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Analysis of process criticality accident risk using a metamodel-driven Bayesian network

机译：使用Metomodel驱动的贝叶斯网络分析过程致幻事故风险

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摘要

In recent years, neural network metamodels have become increasingly popular for reducing the computational burden of performing direct, simulation-based analysis of physical systems. This paper proposes a new methodology for training a neural network metamodel and incorporating it into a Bayesian network-based probabilistic risk assessment. This methodology can be applied to a wide variety of industrial accidents, where there is at least one latent variable that is normally calculated using a physics code. The main benefit of this methodology is that it combines the interpretability and sampling algorithm of a Bayesian network with the high-dimensional, latent variable modeling capability of a neural network metamodel.This paper also provides an example of how this methodology is applied to fissionable material operations in a nuclear facility to estimate process criticality accident risk. Although process criticality accidents are specific to the nuclear industry, the methodology described in this paper can be adapted to other types of industrial accidents and rare events.
机译：近年来，神经网络元模型已经越来越受到降低对实际系统的直接，模拟分析的计算负担。本文提出了一种培训神经网络元模型并将其纳入贝叶斯网络的概率风险评估的新方法。该方法可以应用于各种工业事故，其中至少有一个通常使用物理代码计算的潜变量。该方法的主要好处是它将贝叶斯网络的解释性和采样算法与神经网络元模型的高维潜变量建模能力相结合。本文还提供了该方法如何应用于可裂变材料的示例核设施中的运营估计过程关键意外风险。虽然工艺关键事故是特定于核工业的，但本文描述的方法可以适应其他类型的工业事故和罕见的事件。

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