Velocity and concentration measurements of passive tracer above gravelly seabeds under the wave influence

General description:This dataset was compiled and generated by Helena Stirnweiß as part of a series of Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) experiments conducted at the Institute of Fluid Mechanics at the University of Rostock. All data included herein is original and was acquired under controlled laboratory conditions. 

The experiments were conducted for 21 individual configurations of 3 different wave scenarios (description can be found in 'overview_wavescenarios.csv') and 7 seabed models (description can be found in 'overview_seabedmodels.csv'). The folders where the data is stored are named accordingly '[name of seabed model]_[name of wave scenario]'.

Horizontal (u [m/s]) and vertical (w [m/s]) velocities were measured simultaneously to the concentration (c [l/l]) of a tracer fluid released from the seabed. The collected data was analyzed and phase averages and phase-resolved covariances were derived and are given for the field of view of each configuration as .npy-files in the respective folder. Time-averaged and horizontally averaged profiles were determined for the concentration and all covariances in dependence on the bottom distance. The profiles are stored as .npy-files in the respective Folders. 

Mass mixing length (l_c) and Eddy diffusivity (D_t) profiles were derived for each configuration from the measured data as described in the corresponding article. The variables are given in dependence on the bottom distance as .npy-files in the respective folder. Slopes of the vertical mass mixing length profiles and Eddy diffusivity profiles from linear regression are given in 'slopes_turbmodels.csv'. 

TIME-RESOLVED DATA IS NOT PROVIDED IN THIS DATASET DUE TO EXTENSIVE DATA SIZE but will be shared upon request. Please contact the creators.

Description of .npy files in .zip-folders:

The time-averaged, horizontally averaged profiles (named '[c/RS/TF/TKE]_[optional: names of covariates]_time_averaged_[name of seabed model]_[name of wave scenario].npy') are given in each folder.All time-averaged data is stored in the following format.

import numpy as np

data = np.load(filename.npy, allow_pickle=True)#data[0] -> z-dimensions in mm##data[0][z]

#data[1] -> respective quantity (c, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))##data[1][z]

The phase-averages (named '[u/w/c]_phase_averaged_[name of seabed model]_[name of wave scenario].npy'), phase-resolved covariances of the fluctuations (named '[RS/TF]_[names of the covariates]_[name of seabed model]_[name of wave scenario].npy', note: RS stands for Reynolds stresses, TF stands for turbulent fluxes), and the turbulent kinetic energy (named 'TKE_[name of seabed model]_[name of wave scenario].npy') are given in each folder.All phase-resolved data is stored in the following format.

import numpy as np

data = np.load(filename.npy, allow_pickle=True)#data[0] -> x-dimensions in mm##data[0][z, x]

#data[1] -> z-dimensions in mm##data[1][z, x]

#data[2] -> respective quantity (c, u, w, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))##data[2][phi_idx, z, x]

#(phase-averaging is performed with 100 phase bins -> phi_idx ranges from 0 to 99)

Mass mixing length (lc) and eddy diffusivity (Dt) profiles were derived as described in the corresponding article and are given in each folder under '[lc/Dt]_prof_[name of seabed model]_[name of wave scenario].npy' in the following format:data = np.load(filename.npy, allow_pickle=True)#data[0] -> z-dimensions in mm##data[0][z]

#data[1] -> respective quantity (l_c in mm, D_t in m^2/s)##data[1][z]

数据集概述：本数据集由海伦娜·施特恩魏斯（Helena Stirnweiß）整理生成，系罗斯托克大学流体力学研究所开展的一系列粒子图像测速（Particle Image Velocimetry, PIV）与激光诱导荧光（Laser-Induced Fluorescence, LIF）实验的成果之一。本数据集收录的全部数据均为原创，且均在可控实验室环境下采集获取。

本次实验覆盖3类波浪工况（详细说明见`overview_wavescenarios.csv`）与7种海底模型（详细说明见`overview_seabedmodels.csv`），共计21组独立配置。数据存储文件夹的命名格式统一为「[海底模型名称]_[波浪工况名称]」。

实验同步测量了水平流速（u，单位：m/s）与垂直流速（w，单位：m/s），同时采集了从海底释放的示踪流体的浓度（c，单位：l/l）。对采集数据进行分析后，研究者得到了各配置视场范围内的相位平均量与相位解析协方差，并以.npy文件格式存储于对应文件夹中。此外，针对浓度与所有协方差量，基于距床面距离推导得到了时均与水平均剖面，该类剖面同样以.npy文件格式存储于对应文件夹中。

基于实测数据，结合对应研究论文中的方法，研究者为每一组配置推导得到了质量混合长度（Mass mixing length, l_c）与涡动扩散系数（Eddy diffusivity, D_t）剖面。上述变量以距床面距离为自变量，以.npy文件格式存储于对应文件夹中。基于线性回归得到的垂直质量混合长度剖面与涡动扩散系数剖面的斜率信息，已整理至`slopes_turbmodels.csv`文件中。

由于数据体量庞大，本数据集未包含时间解析数据，若有需求可联系作者获取。

### 压缩包内.npy文件说明
各文件夹中均包含时均水平均剖面文件，命名格式为`[c/RS/TF/TKE]_[可选：协变量名称]_time_averaged_[海底模型名称]_[波浪工况名称].npy`。所有时均数据的存储格式如下：
python
import numpy as np
data = np.load(filename.npy, allow_pickle=True)
# data[0]：z方向维度，单位：mm ## data[0][z]
# data[1]：对应物理量（c、雷诺应力（Reynolds stresses, RS）、湍流通量（turbulent fluxes, TF）、湍动能（turbulent kinetic energy, TKE）） ## data[1][z]

各文件夹中均包含相位平均量文件（命名格式为`[u/w/c]_phase_averaged_[海底模型名称]_[波浪工况名称].npy`）、脉动量的相位解析协方差文件（命名格式为`[RS/TF]_[协变量名称]_[海底模型名称]_[波浪工况名称].npy`，注：RS代表雷诺应力，TF代表湍流通量）以及湍动能文件（命名格式为`TKE_[海底模型名称]_[波浪工况名称].npy`）。所有相位解析数据的存储格式如下：
python
import numpy as np
data = np.load(filename.npy, allow_pickle=True)
# data[0]：x方向维度，单位：mm ## data[0][z, x]
# data[1]：z方向维度，单位：mm ## data[1][z, x]
# data[2]：对应物理量（c、u、w、雷诺应力（RS）、湍流通量（TF）、湍动能（TKE）） ## data[2][phi_idx, z, x]
# （相位平均采用100个相位箱，故phi_idx取值范围为0至99）

各文件夹中均包含基于对应论文方法推导得到的质量混合长度（l_c）与涡动扩散系数（D_t）剖面，文件命名格式为`[lc/Dt]_prof_[海底模型名称]_[波浪工况名称].npy`，存储格式如下：
python
import numpy as np
data = np.load(filename.npy, allow_pickle=True)
# data[0]：z方向维度，单位：mm ## data[0][z]
# data[1]：对应物理量（l_c单位：mm，D_t单位：m²/s） ## data[1][z]