English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Global urbanization has underscored the significance of urban microclimatesfor human comfort, health, and building/urban energy efficiency. Theyprofoundly influence building design and urban planning as major environmentalimpacts. Understanding local microclimates is essential for cities to preparefor climate change and effectively implement resilience measures. However,analyzing urban microclimates requires considering a complex array of outdoorparameters within computational domains at the city scale over a longer periodthan indoors. As a result, numerical methods like Computational Fluid Dynamics(CFD) become computationally expensive when evaluating the impact of urbanmicroclimates. The rise of deep learning techniques has opened newopportunities for accelerating the modeling of complex non-linear interactionsand system dynamics. Recently, the Fourier Neural Operator (FNO) has been shownto be very promising in accelerating solving the Partial Differential Equations(PDEs) and modeling fluid dynamic systems. In this work, we apply the FNOnetwork for real-time three-dimensional (3D) urban wind field simulation. Thetraining and testing data are generated from CFD simulation of the urban area,based on the semi-Lagrangian approach and fractional stepping method tosimulate urban microclimate features for modeling large-scale urban problems.Numerical experiments show that the FNO model can accurately reconstruct theinstantaneous spatial velocity field. We further evaluate the trained FNO modelon unseen data with different wind directions, and the results show that theFNO model can generalize well on different wind directions. More importantly,the FNO approach can make predictions within milliseconds on the graphicsprocessing unit, making real-time simulation of 3D dynamic urban microclimatepossible.

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate