Experimental Inference of Inter-Particle Forces in Granular Systems Using Digital Image Correlation

Abstract

The observed features of granular materials can be successfully predicted using models based on relationship of inter-particle forces and macroscopic properties. In the current work, a drop-tower experimental setup was developed for the impact testing of 2D assembly of cylinders and 3D assembly of spheres with impactor velocity of around 6 m/s. This drop tower setup was used to load 2D granular assembly of polyurethane and polycarbonate cylinders of 1.25″ length with 3 different diameters of 1/4″, 3/8″ and 1/2″. A high speed camera was used for recording the images of approx. 800 × 800 resolution at speeds between 10,000 and 15,000 fps to monitor the deformation of the cylinders. The recorded images were used to obtain the granular fabric and kinematics for each grain and average strains were obtained from images using digital image correlation. The experimental data was subsequently used to infer the interparticle forces between individual grains in the assembly using a Granular Element Method based optimization process.

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