利用深度学习探索热工水力模拟中全局尺度桥接的局部物理相似性
Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation
摘要：
目前的系统热工水力代码在模拟真实工厂条件方面的可信度有限，特别是当几何形状和边界条件外推超出测试设施的范围时。本文提出了一种数据驱动的方法，特征相似性度量（FFSM），建立一个技术基础，以克服这些困难，通过探索局部模式，使用机器学习。多尺度数据中的底层局部模式由一组物理特征表示，这些物理特征体现了感兴趣的物理系统的信息、经验相关性和网格大小的影响。在执行有限数量的高保真数值模拟和足够数量的快速运行的粗网格模拟之后，建立误差数据库，并应用深度学习来构建和探索局部物理特征与模拟误差之间的关系。设计了基于混合对流的案例研究，以展示数据驱动模式在弥合全球尺度差距方面的能力。
英文摘要：
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis to overcome these difficulties by exploring local patterns using machine learning. The underlying local patterns in multiscale data are represented by a set of physical features that embody the information from a physical system of interest, empirical correlations, and the effect of mesh size. After performing a limited number of high-fidelity numerical simulations and a sufficient amount of fast-running coarse-mesh simulations, an error database is built, and deep learning is applied to construct and explore the relationship between the local physical features and simulation errors. Case studies based on mixed convection have been designed for demonstrating the capability of data-driven models in bridging global scale gaps.
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