Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
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DOI：
10.48550/arxiv.2304.12130
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Shengyu Chen;Tianshu Bao;P. Givi;Can Zheng-;Xiaowei Jia
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作者：
Shengyu Chen;Tianshu Bao;P. Givi;Can Zheng-;Xiaowei Jia
模拟湍流对于航空航天工程、环境科学、能源工业和生物医学中许多具有重要社会意义的应用至关重要。大涡模拟（LES）因其计算量小而被广泛地用作直接数值模拟（DNS）的替代方法。然而，LES是无法捕捉所有的尺度湍流输送准确。从低分辨率LES重建DNS对于许多科学和工程学科至关重要，但由于湍流的时空复杂性，它对现有的超分辨率方法提出了许多挑战。在这项工作中，我们提出了一个新的物理指导的神经网络重建低分辨率LES数据的顺序DNS。所提出的方法利用偏微分方程的基础上的流动动力学的时空模型架构的设计。一个退化为基础的细化方法也被开发，以加强物理约束，并进一步减少长期积累的重建误差。两种不同类型的湍流数据上的结果证实了所提出的方法在重建高分辨率DNS数据和保留流动输运的物理特性方面的优越性。
Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simulation (LES) has been widely used as an alternative to direct numerical simulation (DNS) for simulating turbulent flows due to its reduced computational cost. However, LES is unable to capture all of the scales of turbulent transport accurately. Reconstructing DNS from low-resolution LES is critical for many scientific and engineering disciplines, but it poses many challenges to existing super-resolution methods due to the spatio-temporal complexity of turbulent flows. In this work, we propose a new physics-guided neural network for reconstructing the sequential DNS from low-resolution LES data. The proposed method leverages the partial differential equation that underlies the flow dynamics in the design of spatio-temporal model architecture. A degradation-based refinement method is also developed to enforce physical constraints and further reduce the accumulated reconstruction errors over long periods. The results on two different types of turbulent flow data confirm the superiority of the proposed method in reconstructing the high-resolution DNS data and preserving the physical characteristics of flow transport.