CFD Analysis of Wind Distribution around Buildings in Low-Density Urban Community

Hnaien, Nidhal; Hassen, Walid; Kolsi, Lioua; Mesloub, Abdelhakim; Alghaseb, Mohammed; Elkhayat, Khaled; Abdelhafez, Mohamed Hssan Hassan

doi:10.3390/math10071118

Cited by 14 publications

(13 citation statements)

References 33 publications

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“…It is important to note that all the results are based on an effort to faithfully replicate real-world conditions. For this purpose, several parameters, such as wind direction [ 24 ] and pollutant emission velocity from the chimney, were kept constant. However, the wind velocity profile, in particular the reference velocity “u ref ” was varied from 0 to 10 m/s.…”

Section: Resultsmentioning

confidence: 99%

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Air pollution dispersion in Hail city: Climate and urban topography impact

Hassen,

Hnaien,

Ben Said

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

“…For the turbulent kinetic energy, “I" represents the turbulence intensity estimated at 5 % for the climatic condition of Hail region [ 24 ]. Finally, the terms d and Ht in the specific dissipation rate equations, represent respectively the hydraulic diameter of the stacks and the altitude of the whole studied area.…”

Section: Methodsmentioning

confidence: 99%

Air pollution dispersion in Hail city: Climate and urban topography impact

Hassen,

Hnaien,

Ben Said

et al. 2023

Heliyon

Self Cite

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“…Figure 11b shows that the TKE value exhibits a large peak near the RT location. This implies that large velocity fluctuations occurred [41]. The results at the section plane across the WT location are shown in Figure 11c,d.…”

Section: General Flow Patternmentioning

confidence: 91%

CFD Prediction for Wind Power Generation by a Small Vertical Axis Wind Turbine: A Case Study for a University Campus

Tominaga

2023

Energies

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The accuracy of wind power generation predicted by computational fluid dynamics (CFD) simulations combined with meteorological wind data was validated based on comparisons with directly measured data for a small vertical axis wind turbine system installed on a university campus. The CFD simulations were performed in accordance with established guidelines and frameworks for the prediction of urban wind environments. At the rooftop location, where small wind turbines are typically installed, the deviations in wind velocity from the measurements are quite large. However, in the present study, the prediction accuracy for the wind turbine site, which was 4 m above the ground, was acceptable. The total power generation estimated using the assumed power curve based on the rated output of the turbine was 56% larger than that directly measured by the power generator. However, using the power curves obtained from the measurements, the total power generation could be predicted with a high degree of accuracy and with an error of approximately 3%. It is suggested that not only the accuracy of the wind velocity but also that of the power curve is very important because they are directly related to that of the predicted power generation.

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“…Hnaien developed a city model with building details for the Hail region and conducted CFD simulations to identify areas with low wind comfort. The results showed that the weather conditions (wind speed and direction) and building layout significantly affected comfort [14]. Vita combined CFD and dynamic thermal modeling (DTM) to assess the airborne infection risk in buildings and make buildings safer during an epidemic [15].…”

Section: Introductionmentioning

confidence: 99%

A Generalization of Building Clusters in an Urban Wind Field Simulated by CFD

Qiu,

He,

et al. 2023

Atmosphere

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The urban climate has a critical influence on developing sustainable cities, and one important factor is the urban wind environment. Moreover, refining urban wind fields is required for the quantitative assessment of urban wind environments. Computational fluid dynamics (CFD) is a powerful tool for modeling the wind flow characteristics in urban areas. Although CFD has been widely used in various fields, its use for simulating urban wind fields has limitations because of the complexity of urban building models and the high computational workload. Accordingly, we consider the generalization parameters in the vertical and horizontal directions based on the CFD results and the building topology based on the state of the building nodes. We perform a two-dimensional generalization of building clusters, conduct spatial analysis in a geographic information system (GIS), and generate three-dimensional models. This generalization scheme is applied to Meiling Street in Jinjiang City, Fujian Province, China. The results indicate that the generalization decreases the number of buildings from 7003 to 3367 and the computation time from 11 h and 26 min to 10 h and 25 min. The computation efficiency is improved by 8.89%, with 1.85% changes in the average wind speed ratio. This scheme substantially improves the computational efficiency of urban wind field CFD simulations by reducing the geometric model’s complexity without compromising the accuracy. This strategy is suitable for simulating large-scale urban wind fields.

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CFD Analysis of Wind Distribution around Buildings in Low-Density Urban Community

Cited by 14 publications

References 33 publications

Air pollution dispersion in Hail city: Climate and urban topography impact

Air pollution dispersion in Hail city: Climate and urban topography impact

CFD Prediction for Wind Power Generation by a Small Vertical Axis Wind Turbine: A Case Study for a University Campus

A Generalization of Building Clusters in an Urban Wind Field Simulated by CFD

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