design and safety assurance of critical nuclear infrastructure.