Poster, NJIT, Newark, NJ, USA
The onset of shear thickening and, ultimately, jamming occurs due to the formation of large-scale microstructures within the suspension. Although these events are rare, they are significant enough to counterbalance the imposed shear stress in dense suspensions, leading to the development of frictional contact networks that resist further deformation.
Conference talk, Downtown Marriott, Austin, TX, USA
Discontinuous shear thickening (DST) in suspensions occurs when stress drives particles into frictional contact networks, setting a threshold for shear-induced contacts. Using an established simulation technique [1], we show that contact networks grow with solid fraction and fluctuate under flow. As jamming nears, rigid clusters emerge with 2D Ising-like critical behavior. We analyze scaling in mono- and bidisperse suspensions (up to a 4:1 size ratio) and examine how shear and normal stresses relate to rigid structure formation.
Invited Talk, CCNY, New York, NY, USA
Dense non-Brownian suspensions, found in industries like cement and chocolate, exhibit complex flow behavior influenced by shear and particle interactions. Using LF-DEM, we study 2D bidisperse systems, modeling polydispersity and jamming transitions. Employing the pebble game algorithm, we analyze rigidity percolation, particle contacts, and rheology to understand the formation of rigid clusters. Watch the talk here
Talk, South Hall Convention Center, Las Vegas, NV, USA
We study arches that form during clogging in a 3D system of soft hydrogel particles (bulk modulus 10-30 kPa) in water flowing through a square cross-section tube with a circular outlet. The particles are dyed with a fluorescent dye and a laser sheet is used to locate the particles in 3D when the system clogs. The tube’s dimensions are 7.62 cm x 7.62 cm x 25.4 cm. The mean particle diameter is 1.46 cm. We vary the total number of particles from 80-125 and the diameter of the outlet from 2-3.5 cm. We measure the clogging probability, and the 3D particle positions in the clog to identify 3D arches. We find that the number of particles in the clogged arch depends on the width of the orifice and the number of particles.