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Published in: Neural Computing and Applications 2/2019

26-06-2017 | Original Article

Optimum section selection procedure for horizontal axis tidal stream turbines

Authors: Mojtaba Tahani, Narek Babayan

Published in: Neural Computing and Applications | Special Issue 2/2019

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Abstract

Stochastic behavior of renewable energy sources forces designers to optimize the energy converters for the purpose of capturing the maximum amount of available energy. The performance of horizontal axis wind and tidal turbines mainly depends on the geometrical properties such as chord and twist distributions and also the types of sections which are utilized along the blade. The purpose of presented paper is introducing a procedure which can be utilized in order to select the optimum sections for horizontal axis tidal turbines for the purpose of increasing the turbine performance. The presented procedure also can be applied for horizontal axis wind turbines. For the purpose of evaluating the performance of the proposed method, two design types (chord and twist distributions) of tidal turbines are selected as case studies. Power coefficient is considered as objective function, and three types of hydrofoils namely NACA63-8xx, NACA44xx, and RISO-A1-xx are selected as candidate solutions. A blade element momentum theory model is used for calculating the power coefficient. The discrete ant colony optimization algorithm is selected as optimization tool. The results indicate that the utilization of the proposed method will considerably decrease the required process time for obtaining the optimum sections across the blade span, and also it is shown that using different types of sections across the blade span can increase the power coefficient of the turbine. The importance of the proposed method will be significant when various types of hydrofoils and airfoils can be considered as candidate sections across the blade span.

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Metadata
Title
Optimum section selection procedure for horizontal axis tidal stream turbines
Authors
Mojtaba Tahani
Narek Babayan
Publication date
26-06-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue Special Issue 2/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3079-4

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