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

Peng，Wenhui1,3,4; Qin，Shaoxiang1,2; Yang，Senwen1; Wang，Jianchun3; Liu，Xue2; Wang，Liangzhu (Leon)1

2024-01-15

10.1016/j.buildenv.2023.111063

Building and Environment   影响因子和分区

0360-1323

Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex nonlinear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban microclimate simulation. For modeling large-scale urban microclimate problems, CityFFD simulates urban microclimate features based on the semi-Lagrangian approach and fractional stepping method with the Smagorinsky large eddy simulation model. In our simulation, the 1200 sequential time steps are used as training data. We retain and analyze the data from all stages, including the spin-up period, because we wish to understand how the flow develops transiently from initial conditions, and both one-step and sequential timestep predictions are analyzed. When applied to unseen data with different wind directions, the FNO model has a 0.3% one-step prediction error and a maximum error of 5%. A real-time simulation of urban microclimates in 3D is possible with the FNO approach, which is 25 times faster than the traditional numerical solver.

AI CFD Deep learning FNO Real-time Urban microclimate

SCI ; EI

英语

其他

ENGINEERING

2-s2.0-85178484727

Scopus

1.Concordia University,Centre for Zero Energy Building Studies,Department of Building,Civil and Environmental Engineering,Montreal,H3G 1M8,Canada
2.McGill University,School of Computer Science,Montreal,H3A 0G4,Canada
3.Southern University of Science and Technology,Department of Mechanics and Aerospace Engineering,Shenzhen,518055,China
4.The Hong Kong Polytechnic University,Department of Applied Mathematics,999077,Hong Kong

Peng，Wenhui,Qin，Shaoxiang,Yang，Senwen,et al. Fourier neural operator for real-time simulation of 3D dynamic urban microclimate[J]. Building and Environment,2024,248.

Peng，Wenhui,Qin，Shaoxiang,Yang，Senwen,Wang，Jianchun,Liu，Xue,&Wang，Liangzhu .(2024).Fourier neural operator for real-time simulation of 3D dynamic urban microclimate.Building and Environment,248.

Peng，Wenhui,et al."Fourier neural operator for real-time simulation of 3D dynamic urban microclimate".Building and Environment 248(2024).