This paper focuses on pinch-off location and time during 2D droplet impact onto a wetted stationary cylinder using the lattice Boltzmann method. A general off-lattice boundary condition is implemented for the curved boundary. The boundary condition is examined by comparing the steady-state velocity profile of the Taylor-Couette flow with its analytical solution. Additionally, the spreading length of a droplet impacting a wetted stationary cylinder is shown to be in good agreement with the numerical data of the literature. In the results section, the effects of Reynolds, Weber, and Froude numbers, as well as the droplet/cylinder diameter ratio, on the pinch-off length and time are investigated. The simulations revealed that as the Froude number increases, the pinch-off time increases with a slope 1.39 times more than that of the pinch-off length decrease. Moreover, it is shown that the critical Weber number for onset of dripping process increases with increasing the Froude number or decreasing the Reynolds number.
We examined the capability of an unsupervised deep learning network to capture the spatial organizations of large-scale structures in a cross-stream plane of a fully-developed turbulent channel flow at Reτ ≈ 180. For this purpose, a generative adversarial network (GAN) is trained using the instantaneous flow fields in the cross-stream plane obtained by a direct numerical simulation (DNS) to generate similar flow fields. Then, these flow fields are examined by focusing on the turbulent statistics and the spatial organizations of coherent structures. We extracted the intense regions of the streamwise velocity fluctuations (u) and the vortical structures in the cross-stream plane. Comparing the DNS and GAN flow fields, it is revealed that the network not only presents the one-point and two-point statistics quite accurately but also successfully predicts the structural characteristics hidden in the training dataset. We further explored the meandering motions of large-scale u structures by measuring their waviness in the cross-stream plane. It is shown that as the size of the u structures increases, they exhibit more aggressive waviness behavior which in turn increases the average number of vortical structures surrounding the low-momentum structures. The success of GAN in this study suggests its potential to predict similar information at a high Reynolds number and be utilized as an inflow turbulence generator to provide instantaneous boundary conditions for more complicated problems such as turbulent boundary layers. This has the potential to greatly reduce the computational costs of DNS related to a required large computational domain at high Reynolds numbers.
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