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Machine learning of fire hazard model simulations for use in probabilistic safety assessments at nuclear power plants

机译：火灾隐患模型仿真的机器学习，用于核电厂的概率安全评估

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This study explored the application of machine learning to generate metamodel approximations of a physics-based fire hazard model. The motivation to generate accurate and efficient metamodels is to improve modeling realism in probabilistic safety assessments where computational burden has prevented broader application of high fidelity models. The process involved scenario definition, generating training data by iteratively running the fire hazard model called CFAST over a range of input space using the RAVEN software, exploratory data analysis and feature selection, an initial testing of a broad set of metamodel methods, and finally metamodel selection and tuning using the R software.Twenty-five metamodel methods ranging in class and complexity were investigated. Linear models struggled because the physics of fire are non-linear. A k-nearest neighbor (kNN) model fit the vast majority of calculations within +/- 10% for maximum upper layer temperature and its timing.The resulting kNN model was compared to an algebraic model typically used in fire probabilistic safety assessments. This comparison illustrated the potential of metamodels to improve modeling realism over simpler models selected for computational feasibility. While the kNN metamodel is a simplification of the higher fidelity model, the error introduced is quantifiable and can be explicitly considered.
机译：这项研究探索了机器学习在生成基于物理的火灾隐患模型的元模型近似中的应用。生成准确有效的元模型的动机是为了提高概率安全性评估中的建模现实性，在这种情况下，计算负担阻碍了高保真度模型的广泛应用。该过程涉及场景定义，通过使用RAVEN软件在一定范围的输入空间上反复运行称为CFAST的火灾隐患模型，探索性数据分析和特征选择，一系列广泛的元模型方法的初始测试以及最终的元模型来生成训练数据使用R软件进行选择和调整。研究了25种元模型，这些方法的分类和复杂程度各不相同。线性模型之所以难以解决，是因为火的物理性质是非线性的。 k近邻（kNN）模型适合最大上层温度及其时间的+/- 10％以内的绝大多数计算，并将所得kNN模型与通常用于火灾概率安全评估的代数模型进行比较。这种比较说明了元模型与为计算可行性选择的简单模型相比，可以改善建模现实性的潜力。尽管kNN元模型是高保真度模型的简化形式，但引入的误差是可量化的，可以明确考虑。

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