Can terminal settling velocity and drag of natural particles in water ever be predicted accurately?

Abstract

Abstract. Natural particles are frequently applied in drinking water treatment processes in fixed bed reactors, fluidised bed reactors, and sedimentation processes to clarify water and to concentrate solids. When particles settle, it has been found that, in terms of hydraulics, natural particles behave differently when compared to perfectly round spheres. To estimate the terminal settling velocity of single solid particles in a liquid system, a comprehensive collection of equations is available. For perfectly round spheres, the settling velocity can be calculated quite accurately. However, for naturally polydisperse non-spherical particles, experimentally measured settling velocities of individual particles show considerable spread from the calculated average values. This work aims to analyse and explain the different causes of this spread. To this end, terminal settling experiments were conducted in a quiescent fluid with particles varying in density, size, and shape. For the settling experiments, opaque and transparent spherical polydisperse and monodisperse glass beads were selected. In this study, we also examined drinking-water-related particles, like calcite pellets and crushed calcite seeding material grains, which are both applied in drinking water softening. Polydisperse calcite pellets were sieved and separated to acquire more uniformly dispersed samples. In addition, a wide variety of grains with different densities, sizes, and shapes were investigated for their terminal settling velocity and behaviour. The derived drag coefficient was compared with well-known models such as the one of Brown and Lawler (2003). A sensitivity analysis showed that the spread is caused, to a lesser extent, by variations in fluid properties, measurement errors, and wall effects. Natural variations in specific particle density, path trajectory instabilities, and distinctive multi-particle settling behaviour caused a slightly larger degree of the spread. In contrast, a greater spread is caused by variations in particle size, shape, and orientation. In terms of robust process designs and adequate process optimisation for fluidisation and sedimentation of natural granules, it is therefore crucial to take into consideration the influence of the natural variations in the settling velocity when using predictive models of round spheres.

Tuning particle settling in fluids with magnetic fields

“…Fluctuations of this order of magnitude match those present in the non-magnetic spheres reference case. Hence the fluctuations seen in the figure are attributed to the usual variability of particles settling 9,10,35 , therefore no major effect of rotation blockage is seen on the terminal velocity.…”

“…The rich dynamics of single spheres settling in quiescent flows has been extensively explored in previous studies [5][6][7][8][9][10]32 , in particular the transitions between different settling regimes that depend on the relative importance of gravity and viscous effects (parameterized by the Galileo number) have been determined. This problem offers an appealing framework to validate the magnetic method to compensate gravity and revisit numerical simulation predictions 8,33,34 as well as previous experimental results in the literature 6,9,10,35 with an alternative method to explore the parameters space.…”

New model for predicting terminal settling velocity and drag coefficient of the Oncomelania

“…Further, the expression form of R ep and ψ has been determined (Kramer et al, 2021;Wang et al, 2018),…”

“…Few models for the prediction accuracy of terminal settling velocity and drag coefficient cover the wide range of differences in particle properties with a low prediction inaccuracy. The prediction accuracy of the models was derived for nonspherical particles (Ganser, 1993;Haider and Levenspiel, 1989;Hölzer and Sommerfeld, 2008;Ouchene et al, 2016), using the sphericity as a shape descriptor, was not significantly improved for drinking-water-related granules (Kramer et al, 2021).…”