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Shaoxiang Qin

Doctorat - McGill
Superviseur⋅e principal⋅e
Xue (Steve) Liu
Sujets de recherche
Apprentissage profond
Apprentissage spectral

Publications

Modeling Multivariable High-resolution 3D Urban Microclimate Using Localized Fourier Neural Operator
Shaoxiang Qin
Dongxue Zhan
Dingyang Geng
Wenhui Peng
Geng Tian
Yurong Shi
Naiping Gao
Xue Liu
Liangzhu (Leon) Wang
Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-efficient urban planning, supporting carbon redu… (voir plus)ction, enhancing public health and comfort, and advancing the low-altitude economy. However, traditional computational fluid dynamics (CFD) simulations that couple velocity and temperature are computationally expensive. Recent machine learning advancements offer promising alternatives for accelerating urban microclimate simulations. The Fourier neural operator (FNO) has shown efficiency and accuracy in predicting single-variable velocity magnitudes in urban wind fields. Yet, for multivariable high-resolution 3D urban microclimate prediction, FNO faces three key limitations: blurry output quality, high GPU memory demand, and substantial data requirements. To address these issues, we propose a novel localized Fourier neural operator (Local-FNO) model that employs local training, geometry encoding, and patch overlapping. Local-FNO provides accurate predictions for rapidly changing turbulence in urban microclimate over 60 seconds, four times the average turbulence integral time scale, with an average error of 0.35 m/s in velocity and 0.30 °C in temperature. It also accurately captures turbulent heat flux represented by the velocity-temperature correlation. In a 2 km by 2 km domain, Local-FNO resolves turbulence patterns down to a 10 m resolution. It provides high-resolution predictions with 150 million feature dimensions on a single 32 GB GPU at nearly 50 times the speed of a CFD solver. Compared to FNO, Local-FNO achieves a 23.9% reduction in prediction error and a 47.3% improvement in turbulent fluctuation correlation.
2025-02-11
Building and Environment (publié)
doi.org
arxiv.org