Wave tank experiments with velocity and tracer concentration measurements above gravelly beds

# Author: Helena Stirnweiß# Affiliation: Institute of Fluid Mechanics (LSM), University of Rostock, Rostock, Germany# Date Created: 01. August. 2024# Contact: [e-mail] (alternative: [e-mail])# DOI: 10.5281/zenodo.17720136 Proper citation is required when using this dataset in any publication or project. # 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'. The dissipation rate of turbulent kinetic energy (epsilon) was estimated from the time-resolved PIV data based on the method (assumption of local isotropy, also referred to as 'direct' estimate) in Doron et al. (2001, doi.org/10.1175/1520-0485(2001)031<2108:TCADEI>2.0.CO;2). # Description of .npy files in Folders: The time-averaged, horizontally averaged profiles (named '[c/RS/TF/TKE/epsilon]_[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), the turbulent kinetic energy (named 'TKE_[name of seabed model]_[name of wave scenario].npy'), and dissipation rate of TKE (named 'epsilon_[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] TIME-RESOLVED DATA IS NOT PROVIDED IN THIS DATASET DUE TO EXTENSIVE DATA SIZE BUT CAN BE SHARED UPON REQUEST. Please use the contact listed above.