Comparison of different methodologies for estimating local density in particle-based simulations

Knowledge about the local relative density and porosity over a randomly packed particle system is important as it provides valuable insights into the system’s behaviour, as several numerical simulations rely on this information. In this study, different methods of estimating the local density of particle systems are compared and contrasted. These methods depend on particle-based data and can be applied to a wide variety of particle systems, including granular materials and powders. The first method divides the volume of interest along an axis; the second along two and three axes; the third is based on the probability density function of kernel densities (KDE); the fourth uses the Caley–Menger method; the fifth uses the method presented by Strobel; and the sixth performs a Delaunay triangulation of the volume, where two different implementations are investigated. In this study, discrete element method (DEM) simulations are performed to produce the dataset used for comparison. Randomly packed particles with varying radii within a predefined region of interest (ROI) are considered for simulations. The aforementioned methods are then employed to estimate the local relative density at any point in the ROI. The results indicate that the method based on the Voronoi tessellation provides the most accurate local density estimation.