A review of simplified numerical approaches for fast urban airflow simulation

Xu, Xiaoyue; Gao, Zhi; Zhang, Mingjie

doi:10.1016/j.buildenv.2023.110200

Cited by 13 publications

(6 citation statements)

References 77 publications

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“…-A faster numerical simulation method was developed for large urban or regional scales. A few studies (Wang et al, 2021;Xu et al, 2023) mentioned other methods with less detailed modeling, such as the Porous Media Model, Fast Fluid Dynamics, and outdoor Multizone Model, and these methods reduced computational costs. In the case of Bangkok, which consists of uneven groups of buildings and complex buildings, these methods encounter difficulties.…”

Section: Limitations and Further Studymentioning

confidence: 99%

<b>Comparative Analysis of the Urban Ventilation Characteristics between the Old and New Central Business Districts of Bangkok, Thailand</b>

Ngamsiriudom,

Mettatam,

Tamura

et al. 2024

URPR

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Urban heat islands (UHIs) are a global phenomenon of concern, especially in megacities such as Bangkok and Thailand. Addressing UHI effects requires an understanding of their connection to urban spatial structures, with urban ventilation playing a crucial role. This study compared the ventilation characteristics of Bangkok's Yaowarat and Ratchadamri central business districts (CBDs). Utilizing 2022 field measurements, spatial analysis, and computational fluid dynamics (CFD) simulations, this study examined the key factors influencing urban ventilation, primarily wind velocity and open space ratio within a 100meter radius. Each CBD exhibits distinct ventilation characteristics that are attributable to its unique topography. Yaowarat, characterized by compact low-to mid-rise buildings, manifests poorer ventilation than Ratchadamri, characterized by compact high-rise structures complemented by open spaces, which is consistent across both methods. Yaowarat recorded wind velocity (WV) levels below 1 m/s, while Ratchadamri typically registered WV levels exceeding 1 m/s. However, both areas still exhibited suboptimal ventilation and required improvements in WV. This study highlighted the significance of urban climatology in Thailand, raised stakeholder awareness, and emphasized the value of combining field measurements and CFD simulations to inform urban planning decisions for adapting urban ventilation related to UHIs.

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Section: Limitations and Further Studymentioning

confidence: 99%

<b>Comparative Analysis of the Urban Ventilation Characteristics between the Old and New Central Business Districts of Bangkok, Thailand</b>

Ngamsiriudom,

Mettatam,

Tamura

et al. 2024

URPR

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“…In this context, urban canyons are defined by two fundamental properties: the heightto-width ratio (H/W) and the longitudinal axis orientation [26][27][28]. It has been demonstrated that these properties directly affect solar access and shading strategies, wind frequency, and heat exchanges that control surface and air temperatures [22,25,29,30]. As a result, they not only influence the OTC for individuals but can strongly impact the thermal comfort within indoor spaces, energy consumption, and cooling and heating demands [19,[31][32][33][34].…”

Section: Outdoor Thermal Comfort In Urban Canyon Studiesmentioning

confidence: 99%

Effect of Street Asymmetry, Albedo, and Shading on Pedestrian Outdoor Thermal Comfort in Hot Desert Climates

Necira,

Matallah,

Bouzaher

et al. 2024

Sustainability

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Improving urban walkability in the face of climate change is a critical challenge for urban designers. Street design strategies can mitigate heat stress and enhance pedestrian livability. Most previous studies conducted in hot climates recommend adopting deep canyons to improve summer conditions, overlooking the potential improvement of wide streets as essential structural elements of the urban fabric. This study was conducted in Biskra city, southern Algeria, where several mitigation strategies were applied to ‘Emir Abdelkader Boulevard’, as the main structural street inside the city, to create an optimal street model for arid climates. Five scenarios were developed based on three criteria: (Sc1) asymmetric profile northeast side (NES) > southwest side (SWS); (Sc2) asymmetric profile SWS > NES; (Sc3) cool paving; (Sc4) horizontal shading; and(Sc5) shading with a linear tree arrangement. ENVI-met software version 5.1.1 and the RayMan model were used to estimate the local climate conditions and outdoor thermal comfort levels based on the physiological equivalent temperature (PET). All scenarios reduced PET values across the street, with optimal reductions of −2.0 °C, −3.1 °C, −1.3 °C, −1.7 °C, and −1.2 °C in Sc1, Sc2, Sc3, Sc4, and Sc5, respectively. Concerning pedestrian areas, the optimal results durations were at the southwest side below the arcades’ sidewalks during peak hours: Sc2, Sc3, Sc4, Sc5 (2.2 °C–3 H, 2.3 °C–3 H, 2.4 °C–3 H, 2.5 °C–2 H). Sc1 performed best during daytime hours on the northeast side. The utilizing of these results can strongly help urban planners and landscape architects in creating climate-responsive streets that enhance citizens’ quality of life.

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“…Simulation methods such as computational fluid dynamics (CFD) or fast fluid dynamics (FFD) allow architects and engineers to numerically evaluate and hence better understand the environmental implications of chosen design alternatives onto the surrounding such as potential buildings and blocks. However, these simulations are not necessarily accessible or feasible for early designs [1] especially in instances where multiple cases are being simultaneously investigated such as with parametric design workflows. CFD simulations are computationally expensive and often require experts to set up and run.…”

Section: Introductionmentioning

confidence: 99%

Urban airflow prediction by pix2pix trained on FFD

Vecchiarelli,

Kraus,

Griego

et al. 2023

J. Phys.: Conf. Ser.

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Existing computer-aided design tools render insufficient in their capacity to enable architects and engineers to efficiently evaluate alternative designs during early design phases due to their computationally expensive nature, which is especially the case for computational fluid dynamics (CFD) methods. One of the greatest bottleneck for integrating CFD analysis into early design phases is the limited potential for parametric analysis, where a number of design alternatives need to be quickly generated and evaluated. In this context, the present study investigates the use of the generative deep learning method “pix2pix”, which leverages conditional generative adversarial networks (cGANs) for image-to-image translation, for prediction of airflow characteristics in different representations. The evaluation proposes statistical metrics to judge the fitness of the approach in performing urban airflow prediction. Our study demonstrates that the proposed method to be implemented, trained and validated successfully for different representations of the flow field prediction under parametric city shapes by incorporating building height and vectorial information (either components or magnitudes) into the pix2pix image inputs. The findings of the study reveal that the vortical flow fields can be predicted with a high accuracy in space and magnitude in all model variations tested. Adding building height information to the input images also significantly improves Kullback-Leibler (KL) divergence compared to using uniform building heights as inputs. Using vectorial information in the form of decomposed u, v, w-vector fields during training enabled pix2pix to additionally generate vectorial predictions instead of magnitudes only.

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A review of simplified numerical approaches for fast urban airflow simulation

Cited by 13 publications

References 77 publications

<b>Comparative Analysis of the Urban Ventilation Characteristics between the Old and New Central Business Districts of Bangkok, Thailand</b>

<b>Comparative Analysis of the Urban Ventilation Characteristics between the Old and New Central Business Districts of Bangkok, Thailand</b>

Effect of Street Asymmetry, Albedo, and Shading on Pedestrian Outdoor Thermal Comfort in Hot Desert Climates

Urban airflow prediction by pix2pix trained on FFD

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