Optimization of radial inflow wind turbines for urban wind energy harvesting
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DOI: 10.1016/j.energy.2020.117772
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Cited by:
- Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
- Hongye pan, & Jia, Changyuan & Li, Haobo & Zhou, Xianzheng & Fang, Zheng & Wu, Xiaoping & Zhang, Zutao, 2022. "A renewable energy harvesting wind barrier based on coaxial contrarotation for self-powered applications on railways," Energy, Elsevier, vol. 258(C).
- Anbarsooz, M. & Amiri, M., 2022. "Towards enhancing the wind energy potential at the built environment: Geometry effects of two adjacent buildings," Energy, Elsevier, vol. 239(PD).
- Noman, Abdullah Al & Tasneem, Zinat & Sahed, Md. Fahad & Muyeen, S.M. & Das, Sajal K. & Alam, Firoz, 2022. "Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
- ArabGolarcheh, Alireza & Anbarsooz, Morteza & Benini, Ernesto, 2024. "An actuator line method for performance prediction of HAWTs at urban flow conditions: A case study of rooftop wind turbines," Energy, Elsevier, vol. 292(C).
- Zahra Sefidgar & Amir Ahmadi Joneidi & Ahmad Arabkoohsar, 2023. "A Comprehensive Review on Development and Applications of Cross-Flow Wind Turbines," Sustainability, MDPI, vol. 15(5), pages 1-39, March.
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Keywords
Wind turbine; Radial inflow wind turbine; Energy in buildings; Design optimization; CFD; Machine learning;All these keywords.
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