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Experimental and Numerical investigation of a Nozzle at different operating conditions for a Clean in Place System
Author(s) -
Sivaramakrishnan Anand Sivaram,
Abubaker Harish,
Münsch Manuel,
Delgado Antonio,
Murcek Roman,
Boye Andre
Publication year - 2018
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201800248
Subject(s) - nozzle , computational fluid dynamics , solver , computer science , computer simulation , flow (mathematics) , process engineering , mechanical engineering , simulation , engineering , mechanics , aerospace engineering , physics , programming language
Selection and optimization of a Clean In Place (CIP) system is a complex process. The CIP nozzles used in the system plays a huge role in the efficiency of the entire system. Therefore, numerical investigations are widely employed by many industrial processes, especially in the hygiene critical industries, such as Food, Beverage and Pharmaceutical industries. An enormous quantities of cleaning agent and water are consumed on a daily basis during their cleaning processes. Finding a reliable and efficient design and operating condition of the cleaning nozzle based on understanding the internal and external flow properties and their optimization is a priority for these industries. This paper presents the comparison between experimental measurement and the numerical prediction of the flow characteristics of a currently available CIP nozzle which are widely used for the industrial cleaning in place application. The flow features are measured experimentally using the optical measurement technique, Phase Doppler Anemometry (PDA) system which is then numerically compared. Computations of the CIP nozzle system were conducted using a finite volume based commercial computational fluid dynamics (CFD) solver, StarCCM+ V12.04. The Eulerian based unsteady multiphase simulation is carried out using different numerical solvers and the best suitable and efficient one is presented here. The achievement of good agreement between the numerical predictions and the experimental results has presented the opportunity to understand the flow behaviour in unsteady state conditions. Based on these studies further optimization will be carried out with the help of some evolutionary algorithms or artificial neural networks.