0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessA novel development of Embedded Large Eddy Simulation (ELES) model was proposed for urban wind field simulations, incorporating the synthetic volume force method integrated with the Graphic Processing Unit (GPU) parallel computing. By combining the advantages of RANS and LES, the proposed model enhances both computational efficiency and accuracy in comparison to conventional LES models. A validation simulation for the atmospheric boundary layer wind field was conducted to assess the accuracy of the proposed method. Subsequently, an ideal urban block was used to evaluate the influence of LES region sizes and the positioning of the RANS (Reynolds-Averaged Navier-Stokes)/LES interface on the simulation results. It was found that the positioning of the RANS/LES interface in the ELES model appears to have little effects on the mean wind field, while it does affect the fluctuating wind speed results. Comparative analysis with wind tunnel experiments and conventional large eddy simulation confirmed the efficacy of the proposed ELES with accelerated turbulence generation.
Sunce Liao, Mingfeng Huang, Qiang� Li, Kang Cai, Ben He, Cai Li, Ahsan Kareem, Yi‐Qing Ni (2025). Embedded LES modelling of atmospheric boundary layer in urban region using accelerated turbulence generation. , 19(1), DOI: https://doi.org/10.1080/19942060.2025.2585306.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2025
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1080/19942060.2025.2585306
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access