Application of hybrid RANS-LES models to the prediction of mixing time and residence time distribution: Case study of a draft tube reactor. (31st August 2021)

• Record Type: Journal Article
• Authors: Brown, Gary J.; Fletcher, David F.; Leggoe, Jeremy W.; Whyte, David S.
• Is Part Of: Chemical engineering science. Volume 240(2021)
• Publication Date: 2021-08-31
• Subjects: CFD -- Hybrid RANS-LES models -- Residence time -- Mixing time -- Stress blended Eddy simulation -- Draft tube reactor; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.116676
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: Mixing time, average and range of residence time behaviour investigated with CFD. URANS models over-predict mixing time by a factor of ~2×. SST-SAS and SBES hybrid RANS-LES models produce very similar predictions. Hybrid models show good agreement with experimental data. Simulation times with hybrid models considered practical for industrial simulation. Abstract: Residence time and mixing time predictions were investigated using two hybrid RANS-LES models (SBES and SST-SAS) and compared with experimental data and URANS approaches for the case study of a laboratory-scale draft tube reactor. The URANS models are found to over-predict the mixing time by a factor of ~2× and to poorly predict the exit residence time behaviour. In contrast, the two scale-resolving models are found to produce similar predictions which are in good agreement with the experimental data for the mixing time and the shape of the average exit residence time curve. The use of multiple tracers in the scale-resolving simulations shows that both models can also capture the range of residence time behaviours observed in the experiment. The results achieved are considered applicable to a broad range of flows and the simulation times required to achieve good predictions with the scale-resolving models are considered practical for industrial simulation.