Numerical simulation of the processes of atomization and combustion of a coal-water slurry in different-scale installations

Numerical simulation of atomization of a coal-water suspension (CWS) by a pneumatic nozzle is carried out, taking into account the processes of secondary breakup of droplets. The characteristic parameters of atomization were obtained (the dispersed jet opening angle, the size and velocity of the droplets). The data obtained were used to simulate atomization and combustion of the CWS on the firing stand. The results obtained are in good agreement with experimental data. The proposed numerical technique allows research on the introduction of advanced technologies and the improvement of existing different-scale installations (from stands to pilot industrial boilers).

Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net

Accurate particle detection is a common challenge in particle field characterization with digital holography, especially for gel secondary breakup with dense complex particles and filaments of multi-scale and strong background noises. This study proposes a deep learning method called Mo-U-net which is adapted from the combination of U-net and Mobilenetv2, and demostrates its application to segment the dense filament-droplet field of gel drop. Specially, a pruning method is applied on the Mo-U-net, which cuts off about two-thirds of its deep layers to save its training time while remaining a high segmentation accuracy. The performances of the segmentation are quantitatively evaluated by three indices, the positive intersection over union (PIOU), the average square symmetric boundary distance (ASBD) and the diameter-based prediction statistics (DBPS). The experimental results show that the area prediction accuracy (PIOU) of Mo-U-net reaches 83.3%, which is about 5% higher than that of adaptive-threshold method (ATM). The boundary prediction error (ASBD) of Mo-U-net is only about one pixel-wise length, which is one third of that of ATM. And Mo-U-net also shares a coherent size distribution (DBPS) prediction of droplet diameters with the reality. These results demonstrate the high accuracy of Mo-U-net in dense filament-droplet field recognition and its capability of providing accurate statistical data in a variety of holographic particle diagnostics. Public model address: https://github.com/Wu-Tong-Hearted/Recognition-of-multiscale-dense-gel-filament-droplet-field-in-digital-holography-with-Mo-U-net.

Numerical Investigation of Droplet Properties of a Liquid Jet in Supersonic Crossflow

The atomization process of a liquid jet in supersonic crossflow with a Mach number of 1.94 was investigated numerically under the Eulerian-Lagrangian scheme. The droplet stripping process was calculated by the KH (Kelvin-Helmholtz) breakup model, and the secondary breakup due to the acceleration of shed droplets was calculated by the combination of the KH breakup model and the RT (Rayleigh-Taylor) breakup model. In our research, the existing KH-RT model was modified by optimizing the empirical constants incorporated in this model. Moreover, it was also found that the modified KH-RT breakup model is applied better to turbulent inflow of a liquid jet than laminar inflow concluded from the comparisons with experimental results. To validate the modified breakup model, three-dimensional spatial distribution and downstream distribution profiles of droplet properties of the liquid spray in the Ma = 1.94 airflow were successfully predicted in our simulations. Eventually, abundant numerical cases under different operational conditions were launched to investigate the correlations of SMD (Sauter Mean Diameter) with the nozzle diameter as well as the airflow Mach number, and at the same time, modified multivariate power functions were developed to describe the correlations.