Parametrization of irregularity of urban morphologies for designing better pedestrian wind environment in high-density cities – A wind tunnel study

He, Yueyang; Liu, Zhixin; Ng, Edward

doi:10.1016/j.buildenv.2022.109692

Cited by 19 publications

(6 citation statements)

References 46 publications

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“…This research highlights the importance of utilizing accurate and comprehensive data in predicting wind patterns (Y. He et al, 2022). Through the integration of multiple models, scholars have discovered that the order of ventilation potential in various types of urban areas is as follows: road space, green space, and river space (W. Wang et al, 2022).…”

Section: Current Researchmentioning

confidence: 96%

Associations between summer wind environment and urban physical indicators in commercial blocks based on field measurement and simulation in Xi'an

Zhang

Dong

et al. 2023

Urban Climate

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Section: Current Researchmentioning

confidence: 96%

Associations between summer wind environment and urban physical indicators in commercial blocks based on field measurement and simulation in Xi'an

Zhang

Dong

et al. 2023

Urban Climate

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“…Table 1. Research on buildings, structures, and urban spaces related to wind effects using ML [11,12,14,19,20,34,35,63,65,66,.…”

Section: Machine Learning For Wind Estimation In Built Environmentmentioning

confidence: 99%

“…The design of urban spaces exerts an impact on wind speed, which has a significant impact on the comfort of pedestrians, as the air currents generated by buildings can obstruct movement and lead to discomfort. The shape, height, and layout of buildings are crucial considerations in this regard [7][8][9][10][11][12][13][14]. The vertical dimension of a rectangular building can influence the velocity of wind in its close proximity, as higher structures can redirect and impede the movement of air [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Gür,

Karadag

2024

Buildings

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(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.

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“…In the past 15 years, the urban wind speed has decreased, while the rural wind speed has continued increasing (see Figure 7b). Since the limited open space between high-rise buildings reduces the permeability of air ventilation near the surface, the urban wind speed decreases with the development of cities, even in coastal cities (Figure 3c) [63][64][65]. UTCI increase with the decrease in wind speed (see Figure 7a), resulting in the UTCI still maintaining a significant level of urbanization warming.…”

Section: Driving Factors Analysis Of Regional Thermal Comfort Indicesmentioning

confidence: 99%

Comparisons of the Urbanization Effect on Heat Stress Changes in Guangdong during Different Periods

Chao

et al. 2023

Remote Sensing

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While rapid urbanization promotes social and economic development, it exacerbates human outdoor thermal comfort, which increases the risks to human health. This paper uses four thermal comfort indices and multiple satellite observations to explore the urbanization effect on summer heat stress in Guangdong from 1979–2018, a coastal province of China. Two types of thermal comfort index are used here, namely the direct thermal comfort index (Heat Index, HI; Temperature–Humidity Index, THI; Discomfort Index, DI) and the physiological thermal comfort index (Universal Thermal Climate Index, UTCI). We compare the differences in the urbanization effects on the changes in the three direct thermal comfort indices (HI, THI, and DI) and a physiological thermal comfort index (UTCI). The results show that all four thermal comfort indices indicate an overall warming trend. Of them, urban sites show a higher warming trend than rural sites, indicating that heat stress changes are significantly influenced by urbanization from 1979–2018, which is consistent with the effect of urbanization on surface air temperature. However, except for the UTCI, this warming of direct thermal comfort indices affected by urbanization has become insignificant under the regional vegetation greening from 2004–2018 (also consistent with surface air temperature). This is primarily attributed to the different effects of wind speed on the physiological thermal comfort index in urban and rural areas: Decreasing wind speeds in urban areas lead to an increase in UTCI, while wind speeds in rural areas increase instead and decrease UTCI, thus widening the UTCI differences between urban and rural areas. Our results indicate that urbanization has a different effect on thermal comfort indices. When using the thermal comfort index, it is necessary to consider that different thermal comfort indices may bring different results. UTCI considers more factors that affect human heat perception, so it can better describe human outdoor thermal comfort. It also highlights the importance of urban ventilation and urban greenness in mitigating urban outdoor thermal comfort in the sustainable construction of future urbanization in coastal cities.

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Parametrization of irregularity of urban morphologies for designing better pedestrian wind environment in high-density cities – A wind tunnel study

Cited by 19 publications

References 46 publications

Associations between summer wind environment and urban physical indicators in commercial blocks based on field measurement and simulation in Xi'an

Associations between summer wind environment and urban physical indicators in commercial blocks based on field measurement and simulation in Xi'an

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Comparisons of the Urbanization Effect on Heat Stress Changes in Guangdong during Different Periods

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