基于物理与数据驱动的计算流体力学及核热工水力多尺度模拟方法综述
Review of Physics-based and Data-driven Multiscale Simulation Methods for Computational Fluid Dynamics and Nuclear Thermal Hydraulics
AI总结：
本文综述了计算流体力学与核热工水力中的多尺度模拟方法，分类讨论基于物理的串行/并发方法及数据驱动方法（如误差校正、闭合建模），并全面回顾了数据驱动的湍流建模应用。
摘要：
流体流动的建模需要相应充分且有效的方法，以考虑所研究物理问题的多尺度特性。尽管过去几十年计算能力大幅增长，但在工程和系统尺度上直接解析所有尺度的流体流动建模在计算上仍然不可行地昂贵。因此，历史上提出了几种基于物理的方法论，试图“弥合”现有的尺度差距。本文讨论了计算流体力学和热工水力（重点在工程尺度应用）中尺度差距的起源。讨论辅以对基于物理的多尺度建模方法的回顾、分类和讨论。该分类基于文献中已有的分类，区分了串行和并发方法（基于微观与宏观模型之间的信息流）及其变体。回顾了数据驱动（基于机器学习和统计）方法在实现和/或改进多尺度建模（弥合尺度差距）方面的可能应用。所引入的分类区分了误差校正（混合）模型；闭合建模（湍流模型、两相和热流体闭合）；以及旨在促进并发基于物理的多尺度建模方法的方法。对用于湍流建模的数据驱动方法进行了全面回顾。
英文摘要：
Modeling of fluid flows requires corresponding adequate and effective approaches that would account for multiscale nature of the considered physics. Despite the tremendous growth of computational power in the past decades, modeling of fluid flows at engineering and system scales with a direct resolution of all scales is still infeasibly computationally expensive. As a result, several different physics-based methodologies were historically suggested in an attempt to "bridge" the existing scaling gaps. In this paper, the origin of the scaling gaps in computational fluid dynamics and thermal hydraulics (with an emphasis on engineering scale applications) is discussed. The discussion is supplemented by a review, classification, and discussion of the physics-based multiscale modeling approaches. The classification is based on the existing in literature ones and distinguishes serial and concurrent approaches (based on the information flow between micro- and macro-models) with their variations. Possible applications of the data-driven (machine learning and statistics-based) methods for enabling and / or improving multiscale modeling (bridging the scaling gaps) are reviewed. The introduced classification distinguishes error correction (hybrid) models; closure modeling (turbulence models, two-phase and thermal fluid closures); and approaches aimed at the facilitation of the concurrent physics-based multiscale modeling approaches. A comprehensive review of data-driven approaches for turbulence modeling is performed.
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