Steady State 2D Regularized Lid-Driven Cavity Low-resolution Spatiotemporal Snapshots - Part 2

This data was generated using the pseudo-spectral DNS method outlined in Lee, Dowell, and Balajewicz CNSNS (2017).

Filenames denote Reynolds number. Each file contains a velocity snapshot matrix where each column is sampled at a different timestep. Original simulations had a factor of 100 higher temporal resolution than do these matrices; the snapshots provided here were spaced 0.1 nondimensional time units apart. Each column comprises both horizontal and vertical velocity data with the former stacked atop the latter.

A provided convertCavitySnapshot.m function converts a single cavity snapshot into non-stacked organization. cheb.m builds the x-vector (y is identical) and differentiation matrix D where dx(square_snapshot) = matmul(-square_snapshot,D.transpose) and dy(square_snapshot) = matmul(D,square_snapshot). plotSnapshot.m likewise plots a snapshot for user viewing.

All data in this repository is discretized as 130 points in both the x and the y directions, viz. the 'res' variable passed to convertCavitySnapshot.m would be 130.

本数据集系采用Lee、Dowell及Balajewicz等人在2017年于CNSNS期刊上发表的伪谱DNS方法所生成。文件名标识了雷诺数。每个文件包含一个速度快照矩阵，其中每列对应不同的时间步长。原始模拟的时序分辨率为矩阵的100倍；此处提供的快照间隔为0.1无量纲时间单位。每列包含水平和垂直速度数据，其中水平速度数据位于垂直速度数据之上。提供的convertCavitySnapshot.m函数可将单个腔体快照转换为非堆叠组织。cheb.m构建x向量（y向量与此相同）及微分矩阵D，其中dx(平方快照) = 矩阵乘积(-平方快照，D的转置)和dy(平方快照) = 矩阵乘积(D，平方快照)。plotSnapshot.m同样用于绘制快照以供用户查看。本仓库中所有数据在x和y方向上均以130个点进行离散化，即传递给convertCavitySnapshot.m的res变量应为130。