DNS of a counter-flow channel configuration

Mean flow and turbulence statistics of a compressible turbulent counter-flow channel configuration. This dataset is based on direct numerical simulations conducted using OpenSBLI (https://opensbli.github.io/), a Python-based automatic source code generation and parallel computing framework for finite difference discretisation. #==============================================================================================<br> # Please cite the following paper when publishing using this dataset: <br> # Title: Direct numerical simulation of compressible turbulence in a counter-flow channel configuration <br> # Authors: Arash Hamzehloo, David Lusher, Sylvain Laizet and Neil Sandham<br> # Journal: Physical Review Fluids <br> # DOI: https://doi.org/10.1103/PhysRevFluids.6.094603 <br> # ============================================================================================== Please note: Tables 1 and 2 of the above paper provide more detailed information on the counter-flow channels of this dataset. Each folder name of this dataset includes the Mach number, Reynolds number, domain size and grid resolution of a particular case, respectively. In each file, the first column contains the grid-point coordinates in the wall-normal direction (\(y\)) with the channel centreline located at \(y=0\). The mean stresses are defined as \(\langle u_i^{\prime}u_j^{\prime}\rangle=\langle u_i u_j \rangle - \langle u_i \rangle \langle u_j \rangle \). Angle brackets denote averages over the homogeneous spatial directions (streamwise \(x\) and spanwise \(z\)) and time. The Favre average is related to the Reynolds average as \(\langle \rho \rangle \{u_i^{\prime\prime}u_j^{\prime\prime}\}=\langle \rho u_i u_j \rangle - \langle \rho \rangle \langle u_i \rangle \langle u_j \rangle\). The mean Mach number is defined as \(\langle M \rangle = {\sqrt{\langle u \rangle^2+\langle v \rangle^2+\langle w \rangle^2}}/{{\langle a \rangle}}\) where \(a\) is the local speed of sound. The turbulent Mach number is defined as \(M_t = {\sqrt{\langle u^{\prime}u^{\prime} \rangle+\langle v^{\prime}v^{\prime} \rangle+\langle w^{\prime}w^{\prime} \rangle}}/{{\langle a \rangle}}\). # ============================================================================================== Details of the OpenSBLI framework, its numerical methodology and existing flow configurations can be found in the following papers: OpenSBLI: Automated code-generation for heterogeneous computing architectures applied to compressible fluid dynamics on structured grids. (link) OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures. (link) On the performance of WENO/TENO schemes to resolve turbulence in DNS/LES of high‐speed compressible flows. (link)可压缩湍流逆流通道构型的平均流场与湍流统计量数据集。本数据集基于使用OpenSBLI（https://opensbli.github.io/）开展的直接数值模拟（Direct Numerical Simulation, DNS），OpenSBLI是一款面向有限差分离散的、基于Python的自动源代码生成与并行计算框架。 #============================================================================================== # 若使用本数据集进行学术发表，请引用以下论文： # 论文标题：逆流通道构型可压缩湍流的直接数值模拟 # 作者：Arash Hamzehloo、David Lusher、Sylvain Laizet与Neil Sandham # 期刊：Physical Review Fluids # DOI：https://doi.org/10.1103/PhysRevFluids.6.094603 # ============================================================================================== 请注意：上述论文的表1与表2提供了本数据集涉及的逆流通道的更多详细信息。本数据集的每个文件夹名称依次对应特定算例的马赫数（Mach number）、雷诺数（Reynolds number）、计算域尺寸与网格分辨率。每个数据文件的第一列为壁面法向（wall-normal direction）的网格点坐标，通道中心线位于$y=0$处。平均应力定义为$langle u_i^{prime}u_j^{prime} angle=langle u_i u_j angle - langle u_i angle langle u_j angle$。尖括号表示对均匀空间方向（流向$x$与展向$z$）及时间的统计平均。法夫雷平均（Favre average）与雷诺平均（Reynolds average）的关系为$langle ho angle {u_i^{primeprime}u_j^{primeprime}}=langle ho u_i u_j angle - langle ho angle langle u_i angle langle u_j angle$。平均马赫数定义为$langle M angle = frac{sqrt{langle u angle^2+langle v angle^2+langle w angle^2}}{langle a angle}$，其中$a$为当地声速。湍流马赫数定义为$M_t = frac{sqrt{langle u^{prime}u^{prime} angle+langle v^{prime}v^{prime} angle+langle w^{prime}w^{prime} angle}}{langle a angle}$。 # ============================================================================================== 关于OpenSBLI框架的详细信息、其数值方法及已有的流动构型，可参阅以下论文：《OpenSBLI：面向结构化网格可压缩流体动力学的异构计算架构自动代码生成框架》（链接）、《OpenSBLI：适用于多种计算机架构的有限差分解算器自动推导与并行执行框架》（链接）、《高速可压缩流DNS/LES中用于湍流解析的WENO/TENO格式性能研究》（链接）