*** Data underlying the publication: Characteristics of slurry transport regimes: Insights from experiments and interface-resolved Direct Numerical Simulations *** 

Authors: Tariq Shajahan, Thijs Schouten, Shravan K.R. Raaghav, Cees van Rhee, Geert Keetels, Wim-Paul Breugem,
TU Delft, Faculty of Mechanical, Maritime and Materials Engineering, Department of Process & Energy, Multiphase Systems group; and
TU Delft, Faculty of Mechanical Engineering, Department of Maritime and Transport Technology, Dredging Engineering 

Corresponding author: Wim-Paul Breugem

Contact Information:W.P.Breugem@tudelft.nl
Multiphase systems, Process and Energy, 3ME
Delft University of Technology
Mekelweg 2, 2628 CD, Delft, The Netherlands


*** General Introduction ***
This data set contains the data to corresponding to Tariq Shajahan, Thijs Schouten, Shravan K.R. Raaghav, Cees van Rhee, Geert Keetels, Wim-Paul Breugem, Characteristics of slurry transport regimes: Insights from experiments and interface-resolved Direct Numerical Simulations, International Journal of Multiphase Flow, 2024, 104831, ISSN 0301-9322,
https://doi.org/10.1016/j.ijmultiphaseflow.2024.104831.
(https://www.sciencedirect.com/science/article/pii/S0301932224001101)

In this comprehensive study, we performed experiments and interface-resolved Direct Numerical Simulations (DNS) of slurry flow in a horizontal pipe. The experiments were performed in a transparent flow loop with Dpipe = 4 cm. We measured the pressure drop along the pipeline, the spatial solid concentration distribution in the cross-flow plane through Electrical Resistance Tomography (ERT), and used a high-speed camera for flow visualization. The slurry consisted of polystyrene beads in water. The different flow regimes were studied by varying the flow rate, with Re varying from 3272 till 13830. The simulations were performed for the same flow parameters as in the experiments. Taking the experimental uncertainty into account, the results from the DNS and the experiments are in reasonably good agreement. The results for the pressure drop agree also fairly well with popular empirical models from literature. 
In addition, we performed a parametric DNS study in which we solely varied Re and Ga. In all flow regimes, a secondary flow of Prandtl’s second kind is present, ascribed to the presence of internal flow corners and a
ridge of densely packed particles at the pipe bottom during transition towards the fully-suspended regime. In the bulk of the turbulent flow above the bed, secondary flow transport of streamwise momentum dominates over turbulent diffusion in regions where the secondary flow is strong and vice versa where it is weak. 

*** Method : Direct Numerical Simulations (DNS) & Experiment ***
The flow problem was studied using a combination of experiments and Interface resolved Direct Numerical Simulations. The pipe geometry is implemented in the rectangular domain using a volume penalization method and periodic boundary conditions are applied at the ends of the pipe. The DNS is carried out  rectangular domain filled with a viscous fluid in which immersed non-colloidal spherical particles are subjected to a crossflow along the pipe. The two phases in the simulation (fluid and particulate) are treated independently and coupled through a no-slip boundary condition enforced on the surface of the particle. The solution to the fluid phase is computed on a fixed Eulerian mesh and the moving surface of the particle is represented using a Lagrangian mesh that translates with the particle. The simulation code is developed in house and written in FORTRAN90. MATLAB, PYTHON and PARAVIEW has been used to generate the figures presented in the article.
The Experimental data used for comparison is also included in the dataset.


*** Data and scripts to generate figures ***
The data is characterized in the directories: MATLAB, PYTHON, and VISUALIZATION.
Each directory has a designated file with further information on each plot.
Simply running the scripts will generate all the plots in the abovementioned article.
The underlying data is listed in columns, where the DATA is typically stored in the ".txt" format.

1. "plotDATA.m" is a MATLAB script to generate the 1D plots in the figure;
2. The python scripts to generate the 2D plots are labelled according to the figure number: Fig6.py, Fig8.py, Fig.11.py, Fig13.py and Fig17.py; and 
3. The data for visualizating the position and velocity of particles for cases D1-D3 is provided, and further "visualization movies" corresponding to all cases are provided in MP4 and AVI format 