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.

翻译：湍流模拟对于航空航天工程、环境科学、能源工业及生物医学等众多具有重大社会影响的应用领域至关重要。大涡模拟（LES）因其较低的计算成本，已成为替代直接数值模拟（DNS）进行湍流模拟的常用方法。然而，LES无法精确捕捉所有尺度上的湍流输运特征。从低分辨率LES数据中重构DNS对众多科学与工程学科至关重要，但由于湍流场具有时空复杂性，现有超分辨率方法面临诸多挑战。本研究提出一种新型物理引导神经网络，用于从低分辨率LES数据中重构时序DNS数据。该方法在时空模型架构设计中融入控制流动动力学的偏微分方程，并开发了基于退化的精化方法以强化物理约束，进一步减少长时间积累的重构误差。针对两类不同湍流数据的实验结果证实，该方法在重构高分辨率DNS数据及保持流动输运物理特性方面具有显著优势。