On the Influence of Geometrical Features and Wind Direction over an Urban Canyon Applying a FEM Analysis

Battista, Gabriele; Evangelisti, Luca; Guattari, Claudia; Vollaro, Roberto De Lieto

doi:10.1016/j.egypro.2015.12.054

Cited by 3 publications

(1 citation statement)

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“…In urban areas, the presence of neighbouring buildings decreases the heat gain of building surface and surface temperature due to the shading effect [87]. Similarly, sheltering effect triggered from neighbouring buildings reduces local wind speed [40] or the local wind speed increases due to wind channelling resulted from the deepening of a canyon [88,89]. In Liu et al's correlation, these effects of sheltering and sun shading are considered by relating it with plane area density along with local wind velocity and surface temperature.…”

Section: Assessment Of Convective Heat Transfer Coefficient Correlati...mentioning

confidence: 99%

A Review on Prediction Uncertainty in Exterior Heat Transfer Coefficient-Based Building Thermal Load: A Case of Microclimate

Kadam¹,

Hassan²,

Wang³

et al. 2023

IJCMEM

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A correct prediction of building cooling load is essential in building energy consumption in a hot and humid urban area. To this extent, the current study emphasizes a meticulous review of different convective heat transfer coefficient correlations including those developed considering neighbourhood microclimate effect, and the existing ones used in building energy simulation programs such as EnergyPlus, Environmental Systems Performance -Research (ESP-r), Integrated Environmental Solutions Ltd (IES), IDA, and TAS. Furthermore, rigorous quantitative assessment of associated convective thermal load from the windward, leeward, and roof surfaces under the case of microclimatic conditions is performed. The data used in the current assessment are computational fluid dynamics results, as a reference, from previously published data and actual weather data from the hot and humid climate. It is observed that very few convective heat transfer coefficient correlations show closer predicted thermal load (deviation less than 30%) with computational fluid dynamics results, and others exhibit a varying degree of prediction ability with over-predictions in general for the windward, leeward and roof surfaces. Current analysis suggests that further attention is required to increase the prediction ability of convective heat transfer coefficient correlations by developing a convective heat transfer coefficient model considering computational fluid dynamics analysis of the whole district, validating and modifying or redefining existing convective heat transfer coefficient correlations based on real field measurement data considering flow field around the building, and incorporation of urban morphology, vegetation, urban heat island, and urban pollution level in convective heat transfer coefficient correlations development.

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