Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm
2021
Аутори
Svorcan, JelenaPeković, Ognjen
Simonović, Aleksandar
Tanović, Dragoljub
Hasan, Mohammad Sakib
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Wind energy extraction is one of the fastest developing engineering branches today. Number of installed wind turbines is constantly increasing. Appropriate solutions for urban environments are quiet, structurally simple and affordable small-scale vertical-axis wind turbines (VAWTs). Due to small efficiency, particularly in low and variable winds, main topic here is development of optimal flow concentrator that locally augments wind velocity, facilitates turbine start and increases generated power. Conceptual design was performed by combining finite volume method and artificial intelligence (AI). Smaller set of computational results (velocity profiles induced by existence of different concentrators in flow field) was used for creation, training and validation of several artificial neural networks. Multi-objective optimization of concentrator geometric parameters was realized through coupling of generated neural networks with genetic algorithm. Final solution from the acquired Pareto set... is studied in more detail. Resulting computed velocity field is illustrated. Aerodynamic performances of small-scale VAWT with and without optimal flow concentrator are estimated and compared. The performed research demonstrates that, with use of flow concentrator, average increase in wind speed of 20%-25% can be expected. It also proves that contemporary AI techniques can significantly facilitate and accelerate design processes in the field of wind engineering.
Кључне речи:
Wind turbines / speed augmentation / GA / flow concentrator / CFD / ANNИзвор:
Advances in Mechanical Engineering, 2021, 13, 3Издавач:
- Sage Publications Ltd, London
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200105 (Универзитет у Београду, Машински факултет) (RS-MESTD-inst-2020-200105)
DOI: 10.1177/16878140211009009
ISSN: 1687-8132
WoS: 000636709900001
Scopus: 2-s2.0-85103589620
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Svorcan, Jelena AU - Peković, Ognjen AU - Simonović, Aleksandar AU - Tanović, Dragoljub AU - Hasan, Mohammad Sakib PY - 2021 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3547 AB - Wind energy extraction is one of the fastest developing engineering branches today. Number of installed wind turbines is constantly increasing. Appropriate solutions for urban environments are quiet, structurally simple and affordable small-scale vertical-axis wind turbines (VAWTs). Due to small efficiency, particularly in low and variable winds, main topic here is development of optimal flow concentrator that locally augments wind velocity, facilitates turbine start and increases generated power. Conceptual design was performed by combining finite volume method and artificial intelligence (AI). Smaller set of computational results (velocity profiles induced by existence of different concentrators in flow field) was used for creation, training and validation of several artificial neural networks. Multi-objective optimization of concentrator geometric parameters was realized through coupling of generated neural networks with genetic algorithm. Final solution from the acquired Pareto set is studied in more detail. Resulting computed velocity field is illustrated. Aerodynamic performances of small-scale VAWT with and without optimal flow concentrator are estimated and compared. The performed research demonstrates that, with use of flow concentrator, average increase in wind speed of 20%-25% can be expected. It also proves that contemporary AI techniques can significantly facilitate and accelerate design processes in the field of wind engineering. PB - Sage Publications Ltd, London T2 - Advances in Mechanical Engineering T1 - Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm IS - 3 VL - 13 DO - 10.1177/16878140211009009 ER -
@article{ author = "Svorcan, Jelena and Peković, Ognjen and Simonović, Aleksandar and Tanović, Dragoljub and Hasan, Mohammad Sakib", year = "2021", abstract = "Wind energy extraction is one of the fastest developing engineering branches today. Number of installed wind turbines is constantly increasing. Appropriate solutions for urban environments are quiet, structurally simple and affordable small-scale vertical-axis wind turbines (VAWTs). Due to small efficiency, particularly in low and variable winds, main topic here is development of optimal flow concentrator that locally augments wind velocity, facilitates turbine start and increases generated power. Conceptual design was performed by combining finite volume method and artificial intelligence (AI). Smaller set of computational results (velocity profiles induced by existence of different concentrators in flow field) was used for creation, training and validation of several artificial neural networks. Multi-objective optimization of concentrator geometric parameters was realized through coupling of generated neural networks with genetic algorithm. Final solution from the acquired Pareto set is studied in more detail. Resulting computed velocity field is illustrated. Aerodynamic performances of small-scale VAWT with and without optimal flow concentrator are estimated and compared. The performed research demonstrates that, with use of flow concentrator, average increase in wind speed of 20%-25% can be expected. It also proves that contemporary AI techniques can significantly facilitate and accelerate design processes in the field of wind engineering.", publisher = "Sage Publications Ltd, London", journal = "Advances in Mechanical Engineering", title = "Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm", number = "3", volume = "13", doi = "10.1177/16878140211009009" }
Svorcan, J., Peković, O., Simonović, A., Tanović, D.,& Hasan, M. S.. (2021). Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm. in Advances in Mechanical Engineering Sage Publications Ltd, London., 13(3). https://doi.org/10.1177/16878140211009009
Svorcan J, Peković O, Simonović A, Tanović D, Hasan MS. Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm. in Advances in Mechanical Engineering. 2021;13(3). doi:10.1177/16878140211009009 .
Svorcan, Jelena, Peković, Ognjen, Simonović, Aleksandar, Tanović, Dragoljub, Hasan, Mohammad Sakib, "Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm" in Advances in Mechanical Engineering, 13, no. 3 (2021), https://doi.org/10.1177/16878140211009009 . .