基于增强型超分辨率生成对抗网络的空间受限域流动重建
Flow Reconstruction Using Spatially Restricted Domains Based on Enhanced Super-Resolution Generative Adversarial Networks
AI总结：
提出基于ESRGAN的空间受限域流场重建方法，经DNS层流、湍流槽道流及PIV实验验证，可由局部数据可靠重建完整流场并降低计算成本。
摘要：
本研究旨在利用增强型超分辨率生成对抗网络（ESRGAN）模型，从空间受限域数据重建完整流场。流场重建的难点在于，在非线性、多尺度且复杂的流动条件下，准确捕获并重建大量数据，同时确保物理一致性和较高的计算效率。ESRGAN模型具有强大的信息映射能力，能够从不同几何形状和尺寸的局部流场中捕获脉动特征。通过比较瞬时速度场、流动统计特性和概率密度分布，验证了该模型在全域流场重建方面的有效性。以直接数值模拟（DNS）得到的层流钝体绕流作为先验案例，该模型成功地从三个互不重叠的有限区域重建出完整流场，且流动统计特性与原始数据完全吻合。对重建结果的功率谱密度（PSD）验证也证明，该模型能够符合真实完整流场的时间行为。此外，采用摩擦雷诺数（Re_τ=180）的DNS湍流槽道流进行测试，结果表明该模型具备重建湍流场的能力，但结果质量取决于局部区域内流动特征的数量。最后，该模型被应用于根据粒子图像测速（PIV）实验测量结果重建湍流流场，利用近尾迹区域的有限数据重建更大的视场。湍流统计结果与实验数据高度吻合，表明该模型可作为一种可靠的数据驱动方法，在节省计算成本的同时克服PIV视场限制。
英文摘要：
This study aims to reconstruct the complete flow field from spatially restricted domain data by utilizing an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model. The difficulty in flow field reconstruction lies in accurately capturing and reconstructing large amounts of data under nonlinear, multi-scale, and complex flow while ensuring physical consistency and high computational efficiency. The ESRGAN model has a strong information mapping capability, capturing fluctuating features from local flow fields of varying geometries and sizes. The model effectiveness in reconstructing the whole domain flow field is validated by comparing instantaneous velocity fields, flow statistical properties, and probability density distributions. Using laminar bluff body flow from Direct Numerical Simulation (DNS) as a priori case, the model successfully reconstructs the complete flow field from three non-overlapping limited regions, with flow statistical properties perfectly matching the original data. Validation of the power spectrum density (PSD) for the reconstruction results also proves that the model could conform to the temporal behavior of the real complete flow field. Additionally, tests using DNS turbulent channel flow with a friction Reynolds number ($Re_τ= 180$) demonstrate the model ability to reconstruct turbulent fields, though the quality of results depends on the number of flow features in the local regions. Finally, the model is applied to reconstruct turbulence flow fields from Particle Image Velocimetry (PIV) experimental measurements, using limited data from the near-wake region to reconstruct a larger field of view. The turbulence statistics closely match the experimental data, indicating that the model can serve as a reliable data-driven method to overcome PIV field-of-view limitations while saving computational costs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
