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A general super-resolution reconstruction strategy was proposed for turbulent velocity fields using a generative adversarial network-based artificial intelligence framework. Two advanced neural networks, i.e., super-resolution generative adversarial network (SRGAN) and enhanced-SRGAN (ESRGAN), were first applied in fluid mechanics to augment the spatial resolution of turbulent flow. As a validation, the flow around a single-cylinder and a more complicated wake flow behind two side-by-side cylinders were experimentally measured using particle image velocimetry. The spatial resolution of the coarse flow field can be successfully augmented by 42 and 82 times with remarkable accuracy. The reconstruction performances of SRGAN and ESRGAN were comprehensively investigated and compared, including an analysis of the recovered instantaneous flow field, statistical flow quantities, and spatial correlations. The results convincingly demonstrated that both models can reconstruct the high-spatial-resolution flow field accurately even in an intricate flow configuration, and ESRGAN can provide a better reconstruction result than SRGAN in the mean and fluctuation flow field.

Fluid-induced flag vibrations provide unattended, efficient, low-cost, and scalable solutions for energy harvesting to power distributed wireless sensor nodes, heat transfer enhancement in channel flow, and mixing enhancement in process industries. This review surveys three generic configurations, the inverted flag, the standard flag, and the forced flag, i.e., an inverted or standard flag located downstream of a bluff body. Their instability boundaries, vibration dynamics, and vortex dynamics are compared in a unified framework to elucidate their common and distinct features and provide insights into the design of vibrating flags for various applications. Some common features are also identified and analyzed for describing the interaction between multiple flags, three-dimensional (3D) effects, and Reynolds number effects. The suggestions are intended to guide future research directions.

The unsteady flow behind an inverted flag placed in a water channel and then excited into a self-oscillating state is measured using time-resolved particle image velocimetry. The dynamically deformed profiles of the inverted flag are determined by a novel algorithm that combines morphological image processing and principle component analysis. Three modes are discovered with the successive decrease in the dimensionless bending stiffness: the biased mode, the flapping mode, and the deflected mode. The distinctly different flow behavior is discussed in terms of instantaneous velocity field, phase-averaged vorticity field, time-mean flow field, and turbulent kinetic energy. The results demonstrated that the biased mode generated abundant vortices at the oscillating side of the inverted flag. In the deflected mode, the inverted flag is highly deflected to one side of the channel and remains almost stationary, inducing two stable recirculation zones and a considerably inversed flow between them. In the flapping mode, the strongly oscillating flag periodically provides a strengthened influence on the fluid near the two sidewalls. The reverse von Kármán vortex street is well formed and energetic in the wake, and a series of high-speed impingement jets between the neighboring vortices are directed toward the sidewalls in a staggered fashion.

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