Abstract The integration of data science, analytics, and model-based reasoning provides a mechanism for enhanced understanding of systems and improved decision-making, but its potential has not been thoroughly explored for improving the safety and operational efficiency of nuclear power reactors. Nuclear power owners, operators, regulators, and researchers have made significant investments in probabilistic risk assessments, numerical models, computational simulations, and development of databases that capture industry-wide component performance and operating experience. The nuclear industry is relatively unique in the size, variety, scope, technical sophistication, and quality of available data and models that capture system performance under normal operations and a wide-range of adverse event conditions. However, to date, these resources have been used in a largely static and siloed manner. Data science, analytics, and model-based reasoning provide a mechanism for fusing diverse data sources and models to develop new insights on a variety of topics. Of particular interest to the nuclear industry is the ability to leverage these resources to enhance the safety and operational efficiency of nuclear power reactors. In this paper, we present a challenge to the nuclear energy community to better leverage the existing investments in data and models to enhance decision-making. In particular, we propose that integration of recent advances in data science, analytics, and model-based reasoning provides a valuable opportunity for the nuclear industry to build upon their existing investments by accessing the power of modern data integration and risk assessment tools. We begin by describing common data and model resources available in nuclear power operations and safety analysis and offer commentary on the potential power of using Bayesian networks as a structured framework for data fusion and model integration. Then we present two example problem structures for modeling risk-informed operational decisions using heterogeneous data and models to provide simple illustrations of the means by which information streams can be leveraged in real-time to provide online assessment of risk and to increase diagnostic capabilities. Illustrative model formulations are presented for decisions under adverse events and normal operational contexts. We conclude by identifying research activities that will enable the transformation of decision-making by applying new computational and modeling tools to existing data and models.

中文翻译：

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数据融合和模型集成对生成核动力反应堆实时风险洞察的价值

摘要 数据科学、分析和基于模型的推理的集成为增强对系统的理解和改进决策提供了一种机制，但其在提高核动力反应堆安全和运行效率方面的潜力尚未得到彻底探索。核电所有者、运营商、监管机构和研究人员在概率风险评估、数值模型、计算模拟和数据库开发方面进行了大量投资，以获取全行业组件性能和运行经验。核工业在规模、种类、范围、技术复杂性和可用数据和模型的质量方面相对独特，这些数据和模型可捕获正常运行和各种不利事件条件下的系统性能。然而，迄今为止，这些资源在很大程度上以静态和孤立的方式使用。数据科学、分析和基于模型的推理提供了一种机制，可以融合不同的数据源和模型，以开发关于各种主题的新见解。核工业特别感兴趣的是能够利用这些资源来提高核动力反应堆的安全性和运行效率。在本文中，我们向核能界提出了一个挑战，以更好地利用现有的数据和模型投资来加强决策。特别是，我们建议整合数据科学、分析和基于模型的推理方面的最新进展，为核工业提供宝贵的机会，通过访问现代数据集成和风险评估工具的力量，在其现有投资的基础上再接再厉。我们首先描述核电运行和安全分析中可用的通用数据和模型资源，并评论使用贝叶斯网络作为数据融合和模型集成的结构化框架的潜在力量。然后，我们提出了两个示例问题结构，用于使用异构数据和模型对风险知情的运营决策进行建模，以简单说明可以实时利用信息流来提供在线风险评估和提高诊断能力的方法。展示了用于在不良事件和正常操作环境下做出决策的说明性模型公式。最后，我们确定了通过将新的计算和建模工具应用于现有数据和模型来实现决策转变的研究活动。