  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 ($Re_\tau =
180$) 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.
