This study presents an improved comprehensive atomization model for a pressure swirl atomizer. The model integrates internal flow predictions, linear instability analysis of a swirling annular liquid sheet, primary atomization sub-model, and droplet velocity sub-model. Measurement data combined with the inviscid theory model predict the internal flow, providing liquid sheet velocity and thickness at the atomizer outlet. The dispersion relation of surface disturbances is obtained through linear instability analysis. A primary breakup predictive model for particle size distribution is constructed based on the wavelength and growth rate within the full unstable wavenumber range of the dispersion relation. Assuming uniform circumferential distribution and a normal distribution of spray angles, the droplet velocity is assigned according to the liquid sheet velocity. The model is implemented into Eulerian–Lagrangian simulations as initial conditions for discrete phase droplets to simulate the spray field. Results show the model can accurately predict the Sauter mean diameter with an error of less than 6% and effectively predicts the spray structure and spray cone angle. The dependency of the model on its parameters is also studied, determining that the values of the ligament constant and dispersion angle have an obvious impact on the prediction of Sauter mean diameter and spray structure.

Super-resolution (SR) reconstruction of turbulent flows, inspired by breakthroughs in image SR, aims to generate high-resolution flow fields from low-resolution data that are available from numerical simulations and experimental measurements. Deep convolutional neural networks (CNN) have long been the pillar in both image and turbulence SR. Recently, networks based on the Swin Transformer featuring an innovative attention mechanism have achieved great success in image SR. This study first applies a latest variant of the Swin Transformer, SwinFIR (Image Restoration with Swin Transformer using a fast Fourier convolution layer), to the super-resolution of forced homogeneous isotropic turbulence, and compares its performance with a well-established CNN-based model, the Residual-in-Residual Dense Block (RRDB). The results show that the SwinFIR slightly outperforms the RRDB after being trained with a Ln-norm-based pixel loss, but both models still significantly underestimate the high-frequency turbulent energy. Further improvement is hindered by the loss. To address this issue, a new fine-tuning strategy featuring a spectral loss is proposed in this work. The results show that the proposed fine-tuning strategy is highly effective in improving the reconstruction of high-frequency turbulence. The generalization tests on data with higher Reynolds numbers unseen in the training further demonstrate that the fine-tuned network generalizes well for 4× SR (i.e., enhancing resolution by a factor of four in each direction), and that good generalization performance can also be achieved for higher SR factors with a consistent ratio of grid size to Kolmogorov length scale. The underlying mechanisms dictating the generalization performance are discussed, providing clues for developing better training strategies that can endow models with unconditional generalization capability.

This paper introduces a deep learning-based super-resolution framework specifically developed for accurately reconstructing high-resolution velocity fields in two-way coupled particle-laden turbulent flows. Leveraging conditional generative adversarial networks, the generator network architecture incorporates explicit conditioning on physical parameters, such as effective particle mass density and subgrid kinetic energy, while the discriminator network is conditioned on low-resolution data as well as high-frequency content of the input data. High-fidelity direct numerical simulation datasets, covering a range of particle Stokes numbers, particle mass loadings, and carrier gas turbulence regimes, including forced- and decaying-turbulence, serve as training and testing datasets. Extensive validation studies, including detailed analyses of energy spectra, probability density functions, vorticity distributions, and wavelet-based decomposition, demonstrate the model's accuracy and generalization capabilities across different particle parameters. The results show that the network utilizes particle data, mainly in the reconstruction of high-frequency details modulated by particles. Additionally, systematic assessment of the model's performance in capturing previously unseen flow regimes further validates its predictive capabilities.