Numerical analysis is a branch of mathematics that applies designing methods which give approximate but accurate numeric solutions to problems that have no exact solution. The discrete element method (DEM) is a numerical technique used in computational mechanics and physics to analyze the behaviour of systems consisting of discrete, interacting particles. It is commonly employed to study the behaviour of granular materials, such as pharmaceutical powders, grains, rocks, and other particulate systems.
In the discrete element method, the particles are considered as individual entities that interact with each other through contact forces. These forces can include friction, cohesion, repulsion, and other relevant physical interactions. By modelling the behaviour of each particle and its interactions, the collective behaviour of the entire system can be simulated and analyzed.
FEM vs DEM It is worth noting here that Finite Element Method (FEM) is distinct from DEM. FEM is a numerical technique primarily used for analyzing the behaviour of structures and continua, such as solid bodies, fluids, and heat transfer. FEM is widely used for structural analysis, heat transfer, fluid flow, and various multiphysics problems. On the other hand, DEM is specifically designed for simulating the behaviour of discrete, interacting particles or granular materials. In DEM, individual particles are treated as discrete entities with their own properties and interactions. It is commonly used to study granular materials and other particulate systems, including their behaviour under different loading and boundary conditions. |
DEM Methodology
The DEM typically involves the following steps:
By repeating these steps, the discrete element method allows researchers and engineers to study a wide range of phenomena, including the behaviour of granular materials under different loading conditions, the flow of particles in hoppers or silos, the compaction of powders, and the stability of structures composed of discrete elements.
DEM has found applications in various fields, including civil engineering, mining, geotechnical engineering, pharmaceuticals, and agriculture, where the understanding of particle interactions and their effects on system behaviour is crucial.
The computational demands of the discrete element method (DEM) are indeed significant, and advancements in computing technology, particularly faster CPUs and higher computing power, have played a crucial role in making DEM simulations more feasible and efficient. Furthermore, the availability of high-performance computing (HPC) resources, such as clusters or cloud-based computing platforms, has further accelerated the execution of DEM simulations. HPC systems provide access to large-scale computational resources, allowing researchers to distribute the computational workload across multiple processors or nodes, thus reducing simulation time.
DEM in the Pharmaceuticals Sector
Let us now see how DEM serves the pharmaceuticals sector. It is particularly useful in the analysis and design of particulate systems and processes. Here are some ways DEM is used in pharmaceuticals:
These are just a few examples of how DEM is applied in pharmaceuticals. By leveraging DEM simulations, pharmaceutical researchers and engineers can gain a deeper understanding of particulate systems, optimize manufacturing processes, improve product quality, and enhance drug delivery systems. DEM serves as a valuable tool for studying the behaviour of pharmaceutical powders, granules, tablets, and other particulate materials, aiding in the development and production of safe and effective pharmaceutical products.
There are several commercially available software packages that offer discrete element method (DEM) capabilities. As an example, EDEM for Altair provides a co-simulation capability within the Altair HyperWorks platform. EDEM accurately simulates and analyzes the behaviour of powders, tablets and capsules. It can provide key insight into operations and processes otherwise difficult or impossible to obtain using experiments alone. This increases process efficiency and capability, improves product quality, reduce prototyping costs, and helps pharmaceutical manufacturers get products to the market quicker.