Numerical modelling of granular flows: a reality check

Abstract

Discrete particle simulations provide a powerful tool for the advancement of our understanding of granular media, and the development and refinement of the multitudinous techniques used to handle and process these ubiquitous materials. However, in order to ensure that this tool can be successfully utilised in a meaningful and reliable manner, it is of paramount importance that we fully understand the degree to which numerical models can be trusted to accurately and quantitatively recreate and predict the behaviours of the real-world systems they are designed to emulate. Due to the complexity and diverse variety of physical states and dynamical behaviours exhibited by granular media, a simulation algorithm capable of closely reproducing the behaviours of a given system may be entirely unsuitable for other systems with different physical properties, or even similar systems exposed to differing control parameters. In this paper, we focus on two widely used forms of granular flow, for which discrete particle simulations are shown to provide a full, quantitative replication of the behaviours of real industrial and experimental systems. We identify also situations for which quantitative agreement may fail are identified, but important general, qualitative trends are still recreated, as

Energy decay in a tapped granular column: Can a one-dimensional toy model provide insight into fully three-dimensional systems?

“…This may prove particularly valuable in predicting and characterizing the behaviors of systems where the numbers of particles or the physical extent of the system may render full parameter studies via simulation or experiment unfeasible. Since, due to the limitations of contemporary computational hardware and experimental techniques, a majority of industrial particle-handling systems currently fall within this category [8], this approach may indeed prove highly useful.…”

“…While we are currently witnessing a continual improvement in the speed and power of simulational techniques for modeling granular systems [8] as well as in the capabilities of experimental techniques, we are still a long way from being able to reliably analyze the behaviors of large, three-dimensional systems with precision. However, within the limitations of contemporary technology, we can successfully model small, often one-dimensional or quasi-two-dimensional toy systems to a high degree of precision, producing simulations and models that accurately and quantitatively represent observations from similar experimental systems [9][10][11].…”

Positron Emission Particle Tracking for Liquid‐Solid Mixing in Stirred Tanks

“…A major disadvantage of this methodology, however, is that it is extremely computationally expensive [41], especially in the case of two-way coupling and finely resolved flow, thus limiting its suitability for simulating large, industrial-scale systems. Nonetheless, as algorithms become more efficient and hardware more powerful, Euler-Lagrange methodologies are expected to prove increasingly valuable in the coming years and decades [42].…”

“…While the above-described numerical modeling techniques are very powerful and allow a great deal of detailed information to be extracted from a system, if their results are to be meaningful, they require -for both Euler-Lagrange and Euler-Euler approaches -extensive calibration and validation against similarly detailed experimental data [42,43]. High-resolution, 3D imaging methodologies, such as the PEPT technique, which is described in the coming sections, represent an ideal source of such data [44].…”

Interrelation of structural and kinematic characteristics during free-surface gravity flows of granular materials

“…Savage [5]; Brennen [6]; Forterre and Pouliquen [7]; Pudasaini [8]; Domnik et al [9], Windows-Yule et al [10]. However, as it was truly noted by Jesuthasan and Savage [11], Hill and Fan [3], so far mathematical models of granular flows have not been fully established and, furthermore, the united model of such flows is not available [7,10]. Thus, there is a need for reliable experimental data that can be used for verification of the mathematical models.…”