%0 Journal Article
%T Study of Swarm Behavior in Modeling and Simulation of Cluster Formation in Nanofluids
%A Mohammad Pirani
%A Hassan Basirat Tabrizi
%A Ali Farshad
%J Modelling and Simulation in Engineering
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/142165
%X Modeling the multiagents cooperative systems inspired from biological self-organized systems in the context of swarm model has been under great considerations especially in the field of the cooperation of multi robots. These models are trying to optimize the behavior of artificial multiagent systems by introducing a consensus, which is a mathematical model between the agents as an intelligence property for each member of the swarm set. The application of this novel approach in the modeling of nonintelligent multi agents systems in the field of cohesion and cluster formation of nanoparticles in nanofluids has been investigated in this study. This goal can be obtained by applying the basic swarm model for agents that are more mechanistic by considering their physical properties such as their mass, diameter, as well as the physical properties of the flow. Clustering in nanofluids is one of the major issues in the study of its effects on heat transfer. Study of the cluster formation dynamics in nanofluids using the swarm model can be useful in controlling the size and formation time of the clusters as well as designing appropriate microchannels, which the nanoparticles are plunged into. 1. Introduction Swarming, as a novel approach in modeling the dynamics of multiagent systems, inspires from the behavior of biological self-organized and decentralized systems that cooperate to do a special task. Decentralization means that the swarm has no central leader or boss and each member does its work with a kind of imitation. First attempts to describe the behavior of such biological systems from the mathematical points of view belong to Breder [1] who developed the motion equation of schools of fish and claimed that the motion of each member is the resultant of a long-range attraction and short-range repulsion components. Gazi and Passino [2, 3] described the stability of swarm systems. Many efforts have been investigated to introduce the behavior of multiagent systems whose members have mechanical interaction with each other. It means they may collide to one another and exchange some momentum or arrange in special configurations that can be seen in cluster formations from multiphase and granular flows to nanofluids. A particle dynamic description of solid particles in multiphase flows is one of the aspects of multiagent mechanical systems. Therefore, many attempts were dedicated to describe the behavior of particles in multiphase flows (see Hase [4], Li and Kuipers [5]). Dorigo et al. [6] introduced an optimization method based on the dynamics of the swarm of ant
%U http://www.hindawi.com/journals/mse/2013/142165/