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Link between Single-Particle Properties and Macroscopic Properties in Particulate Assemblies: Role of Structures within Structures
S. J. Antony
Philosophical Transactions: Mathematical, Physical and Engineering Sciences
Vol. 365, No. 1861, Chemistry and Engineering (Dec. 15, 2007), pp. 2879-2891
Published by: Royal Society
Stable URL: http://www.jstor.org/stable/25190630
Page Count: 13
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The prevalence of particulate materials in modern industrial processes and products provides a significant motivation to achieve fundamental understanding of the bulk behaviour of particulate media. The rapid progress being made with atomic force microscopy and related particle characterization techniques pushes the limits of micro- and nanotechnologies such that interparticle interactions can be engineered to fabricate particulate assemblies to deliver specific functionalities. In this paper, primarily based on discrete element method simulations that we performed over the past 10 years, we summarize the key findings on the role of force transmission networks in dense particulate systems subjected to shearing. In general, the macroscopic strength characteristics in particulate systems is dictated by the distribution of heavily loaded contacts, also referred to as 'strong' force chains. Surprisingly, they constitute only a limited proportion of all contacts in particulate systems. They act like a 'granular brain' (memory networks) at particle scale. We show that the structural arrangement of the force chains and their evolution during loading depends on the single-particle properties and the initial packing condition in particulate assemblies. Further, the 'nature' offorce chains in sheared granular media induces larger 'solid' grains to behave like 'fluid' particles, retarding their breakage. Later, we probe for ways by which we can control the signature of memory networks in packed beds, for example by applying an external electrical field in a densely packed particulate bed subjected to shearing (combined electromechanical loading). Though further research is required to account for more realistic conditions and preferably to allow particles to self-organize to strength specifications, understanding the hidden memory networks in particulate materials could be exploited to optimize their collective strength.
Philosophical Transactions: Mathematical, Physical and Engineering Sciences © 2007 Royal Society