Elsakka, M.M., Ingham, D.B. orcid.org/0000-0002-4633-0852, Ma, L. orcid.org/0000-0002-3731-8464 et al. (3 more authors) (2022) Response surface optimisation of vertical axis wind turbine at low wind speeds. Energy Reports, 8. pp. 10868-10880. ISSN 2352-4847
Abstract
The Vertical Axis Wind Turbines (VAWTs) have an increasing global market and this emphasis the need for to improve the performance of VAWTs, especially at relatively low wind speed. This paper utilises the Response Surface methodology to optimise the performance of a VAWT. A three bladed VAWT configuration was considered with a NACA0015 profile. Three significant input parameters were selected including the tip speed ratio, the turbine solidity, and the pitch angle. An extended range of each input parameter was chosen in order to gain a good insight into how these input parameters affect the performance of the VAWT. The high-fidelity Computational Fluid Dynamics (CFD) simulations were carried out for the modelling of the turbine. The use of the Response Surface Optimisation based on Multi-Objective Genetic Algorithm (MOGA) along with the CFD simulations is found to be useful in the selection of the optimal design of VAWT. Moreover, the 3D aspects of the VAWT geometry are investigated and these include the turbine aspect ratio and the effect of the blade tip geometry. The implementation of an optimised winglet at the tip of the turbine blades is found to provide a significant enhancement of the cycle averaged power coefficient, especially at low aspect ratios.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Response Surface Optimisation; Computational Fluid Dynamics; Vertical Axis Wind Turbines; Winglet |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Funding Information: | Funder Grant number THE DEPARTMENT FOR BUSINESS, ENERGY & INDUSTRIAL STRATEGY 527071841 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Sep 2022 16:10 |
Last Modified: | 26 Sep 2022 16:10 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.egyr.2022.08.222 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190847 |