Improvement of k-epsilon turbulence model for CFD simulation of atmospheric boundary layer around a high-rise building using stochastic optimization and Monte Carlo sampling technique

Shirzadi, Mohammadreza; Mirzaei, Parham A.; Naghashzadegan, Mohammad

Abstract

The accuracy of the computational fluid dynamics (CFD) to model the airflow around the buildings in the atmospheric boundary layer (ABL) is directly linked to the utilized turbulence model. Despite the popularity and their low computational cost, the current Reynolds Averaged Navier-Stokes (RANS) models cannot accurately resolve the wake regions behind the buildings. The default values of the RANS models’ closure coefficients in CFD tools such as ANSYS CFX, ANSYS FLUENT, PHOENIX, and STAR CCM+ are mainly adapted from other fields and physical problems, which are not perfectly suitable for ABL flow modeling. This study embarks on proposing a systematic approach to find the optimum values for the closure coefficients of RANS models in order to significantly improve the accuracy of CFD simulations for urban studies. The methodology is based on stochastic optimization and Monte Carlo Sampling technique. To show the capability of the method, a test case of airflow around an isolated building placed in a non-isothermal unstable ABL was considered. The recommended values for this case study in accordance with the optimization method were thus found to be 1.45 ≤

Citation

Shirzadi, M., Mirzaei, P. A., & Naghashzadegan, M. (in press). Improvement of k-epsilon turbulence model for CFD simulation of atmospheric boundary layer around a high-rise building using stochastic optimization and Monte Carlo sampling technique. Journal of Wind Engineering and Industrial Aerodynamics, 171, https://doi.org/10.1016/j.jweia.2017.10.005

• Journal Article Type: Article
• Acceptance Date: Oct 9, 2017
• Online Publication Date: Nov 5, 2017
• Deposit Date: Oct 18, 2017
• Publicly Available Date: Nov 6, 2018
• Journal: Journal of Wind Engineering and Industrial Aerodynamics
• Print ISSN: 0167-6105
• Electronic ISSN: 0167-6105
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 171
• DOI: https://doi.org/10.1016/j.jweia.2017.10.005
• Keywords: CFD, Turbulence, Optimization, Microclimate, Monte Carlo Sampling, Atmospheric Boundary Layer
• Public URL: https://nottingham-repository.worktribe.com/output/893174
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S016761051730020X
• Contract Date: Oct 18, 2017