arXiv subjects

Ruyue Cheng

Publications and source records attributed to Ruyue Cheng.

2 recordsLinked to original sources

arXiv2026

Large-eddy simulation of moderately dense evaporating sprays with particle-informed super-resolution

In large-eddy simulation (LES) of dense sprays or sprays with pronounced clustering, evaporation rates can be inaccurate when the mesh is too coarse to provide realistic boundary conditions for the widely employed single droplet evaporation model. This is especially relevant to liquid spray combustion in practical applications. Deep learning-based super-resolution (SR) has recently emerged as a promising method for LES subgrid-scale modeling, capable of enhancing flow field resolution. This technique appears well-suited to reconstruct the local gas fields within the inter-droplet space that can be used to correct the evaporation rates. However, it has not yet been applied for this purpose. This paper presents an innovative SR approach $-$ particle-informed super-resolution (PISR) $-$ that approximates high-resolution flow fields for improved evaporation computation. It is validated with a priori, a posteriori and generalization tests on moderately dense sprays. The results show that PISR-LES can closely replicate the evaporation rates computed in a carrier-phase direct numerical simulation (CP-DNS), significantly reducing the discrepancy in the fuel mass fraction field between LES and CP-DNS. Furthermore, the PISR model exhibits robust generalization to cases unseen in training when varying air temperature, droplet diameter, turbulent Reynolds number and spray pattern.

physics.flu-dyn↗

arXiv2025

Super-resolution of turbulent velocity fields in two-way coupled particle-laden flows

This paper introduces a deep learning-based super-resolution (SR) framework specifically developed for accurately reconstructing high-resolution velocity fields in two-way coupled particle-laden turbulent flows. Leveraging conditional generative adversarial networks (cGANs), 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 (DNS) 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 (PDFs), 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.

physics.flu-dyn↗