Granular materials are an essential component of many fields, such as the medicine and agriculture industries, where their behavior is affected by the properties of constituent particles. The Discrete Element Method (DEM) is a potential technique used to describe the mechanical behavior of granular materials by making a mechanical model which describes the affected parameters, and one of these parameters is the shape of particles. It is an important characteristic that is represented by a sphericity index. In this study, the macro and micromechanical shear behaviors of granular materials are investigated using the Discrete Element Method (DEM). For this purpose, a three-dimensional (3D) program (EDEM) was developed on the basis of (DEM) and was used to model various particle shapes for a direct shear test. An assembly with a different particle center distance was prepared. The results showed a changing relationship between shear strength and the sphericity index, and micro-mechanical responses showed that particle shape affected the shape and the thickness of the shear zone.
Granules are used in various industries such as medicine and agriculture, and their behavior is influenced by the characteristics of the constituent particles. The Discrete Element Method (DEM) is a technique for characterizing the mechanical behavior of granular materials by building a mechanical model that describes the impacted parameters, including particle shape, which is being one of these parameters. As a result, the discrete element method is applied to investigate the macro-and micro-mechanical shear behavior of granular materials. For this study, a gravitational disposition for geometrical arrangement model has been used to model various triple particle sizes for a direct shear test using (EDEM®), which is a three-dimensional (3D) program based on (DEM). Different triple particle sizes were used to create an assembly. The results revealed that the size index affected the relationship between shear strength, angular velocity, dilation, coordination number (CN), and volumetric strain.
Granular materials are used in various industries, including pharmaceutical and agriculture, where the material properties of elements have an important impact on their flow behavior. Numerical codes based on the Discrete Element Method (DEM) are decisive for describing the flow of granular material. The DEM could investigate granular materials' macro and micro-mechanical shear behaviors. The commercial software EDEM® based on the DEM was utilized for this purpose. A gravitational disposition for the geometrical arrangement model has been performed in this study to model different particle sizes for a direct, simple shear test (DSST). The results indicated that referring to the size index (SI), a positive correlation occurred with the shear strength, dilation, volumetric strain, a negative correlation with the average particle angular velocity, and a neutral correlation with the coordination number (CN).
The presence of moisture content in a silo makes the preservation of a grain stock challenging especially when dealing with a large stock. This made it as a major concern for engineers to preserve large crops of grains and avoid huge losses. By installing a screw inside of a silo, the moisture problem could be avoided by stirring the loaded granular bed alongside aeration, also it could be effective to mix different types of loaded materials. The present work has sought to develop predictive models of discrete element method for mixing uniformity assessment when mixing wheat granules in a hopper-bottom screw mixer. The different factors being investigated are: initial configuration of particles, screw rotational direction, screw pitch length, screw diameter and screw rotational velocity. Findings regarding bed homogeneity were calculated using the nearest neighbor’s method. The best mixture was obtained when considering a side-wise filling type of particles ahead mixing and using a 20 mm screw diameter, 30 mm screw pitch and rotating the screw at 80 rpm speed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.