Powder Technology

DEM-SPH Coupling Simulation Files

Tensile strength of the material according to its binder content

These data is the raw or processed simulation result of our numerical experiments.

Tensile strength of the material according to its binder content

These research data files includes the original Matlab code of the core algorithms in our paper: (1)'LBM_main_module.m' and 'DEM_force_compute.m' are the core code of the LBM and DEM modules introduced in Section 2. (2)'Initialize_mesh (with progressive scanning algorithm).m', 'Fast_intersection_judgment_algorithm.m' and 'Solid_volume_fraction_fast_algorithm.m' illustrate program implementations of the algorithms introduced in Section 3, which include the improved progressive scanning algorithm, fast intersection judgment algorithm and the proposed solid volume fraction fast algorithm. (3)'Porous_media_generation.m' is the module for generation random porous filter media in Section 5.

Data of the manuscript entitled “Preparation and characterization of a microencapsulated flame retardant and its flame-retardant mechanism in unsaturated polyester resins”.

In Torres-Serra et al, we propose a methodology based on cluster analysis of data sets including quantitative flow descriptors of a wide range of powders and grains, with application in the packaging industry. Our work has implications for objectifying the commonly qualitative process of selecting the most suitable bulk feeding technique in handling equipment design. The first data set DS1 (‘DS1.csv’) describes 174 materials characterised by 6 conventional material properties. The second data set DS2 (‘DS2.csv’) describes 11 representative materials, fully-characterised by 126 conventional and specialised variables. Numbering of the specialised variables in DS2 identifies average measurements of up to 20 new specialised material properties for 6 different test cases, as discussed in the associated article. The tables in ‘legends.pdf’ detail the legend of variables in the two data sets DS1 and DS2, comprising material property symbols and descriptions used in the associated article. The interactive MATLAB® figures ‘classes.fig’ and ‘clusters.fig’ show observations in DS1 projected into a reduced 3D space defined by PCA, corresponding respectively to figures Fig. 8a (feeder-type classification from industrial know-how) and Fig. 8b (flowability-based clustering) in the associated article.

The fragmentation analysis of each trial blast is attached in the data file.

The simulation results in Origin files, with figures.

CT scan data for three-dimensional visualization of the evolution of pores and fractures in reservoir rocks under triaxial stress.