DEM simulations of bi-disperse beds during bedload transport

This depository contains the data of all DEM simulations used in the publication Chassagne, R., Frey, P., Maurin, R., and Chauchat, J. Mobility of bidisperse mixtures during bedload transport. Physical Review Fluids, 5(11):114307. doi:10.1103/PhysRevFluids.5.114307, as well as post processing scripts to use the data. The simulations are located in seven folders, Monodisperse/ (mondisperse simulations where the fluid forcing is varied), N0.5/ (simulations with 0.5 layer of large particles above a small particle bed and or different fluid forcing), N1/ (simulations with 1 layer of large particles above a small particle bed and or different fluid forcing), N2/, N3/, N4/ and sizeRatio (2 layers of large particles, fixed fluid forcing but the diameter of the underlying small particles is varied). The data of each simulations are contained in separate subfolders named after the simulation. For example, H8Sh0.45/ corresponds to a monodisperse simulation with a bedheight of 8dl (dl is the large particle diameter) and a shields number of 0.45. H10N2R2Sh0.7/ corresponds to a bidisperse simulation with a bed height of 10dl, 2 layers of large particles, a size ratio of 2 between large and small particles and a shields number of 0.7. For each simulation, the time data are saved in data.hdf5 and averaged data in average.hdf5. A GeomParam.txt file is also in each folder. It contains information of the simulation that the post processing programm will read. The python script used to initiate the YADE-DEM simulation is also given for information (it contains all parameters of the simulation). The post-processing programm has been coded in python2.7 with an oriented-object procedure. The h5py package is necessary to read the .hdf5 files. The scripts do not work in python3, but can be very easily adapted if necessary (you only have to modify the "print" functions). The scripts are available in ScriptsPP/ and are organized as follow. For bidisperse simualtions, a mother class in SegregationPP and two child classes SegFull (to load the full time data set) and SegMean (to load only average data). For monodisperse simualtions, a mother class in MonodispersePP and two child classes MonoFull (to load the full time data set) and MonoMean (to load only average data). Two scripts examplePP1.py and examplePP2.py are proposed and show how to manipulate theses classes and the data.