PIGNN-CFD: A physics-informed graph neural network for rapid predicting urban wind field defined on unstructured mesh-Reference-Cited by-同舟云学术

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PIGNN-CFD: A physics-informed graph neural network for rapid predicting urban wind field defined on unstructured mesh

Published:2023-03 Issue: Volume:232 Page:110056
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Shao Xuqiang,Liu Zhijian,Zhang Siqi,Zhao Zijia,Hu Chenxing

Funder

National Natural Science Foundation of China

Ministry of Education of the People's Republic of China

Natural Science Foundation of Hebei Province

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference52 articles.

1. Airflow patterns within a complex urban topography under hot and dry climate in the Algerian Sahara;Kitous;Build. Environ.,2012

2. Building cluster and shading in urban canyon for hot dry climate: Part 1: air and surface temperature measurements;Bourbia;Renew. Energy,2004

3. Remote sensing retrieval of urban land surface temperature in hot-humid region;Shi;Urban Clim.,2018

4. On-site evaluation of pedestrian-level air quality at a U-type street canyon in an ancient city;Cui;J. Wind Eng. Ind. Aerod.,2017

5. Influence of urban geometry on outdoor thermal comfort in a hot dry climate: a study in Fez, Morocco;Johansson;Build. Environ.,2006

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