基于贝叶斯网络的预测能力成熟度量化
Predictive Capability Maturity Quantification using Bayesian Network
摘要：
在核工程中，建模与仿真（M&Ss）被广泛应用于支持风险知情的安全分析。由于核安全分析具有重要意义，因此需要一个令人信服的验证过程来评估模拟的充分性，即，M&S工具能够充分表示感兴趣的系统量的程度。然而，由于数据缺口，验证成为一个不确定性下的决策过程。专家的知识和判断需要收集，选择，表征和整合证据，以达到最终的充分性决策。然而，在确认框架CSAU：代码扩展、适用性和不确定性（NUREG/CR-5249）和EMDAP：评估模型开发和评估过程（RG 1.203）中，这样的决策过程在很大程度上是隐含和模糊的。当情景复杂时，知识偏差和不可靠的判断可能会被忽略，这可能会增加模拟充分性结果的不确定性和相应的风险。因此，需要一个框架，以一种实用的，透明的，一致的方式，正式的决策过程中的模拟充分性。本文提出了一个框架“预测能力成熟度量化使用贝叶斯网络（PCMQBN）”作为一个量化的框架，评估仿真充分性的基础上收集的信息，从验证活动。一个案例研究准备评估的充分性的光滑粒子流体动力学模拟预测的水动力作用在静态结构在外部洪水的情况下。与定性充分性评估和隐式充分性评估相比，PCMQBN能够提高仿真充分性结果的可信度，并减少风险知情安全分析中的预期损失。
英文摘要：
In nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Since nuclear safety analysis has important implications, a convincing validation process is needed to assess simulation adequacy, i.e., the degree to which M&S tools can adequately represent the system quantities of interest. However, due to data gaps, validation becomes a decision-making process under uncertainties. Expert knowledge and judgments are required to collect, choose, characterize, and integrate evidence toward the final adequacy decision. However, in validation frameworks CSAU: Code Scaling, Applicability, and Uncertainty (NUREG/CR-5249) and EMDAP: Evaluation Model Development and Assessment Process (RG 1.203), such a decision-making process is largely implicit and obscure. When scenarios are complex, knowledge biases and unreliable judgments can be overlooked, which could increase uncertainty in the simulation adequacy result and the corresponding risks. Therefore, a framework is required to formalize the decision-making process for simulation adequacy in a practical, transparent, and consistent manner. This paper suggests a framework "Predictive Capability Maturity Quantification using Bayesian network (PCMQBN)" as a quantified framework for assessing simulation adequacy based on information collected from validation activities. A case study is prepared for evaluating the adequacy of a Smoothed Particle Hydrodynamic simulation in predicting the hydrodynamic forces onto static structures during an external flooding scenario. Comparing to the qualitative and implicit adequacy assessment, PCMQBN is able to improve confidence in the simulation adequacy result and to reduce expected loss in the risk-informed safety analysis.
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