A method for advanced stochastic generation of particles in granular flow simulations and its practical applications in industry

Ida Critelli

EnginSoft SpA

A. Tasora
University of Parma

M. Colledani
Politecnico di Milano

An emerging frontier of multi-body dynamics deals with the implementation of particle dynamics into larger rigid/flex multibody models. Reliability of such kind of simulations, strongly relies on particle properties such as shape, size, and surface response. Moreover, since true systems normally include particles of multiple types, properties should be defined through stochastic approaches.

Common methods exposed in literature have intrinsic limits, making not possible the setup of flows involving heterogeneous and multi-disperse particle types. To address this problem, we leverage on object-oriented programming and propose a novel, efficient and systematic approach to the parametric generation of particles. Particles are sorted from a probability space defined through a tree-like data structure, where the tree nodes represent sub-classes of material mixtures and the leaves are the particle generators. The hierarchical structure of the program provide maximum flexibility to generate almost any type of particle mix, while controlling most properties in accordance to predefined statistical distributions. The way that particle families, generators and statistical distributions are assembled together can be defined by an optional configuration file based on the JSON serialization format.

We tested this framework within our multibody simulation software whose formulation, based on Differential Variational Inclusions (DVI), can target problems with a massive number of particles with frictional contacts. The proposed examples highlight the power of the method and the quality of the outputs. Likely, the proposed technology can be coupled with other (commercial) multibody codes, expanding the existing limits for particle simulation.

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