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ENTS: A Novel Algorithm for Sampling-Based Dynamic Probabilistic Risk Assessment
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AI
DOI：10.1080/00295450.2026.2618948.png)
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Abstract
En 中文
Dynamic probabilistic risk assessment (PRA) is a computational approach to risk assessment that offers certain advantages over the more conventional PRA methods. Advantages of dynamic PRA include automatic generation of potential accident scenarios and explicit consideration of complex system dynamics; however, dynamic PRA typically requires computationally costly simulations, which have been a significant barrier to its widespread adoption. To improve the computational performance of dynamic PRA, this paper proposes a novel algorithm for sampling-based dynamic PRA, the exploratory nuclear tree sampler (ENTS). ENTS utilizes importance sampling and Monte Carlo tree search to reduce the computational cost and increase the number of unique scenarios generated during a dynamic PRA. To demonstrate the computational improvements offered by ENTS, its performance is assessed using two case studies from the dynamic PRA literature. The computational performance of a comparable Monte Carlo method is assessed and used as a benchmark for comparison. This comparison shows that the ENTS approach requires fewer overall simulations to estimate the system risk and generates more unique accident scenarios than the Monte Carlo approach.
Keywords:
Probabilistic risk assessment
dynamic probabilistic risk assessment
Monte Carlo simulation
Monte Carlo tree search
importance sampling
nuclear power plants