This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

The significant growth of artificial intelligence (AI) methods in machine learning (ML) and
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …

Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-
temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an …

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …

This study proposes a newly developed deep-learning-based method to generate turbulent
inflow conditions for spatially develo** turbulent boundary layer (TBL) simulations. A …

The impetus of the current three-dimensional Eulerian–Lagrangian work is to analyze the
impact of simultaneously using the inventive high-voltage conductors and Nitrotherm …

Recent attempts to employ deep learning technology for the super-resolution (SR)
reconstruction of turbulence have focused chiefly on reconstructing two-dimensional (2D) …

The current revolution in the field of machine learning is leading to many interesting
developments in a wide range of areas, including fluid mechanics. Fluid mechanics, and …

Obtaining reliable flow data is essential for the fluid mechanics analysis and control, and
various measurement techniques have been proposed to achieve this goal. However …

With the rapid development of artificial intelligence technology, data-driven advanced
models have provided new ideas and means for airfoil aerodynamic optimization. As the …