Abstract
• Accurate description of the flow inside emulsification devices is crucial. • This study compares LES and RANS predictions to DNS validation data for an HPH. • Both LES and RANS are able to predict the general outline of the flow. • LES shows slightly better performance than RANS in predicting dissipation rates. • Low computational cost of RANS keeps it an interesting supplementary tool. There is a large interest in predicting high-pressure homogenizer (HPH) valve hydrodynamics using CFD, in academic research and industrial R&D. Most of these studies still use two-equation RANS turbulence models, whereas only a few have used LES formulations. From a theoretical perspective, LES is known to be more accurate than RANS, especially in terms of estimating the dissipation rate of turbulent kinetic energy, which is the most important parameter needed for predicting efficiency using a population balance equation (PBE). However, LES also comes at a considerably higher computational cost. To choose the appropriate modelling approach, it is important to understand how much the accuracy and the computational cost increase between RANS and LES. This study provides the first validation of high-pressure homogenizer hydrodynamics, comparing RANS and a well-resolved LES to numerical experimental validation data of direct numerical simulation (DNS), on a model of the gap outlet jet. The LES does result in a higher accuracy throughout, but the differences are relatively small, when focusing on the flow inside the jet. When using the CFD results to predict maximum surviving drop diameter, the LES results in a relative error of 4.8% whereas the RANS leads to a relative error of 18%. Both errors are substantially smaller than those from a traditional scale-based equation instead of a CFD-PBE. When seen in the substantial reduction of computational time (a factor of 970), results illustrate how RANS could remain a viable supplementary technique for CFD modelling of HPHs, despite its many limitations. Best practice recommendations for obtaining this RANS performance is discussed.
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