Zobrazeno 1 - 10
of 1 134
pro vyhledávání: '"Wang Jianchun"'
Acquisition of large datasets for three-dimensional (3D) partial differential equations are usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and shows great pot
Externí odkaz:
http://arxiv.org/abs/2411.04502
Autor:
Zhang, Zhiyao, Li, Zhijie, Wang, Yunpeng, Yang, Huiyu, Peng, Wenhui, Teng, Jian, Wang, Jianchun
The accurate and fast prediction of long-term dynamics of turbulence presents a significant challenge for both traditional numerical simulations and machine learning methods. In recent years, the emergence of neural operators has provided a promising
Externí odkaz:
http://arxiv.org/abs/2411.01885
Autor:
Hu, Running, Xie, Jin-Han, Li, Xinliang, Yu, Changping, Hu, Yuan, Wang, Jianchun, Chen, Shiyi
Helical magnetohydrodynamic turbulence with Hall effects is ubiquitous in heliophysics and plasma physics, such as star formation and solar activities, and its intrinsic mechanisms are still not clearly explained. Direct numerical simulations reveal
Externí odkaz:
http://arxiv.org/abs/2405.03405
Autor:
Luo, Tengfei, Li, Zhijie, Yuan, Zelong, Peng, Wenhui, Liu, Tianyuan, Liangzhu, Wang, Wang, Jianchun
The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh-Taylor (RT) turbulence with miscible fluids at Atwood number $A_t=0.5$, stratification parameter $Sr=1.0$, and Reynol
Externí odkaz:
http://arxiv.org/abs/2404.05834
Predicting the large-scale dynamics of three-dimensional (3D) turbulence is challenging for machine learning approaches. This paper introduces a transformer-based neural operator (TNO) to achieve precise and efficient predictions in the large-eddy si
Externí odkaz:
http://arxiv.org/abs/2403.16026
Fast and accurate predictions of turbulent flows are of great importance in the science and engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural operator (IUFNO) in the stable prediction of long-time dynamics of
Externí odkaz:
http://arxiv.org/abs/2403.03051
The discrete direct deconvolution model (D3M) is developed for the large-eddy simulation (LES) of turbulence. The D3M is a discrete approximation of previous direct deconvolution model studied by Chang et al. ["The effect of sub-filter scale dynamics
Externí odkaz:
http://arxiv.org/abs/2402.08442
Autor:
Yan, Zheng, Wang, Jianchun, Wang, Lifeng, Lei, Zhu, Wu, Junfeng, Duan, Junyi, Tong, Fulin, Li, Xinliang, Yu, Changping
A novel self-sustaining mechanism is proposed for large-scale helical structures in compressible turbulent flows. The existence of two channels of subgrid-scale and viscosity terms for large-scale helicity evolution is confirmed for the first time, t
Externí odkaz:
http://arxiv.org/abs/2402.01996
Lyapunov exponents and Lagrangian chaos suppression in compressible homogeneous isotropic turbulence
Autor:
Yu, Haijun, Fouxon, Itzhak, Wang, Jianchun, Li, Xiangru, Yuan, Li, Mao, Shipeng, Mond, Michael
Publikováno v:
Physics of Fluids, 35(12), 2023
We study Lyapunov exponents of tracers in compressible homogeneous isotropic turbulence at different turbulent Mach number $M_t$ and Taylor-scale Reynolds number $Re_\lambda$. We demonstrate that statistics of finite-time Lyapunov exponents have the
Externí odkaz:
http://arxiv.org/abs/2310.09717
We study the statistically steady states of the forced dissipative three-dimensional homogeneous isotropic turbulence at scales larger than the forcing scale in real separation space. The probability density functions (PDFs) of longitudinal velocity
Externí odkaz:
http://arxiv.org/abs/2308.15292