DOI: 10.3303/CET2184037 Paper Received: 22 July 2020; Revised: 4 December 2020; Accepted: 9 February 2021 Please cite this article as: Brener A., Musabekova L., Dausheyeva N., 2021, On Modelling the Swarming in Dispersed Systems, Chemical Engineering Transactions, 84, 217-222 DOI:10.3303/CET2184037 CHEMICAL ENGINEERING TRANSACTIONS VOL. 84, 2021 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Paolo Ciambelli, Luca Di Palma Copyright © 2021, AIDIC Servizi S.r.l. I SBN 978-88-95608-82-2; I SSN 2283-9216 On Modeling the Swarming in Dispersed Systems Arnold Brener*, Leila Musabekova, Nurzhamal Dausheyeva Auezov University of South Kazakstan, Tauke Khan, 5, Shymkent, 160012, Kazakhstan amb_52@mail.ru The paper is devoted to modeling the swarming of micro and nanoparticles in dispersion flows. A systematic analysis of the swarming phenomenon is presented, and approaches to the creation of generalized swarming models for describing the mentioned phenomena are offered for discussion. Based on the review of various works and speculative analysis, for the first time it is proposed to distinguish three main different mechanisms of swarm formation in dispersed mixtures. These mechanisms determine the swarming processes in wide range of particles sizes: from nanoparticles to small dust particles. The results of computer simulation and numerical experiments for describing swarming phenomenon proceeding by the inertial mechanism using both the computer fluid dynamics (CFD) simulation and the stochastic lattices approach adapted especially in the submitted work for describing swarming phenomenon have been also submitted. 1. Introduction Since the term swarming is used in the study of various issues (Carrillo et al., 2010, Naldi et al., 2010), it is necessary to clarify that in this work, swarming is understood as the formation of moving areas with an increased concentration of the dispersed phase in flows (Bouffanais, 2016). This problem is clearly poorly understood from the point of view of creating generalized models and universal approaches (Rimer & Ariel, 2017). The swarming phenomenon can occur in various processes in which the particles of the dispersed phase are entrained by the flow of a continuous medium. Swarming is observed both in flows of microparticles and in nano-dispersed systems (Pirani et al., 2013). Applying to nanosystems the following examples can be noted: when modelling processes in intracellular environments (Weber et al., 2015), in biology and biotechnology when modeling the migration and aggregation of cellular systems (Brückner et al., 2019,), under modeling and design of mechatronic systems too (Su et al., 2018). 1.1 Speculative analysis of the phenomenon Hypothetically, three main different mechanisms of the formation of a swarm of particles in a continuous flow of a dispersed mixture can be proposed. The first swarming mechanism may be due to a change in the structure of the flow of a continuous carrier medium in the volume of the apparatus (Schmidt et al., 2006). Such a mechanism can apparently be called as inertial swarming. The second mechanism is due to the influence of singularities or attracting (repulsive) centers in the volume of the apparatus (Satyobroto Talukder, 2011). Such a mechanism can be called as attractive swarming. And, finally, the third mechanism of this phenomenon is due to the presence of interaction forces acting between the particles of the dispersed mixture in the flow of a continuous medium (Carranza & Coates, 2000). This mechanism can be called as interacting swarming. Each of the noted mechanisms has its own characteristic features (Bees et al., 2000). Inertial swarming can be accompanied by separation of the dispersion flow, i.e. (id est), by creating several swarms with different particle size and masses (orders) distribution functions. By this the initial general distribution function transforms into several functions for different swarms (Figure 1 (A)). The inertial swarming mechanism is used for the separation of polydisperse systems with microparticles and larger particles (Carranza & Coanes, 2000). Attractive swarming, in turn, can proceed according to various scenarios. The first, force scenario, is realized by the influence of the force changing particles trajectories in direction to or from the singularity dot (Figure 1 (B)). The second scenario can be called a signal scenario. In this case, the 217