Author
Listed:
- Ruoping Chu
(China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China)
- Kai Wang
(China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China)
Abstract
Urban distributed energy systems play a crucial role in the development of sustainable and low-carbon cities. Evaluating urban wind resources is essential for effective wind energy harvesting, which requires detailed information about the urban flow field. Computational fluid dynamics (CFD) has emerged as a viable and scalable method for assessing urban wind resources. This review paper synthesizes the characteristics of the urban wind environment and resources, outlines the general framework for CFD-aided wind resource assessment, and addresses future challenges and perspectives. It highlights the critical need to optimize wind energy harvesting in complex built environments. The paper discusses the conditions for urban wind resource assessment, particularly the extraction of boundary conditions and the performance of small wind turbines (SWTs). Additionally, it notes that while large eddy simulation (LES) is a high-fidelity model, it is still less commonly used compared to Reynolds-averaged Navier–Stokes (RANS) models. Several challenges remain, including the broader adoption of high-fidelity LES models, the integration of wake models and extreme conditions, and the application of these methods at larger scales in real urban environments. The potential of multi-scale modeling approaches to enhance the feasibility and scalability of these methods is also emphasized. The findings are intended to promote the utilization and further development of CFD methods to accelerate the creation of resilient and energy-efficient cities, as well as to foster interdisciplinary innovation in wind energy systems.
Suggested Citation
Ruoping Chu & Kai Wang, 2025.
"CFD in Urban Wind Resource Assessments: A Review,"
Energies, MDPI, vol. 18(10), pages 1-21, May.
Handle:
RePEc:gam:jeners:v:18:y:2025:i:10:p:2626-:d:1659463
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