NUMERICAL SIMULATION OF FLOW AND LOCAL SCOUR AROUND A CYLINDER USING VARIOUS TURBULENCE MODELS IN RANS

Author(s):  
Yuki KAJIKAWA ◽  
Ryoya MORIYAMA ◽  
Masamitsu KUROIWA ◽  
Masahide TAKEDA ◽  
Ain Natasha Balqis
Author(s):  
Alessandro Chiarini ◽  
Maurizio Quadrio

AbstractA direct numerical simulation (DNS) of the incompressible flow around a rectangular cylinder with chord-to-thickness ratio 5:1 (also known as the BARC benchmark) is presented. The work replicates the first DNS of this kind recently presented by Cimarelli et al. (J Wind Eng Ind Aerodyn 174:39–495, 2018), and intends to contribute to a solid numerical benchmark, albeit at a relatively low value of the Reynolds number. The study differentiates from previous work by using an in-house finite-differences solver instead of the finite-volumes toolbox OpenFOAM, and by employing finer spatial discretization and longer temporal average. The main features of the flow are described, and quantitative differences with the existing results are highlighted. The complete set of terms appearing in the budget equation for the components of the Reynolds stress tensor is provided for the first time. The different regions of the flow where production, redistribution and dissipation of each component take place are identified, and the anisotropic and inhomogeneous nature of the flow is discussed. Such information is valuable for the verification and fine-tuning of turbulence models in this complex separating and reattaching flow.


1994 ◽  
pp. 71-79 ◽  
Author(s):  
Shoji Fukuoka ◽  
Kunihiro Tomita ◽  
Tetsuo Hotta ◽  
Tomohiro Miyagawa

2017 ◽  
Vol 64 (3) ◽  
pp. 401-418 ◽  
Author(s):  
Mateusz Jędrzejewski ◽  
Marta Poćwierz ◽  
Katarzyna Zielonko-Jung

Abstract In the paper, the authors discuss the construction of a model of an exemplary urban layout. Numerical simulation has been performed by means of a commercial software Fluent using two different turbulence models: the popular k-ε realizable one, and the Reynolds Stress Model (RSM), which is still being developed. The former is a 2-equations model, while the latter – is a RSM model – that consists of 7 equations. The studies have shown that, in this specific case, a more complex model of turbulence is not necessary. The results obtained with this model are not more accurate than the ones obtained using the RKE model. The model, scale 1:400, was tested in a wind tunnel. The pressure measurement near buildings, oil visualization and scour technique were undertaken and described accordingly. Measurements gave the quantitative and qualitative information describing the nature of the flow. Finally, the data were compared with the results of the experiments performed. The pressure coefficients resulting from the experiment were compared with the coefficients obtained from the numerical simulation. At the same time velocity maps and streamlines obtained from the calculations were combined with the results of the oil visualisation and scour technique.


2016 ◽  
Author(s):  
Ahmed Abdelrazek ◽  
Ichiro Kimura ◽  
Yasuyuki Shimizu

2002 ◽  
Vol 2 (12) ◽  
pp. 1057-1062 ◽  
Author(s):  
E.E. Elhadi . ◽  
Lei Xiaosong . ◽  
Wu Keqi .

2018 ◽  
Author(s):  
A. Muthuvel ◽  
Monish Babu K. ◽  
Purandarraj S. ◽  
Darshan N. K.

2019 ◽  
Vol 213 ◽  
pp. 02076
Author(s):  
Jan Sip ◽  
Frantisek Lizal ◽  
Jakub Elcner ◽  
Jan Pokorny ◽  
Miroslav Jicha

The velocity field in the area behind the automotive vent was measured by hot-wire anenemometry in detail and intensity of turbulence was calculated. Numerical simulation of the same flow field was performed using Computational fluid dynamics in commecial software STAR-CCM+. Several turbulence models were tested and compared with Large Eddy Simulation. The influence of turbulence model on the results of air flow from the vent was investigated. The comparison of simulations and experimental results showed that most precise prediction of flow field was provided by Spalart-Allmaras model. Large eddy simulation did not provide results in quality that would compensate for the increased computing cost.


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