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Spherical harmonic-based random fields based on real particle 3D data: Improved numerical algorithm and quantitative comparison to real particles

Liu, X.
Garboczi, E. J.
Grigoriu, M.
Lu, Y.
Erdoğan, Sinan Turhan
The shape of particles often plays an important role in how they are used and in the properties of composite systems in which they are incorporated. When building models of systems that include real particles, it is often of interest to generate new, virtual particles whose 3D shape statistics are based on the 3D shape statistics of a collection of real particles. A previous paper showed mathematically how this can be carried out, but only had a small set of real particle shape data to use and only made a limited amount of qualitative comparisons to the real particle data. The present paper shows how the numerical method used to create virtual particles has been improved and immensely accelerated, allowing the use of large particle datasets. Making use of several large particle shape datasets, the paper confirms that the algorithm creates particles whose statistical shape properties closely match the real particles from which they were generated. Another question that can now be addressed with these larger particle datasets is: how many real particles are enough to be representative of the particle class from which they were drawn? The types of particles analyzed include two size ranges of crushed granite-hornblende rocks, silica sand, calcium carbonate powder, and ground granulated blast furnace slag. Published by Elsevier B.V.