화학공학소재연구정보센터
Chemical Engineering Science, Vol.177, 340-353, 2018
A validation of commonly used CFD methods applied to rotor stator mixers using PIV measurements of fluid velocity and turbulence
Computational fluid dynamics (CFD) has been applied extensively for studying rotor-stator mixers (RSM) in the past, both as a design-tool and in modelling mixing and emulsification. Modelling is always a balance between accuracy and computational cost. The theoretically soundest methods (i.e. fully resolved transient simulations) have often been deemed unfeasible, and the majority of previously published studies use severe simplifications (i.e. k-epsilon models for turbulence and multiple reference frame for rotation). High quality experimental validation is in great need, but are rare, due to the lack of local fluid velocity measurement. Experimental validations of CFD on RSMs have previously been provided using laser Doppler anemometry. This study provides the first validation using particle image velocimetry, allowing for substantially higher spatial resolution than with the previously used techniques. The objective of this study is to map the possibilities and limitations of these commonly used CFD modelling approaches for RSMs. Special emphasis is put on validating the dissipation rate of turbulent kinetic energy (TKE). Despite being the parameter used for linking CFD to mixing or dispersion models, this has not been the subject of experimental validation in previous studies. Based on the validations, a list of best practice recommendations are given (in terms of turbulence model, mesh resolution and rotation formulation). When adhering to these, the CFD model accurately captures power draw, flow number, and the detailed velocity field inside the region where mixing and dispersion takes place. The dissipation rate of TKE is captured qualitatively but underestimate experimental values. Implications in terms of limitations are discussed in detail, including estimations of accuracy implications for emulsification and mixing modelling. (C) 2017 Elsevier Ltd. All rights reserved.