Verification Of Turbulence Models For Flow In A Constricted Pipe At Low Reynolds Number

Keywords

Biomedical systems; Computational fluid dynamics (CFD); Large eddy simulation (LES); Laser Doppler Anemometry (LDA); Reynolds-Averaged Navier-Stokes (RANS); Scale resolving simulation; Stenosis; Turbulence modelling

Abstract

Computational fluid dynamics (CFD) is a useful tool for prediction of turbulence in aerodynamic and biomedical applications. The choice of appropriate turbulence models is key to reaching accurate predictions. The present investigation concentrated on the comparison of different turbulence models for predicting the flow field downstream of a constricted pipe. This geometry is relevant to arterial stenosis in patients with vascular diseases. More specifically, the results of Unsteady Reynolds-Averaged Navier-Stokes (URANS) and scale resolving simulation (SRS) turbulence models such as Large Eddy Simulation (LES) and Detached Eddy Simulation (DES) were compared with experimental measurements. Comparisons included the mean flow and fluctuations downstream of the constriction. Results showed that the LES model was in better agreement with the velocity measurements performed using a Laser Doppler Anemometry (LDA). In addition, although URANS models predicted a wake region size and mean flow velocities comparable to SRS turbulence models, no small-scale vortical structures can be observed in the URANS solution due to the nature of these models. Modeling of these structures would, however, be helpful when more detailed flow behavior is needed such as in studies of acoustic sources. Hence, LES would be an optimal turbulence model for the flow under consideration, especially when sound generation would be of interest.

Publication Date

1-1-2018

Publication Title

Proceedings of the Thermal and Fluids Engineering Summer Conference

Volume

2018-March

Number of Pages

1865-1874

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1615/TFEC2018.tfl.021662

Socpus ID

85056084262 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85056084262

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