five

Direct Numerical Simulation Dataset of Turbulent Channel Flow at Re_tau=180

收藏
Figshare2025-11-25 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/Direct_Numerical_Simulation_Dataset_of_Turbulent_Channel_Flow_at_Re_tau_180/30636068
下载链接
链接失效反馈
官方服务:
资源简介:
This database corresponds to a direct numerical simulation (DNS) of an incompressible turbulent channel flow at friction Reynolds number Re_tau=u_tau*delta/nu=180, where delta = 1 m is the channel half-height, u_tau = 1 m/s is the friction velocity, and nu is the kinematic viscosity. The flow is computed with the in-house flow solver RHEA (https://gitlab.com/ProjectRHEA/flowsolverrhea). The simulation is initialized from a parabolic velocity profile seeded with random perturbations, and advanced in time until fully developed turbulence is attained after approximately 10 large-eddy turn-over times (LETOTs), defined as t_ell = delta/u_tau. Once statistical steady-state is reached, instantaneous flow fields are sampled every Delta t/t_ell = 0.5, yielding a database of 4600 equally spaced 3D snapshots. In the present work, the released dataset comprises 1525 snapshots extracted from this sequence, while the full set can be generated using the RHEA flow solver. Data is stored separately in HDF5 files in the different LETOTs folders. Each file comprises a 3D instantaneous snapshot of the following flow fields: (i) velocity in the streamwise (u), wall-normal (v) and spanwise (w) directions, and (ii) pressure.The mesh is stored in the 3d_turbulent_channel_flow-MESH.h5 file located in the mesh folder. The computational domain extends 4*pi*delta x 2*delta x 4/3*pi*delta in the streamwise (x), wall-normal (y) and spanwise (z) directions. The grid comprises 256 x 128 x 128 points, with uniform spacing in the homogeneous directions corresponding to Delta x^+ = 9 and Delta z^+ = 6, and a stretched distribution in the y-direction such that the first off-wall point is located at y^+ = 0.1 and 0.1
创建时间:
2025-11-25
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作