Application of hybrid RANS-LES models to the prediction of flow behaviour in an industrial crystalliser

Gary J. Brown, David F. Fletcher, Jeremy W. Leggoe, David S. Whyte

Research output: Contribution to journalArticle

Abstract

Knowledge of the residence time and shear rates in industrial crystallisers is critical for any assessment of the performance of these vessels from a chemical engineering perspective. It is unlikely that the range of expected residence time behaviours, or the shear rates can be predicted accurately with a RANS model. In the current study, a wide range of hybrid RANS-LES models, including Stress-Blended Eddy Simulation (SBES), were used to predict the flow field in a laboratory-scale alumina precipitator with the objectives of both quantifying the accuracy of the models and assessing if the hybrid models provide significant improvement over a previous RANS modelling study. Predicted mean and fluctuating velocities have been compared with Laser Doppler Velocimetry (LDV) data from a laboratory-scale alumina precipitator. The results achieved show that hybrid RANS-LES models can accurately predict both the mean and fluctuating velocities in the precipitator vessel. Importantly, as the mesh is refined, agreement with experimental data improves and differences between model predictions reduce, showing that sensitivity to the sub-grid scale model reduces if all the relevant large-scale turbulence structures are explicitly resolved. Prediction of fluctuating velocities is found to be more accurate than that achieved in a previous RANS modelling study. The SBES results are found to be mesh-independent, and to give closer agreement with experimental data, on a coarser mesh than both the SST-DDES and SST-SAS approaches as the model formulation allows rapid transition to an explicit LES model immediately outside the wall boundary layer. This is an important result for industrial simulation due to the significant reduction in simulation times needed.

Original languageEnglish
Pages (from-to)1797-1819
Number of pages23
JournalApplied Mathematical Modelling
Volume77
DOIs
Publication statusPublished - 1 Jan 2020

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