activity
20202026
most citedDeconvolutional artificial neural network models for large eddy simulation of turbulence

88 citations · 106 across the 5 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2026

Physics informed wavelet Fourier representation for multiscale fluid dynamics

Chao Wang, Shilong Li, Yunpeng Wang +4

Multiscale fluid flows often contain localized flow structures, such as viscous shock layers, wet-dry fronts, steady viscous wakes, decaying vortical structures, and vortex-sheddin…

physics.flu-dyn2022

An inviscid model study of sandstorm in unstably stratified atmospheric boundary layer

Chenyue Xie, Xiangming Xiong, Jianjun Tao

According to field observations, the atmospheric boundary layer is usually unstably stratified before a dust and sandstorm, the particle-laden turbulent gravity current with an ext…

physics.flu-dyn2022★ 14 cited

Temporally sparse data assimilation for the small-scale reconstruction of turbulence

Yunpeng Wang, Zelong Yuan, Chenyue Xie +1

Previous works have shown that the small-scale information of incompressible homogeneous isotropic turbulence (HIT) is fully recoverable as long as sufficient large-scale structure…

physics.flu-dyn2021★ 4 cited

Artificial neural network approach for turbulence models: A local framework

Chenyue Xie, Xiangming Xiong, Jianchun Wang

A local artificial neural network (LANN) framework is developed for turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms are reconstructed by artificial n…

physics.flu-dyn2020★ 88 cited

Deconvolutional artificial neural network models for large eddy simulation of turbulence

Zelong Yuan, Chenyue Xie, Jianchun Wang

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at differ…