Investigation of pedestrian-level wind environment near two high-rise buildings in different arrangements

Abstract

The presence of buildings that surround the wind environment adversely affects at the pedestrian level. The present study investigates the effect of different arrangement of two buildings on wind flow structure and modification of wind speed conditions at the pedestrian level. The investigation was carried out for parallel, tandem, and staggered arrangement of two buildings using computational fluid dynamics simulations. The wind tunnel experiments were conducted to validate the computational fluid dynamics results. The computational fluid dynamics simulations were performed using the standard [Formula: see text] model with LK modification and revised closure coefficients. Different flow features such as skew-symmetric vortex structure for parallel arrangement, reattachment of shear layer on the surfaces of the downstream building for tandem arrangement, and deviation of wake region behind the upstream building to leftward of the flow direction for staggered arrangement were observed. It was observed that the strong wind conditions were mostly affected by tandem and parallel location of the twin buildings. The results of numerical simulation obtained using the modified SKE model were found to be in good agreement with the experimental results.

Wind-interference effect on an octagonal building in varying positions by large eddy simulation

“…Hu et al (2020) used machine learning techniques based on 30% of a dataset to predict wind pressure coefficients on the principal building. Mittal et al (2019) studied the effect of various arrangements of two buildings on wind flow structure and modification of wind speed conditions at the pedestrian level. Sharma et al (2019) analysed the interference effect between two square tall buildings in tandem positions.…”

Computational fluid dynamics and artificial neural network‐based analysis and forecasting of wind effects on obliquely parallel multiple building models using categorical variable encoding

“…Hu et al (2020) 7 delved into interference effects caused by urban clusters on tall buildings, utilizing machine learning for precise prediction of wind pressure coefficients. Mittal et al (2019) 8 examined the impact of various building arrangements on wind flow using computational fluid dynamics (CFD) simulations, and Sharma et al (2019) 9 investigated interference between two square tall buildings. Sy et al (2019) 10 conducted wind tunnel tests on wind-over-top flow between high-rise buildings, and Yu et al (2018) 11 performed wind tunnel experiments on interference effects between tall buildings, revealing wake vortex-induced resonance.…”