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Evaluation of Reynolds stress, k-ε and RNG k-ε turbulence models in street canyon flows using various experimental datasets

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Abstract

In this study the Reynolds-averaged Navier-Stokes computational fluid dynamics methodology is used, which has proved to be a powerful tool for the simulations of the airflow and pollutant dispersion in the atmospheric environment. The interest is focused on the urban areas and more specifically on the street canyons, several types of which are examined in order to evaluate the performance of various turbulence models, including a Reynolds-stress model and variations of the k-ε model. The results of the two-dimensional simulations are compared with measurements from a diversity of independent street canyon experimental datasets, covering a wide range of aspect ratios, free stream velocities and roughnesses. This way more general and reliable conclusions can be reached about the applicability, accuracy and ease of use of each turbulence model. In this work, the renormalization group k-ε presented better results in most cases examined, while the Reynolds-stress model did not stand up for the expectations and also exhibited convergence problems.

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Authors and Affiliations

  1. Environmental Technology Laboratory, Department of Mechanical Engineering, University of West Macedonia, Kastorias and Fleming Str., 50100, Kozani, Greece

    Nektarios Koutsourakis & John G. Bartzis

  2. Computational Fluid Dynamics Unit, School of Chemical Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., Zografou Campus, 15780, Athens, Greece

    Nicolas C. Markatos

  3. Environmental Research Laboratory, Institute of Nuclear Technology-Radiation Protection, National Centre for Scientific Research ‘Demokritos’, 15310, Aghia Paraskevi, Athens, Greece

    Nektarios Koutsourakis

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  1. Nektarios Koutsourakis
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Correspondence to Nektarios Koutsourakis.

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Koutsourakis, N., Bartzis, J.G. & Markatos, N.C. Evaluation of Reynolds stress, k-ε and RNG k-ε turbulence models in street canyon flows using various experimental datasets. Environ Fluid Mech 12, 379–403 (2012). https://doi.org/10.1007/s10652-012-9240-9

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