+91-512-259-7629
Associate Professor
anuragt[AT]iitk.ac.in
+91-512-259-6591 (Office)
Faculty Building 465
Our research group focuses on understanding the behaviour of bulk solids with a special focus on granular materials. This category of substances consists of numerous distinct solid particles. Examples include grains, cereals, pulses, sand, coal, etc. Though encountered commonly in a variety of natural and industrial settings, understanding and predicting their flow and mixing behaviour is a challenge. The varied flow behaviour that these substances can display contributes to the difficulty of predicting their behaviour. Investigating the rheology of granular materials in these different flow regimes is an active area of research in our group. Using discrete element method simulations, we obtain the constitutive equations that can accurately describe the flow of granular materials in different regimes. The flow behaviour and rheology of cohesive and wet granular materials is also being investigated.
Another aspect of our research is aimed at understanding and predicting the mixing and segregation phenomena in granular mixtures. The fundamental understanding of segregation of binary mixtures due to difference either in density or in size based on computation of particle level forces by our group enables accurate predictions of various flow properties of interest in simple shear flows. Combined effect of size and density difference on segregation of mixtures are currently being investigated. In addition, efforts to extend the theory for predicting flow and segregation of multi-component mixtures are being pursued.
Quantitative simulations of powders and other bulk solids for existing industrial operations/equipment is another focus area of our research. A systematic approach, utilising direct experimental measurements of particle level properties using table top experiments, image processing as well as those at the level of the bulk material enable determination of various DEM parameters for quantitative simulations and significantly reduce the number of iterations required using the popular trial-and-error method of bulk calibration.