An extended SST k−ω framework for the RANS simulation of the neutral Atmospheric Boundary Layer. (February 2023) - Bellegoni, Marco; Cotteleer, Léo; Raghunathan Srikumar, Sampath Kumar; Mosca, Gabriele; Gambale, Alessandro; Tognotti, Leonardo; Galletti, Chiara; Parente, Alessandro
Abstract: The use of Computational Fluid Dynamics (CFD) for environmental studies is continuously growing. In this context, simulations are usually carried out solving Reynolds-averaged Navier–Stokes (RANS) equations that require an appropriate implementation of the Atmospheric Boundary Layer (ABL). This paper proposes a framework based on the Shear Stress Transport (SST) k − ω turbulence model, which is highly recommended for microclimate analysis, since it is known to well reproduce homogeneous ABL flows in the context of RANS simulations. Besides, a new blending approach is developed to account for the presence of obstacles in the domain. The model is implemented in the open-source OpenFOAM code and validated against well-known benchmark cases spanning different configurations, i.e., an empty fetch, a single building and an array of buildings. The performance of the proposed model is very satisfactory. For instance, the Factor-of-2 validation metric is FAC2 > 0.8 for both velocity and turbulent kinetic energy in nearly all cases. Highlights: A comprehensive approach is developed to describe the ABL using the SST k- ω model. A new blending approach is proposed to account for the presence of obstacles. The model is validated against experimental data and compared with existing models