Numerical models for robust shape optimization of wind turbine blades

Damir Vučina, Ivo Marinić-Kragić and Zoran Milas

Renewable Energy, 2016, vol. 87, issue P2, 849-862

Abstract: A computational framework for the shape optimization of wind turbine blades is developed for variable operating conditions specified by local wind speed distributions. The numerical workflow consists of a genetic algorithm based optimizer, a computational fluid dynamics based simulator and a 3D geometric modeller. The developed numerical workflow also implements the coupling of the process flows as well as passing data amongst the individual applications including the corresponding data mining. Several approaches to modeling 3D shapes are developed and employed by the workflow. They include parametric curves defining 2D curves lofted into 3D shapes in combination with applying computational geometry operators and full 3D parametric surface models which enable generic 3D shapes to be represented. The proposed definitions of excellence include annual energy production for given wind speed distributions and net-present-value and internal-rate-of-return based indicators as potential constituents of the fitness functions. Several case studies are presented with promising results towards the aspired custom-shaped wind turbine blades for optimum performance for any given specific location. The developed computational workflow can therefore be seen as a numerical device for custom optimization of performance of renewable energy systems.

Keywords: Parametric 3D shape optimization; Robust optimization; Wind turbine; CFD (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148115303931
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:87:y:2016:i:p2:p:849-862

DOI: 10.1016/j.renene.2015.10.040

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().