Minimizing erosive wear through a CFD multi-objective optimization methodology for different operating points of a Francis turbine

R.D. Aponte, L.A. Teran, J.F. Grande, J.J. Coronado, J.A. Ladino, F.J. Larrahondo and S.A. Rodríguez

Renewable Energy, 2020, vol. 145, issue C, 2217-2232

Abstract: Erosive wear has been a serious concern in mainly run-of-the-river medium and small Francis turbines from both economic and technical perspectives. With the aim of finding ways to mitigate erosive wear, this paper proposes a methodology to obtain, via an optimization approach, geometries that maximize the resistance to erosive wear by hard particles and cavitation of the internal components (runner, guide vanes and cover labyrinths) of a Francis turbine. This improvement was implemented to reduce the costs of corrective maintenance and to maximize the machines’ availability and energy generation profits. The methodology used computational fluid dynamics (CFD) and optimization techniques, such as the design of experiments of the factorial type, artificial neural networks and genetic algorithms with a multi-point approach, which includes two operation points, and a multi-objective approach, which simultaneously considers erosive wear by hard particles, cavitation damage and efficiency. It was found that the new geometries of the analysed components of the turbine can allow a decrease of up to 73% in the wear rate, maintaining an efficiency close to the original value throughout the operating range. With the optimized geometry, a mechanical check was performed using finite element simulations to validate that the optimal geometries had the required strength.

Keywords: Erosion wear; Computational fluid dynamics; Francis turbines; Optimization; Genetic algorithms; Artificial neural networks (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014811931136X
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:145:y:2020:i:c:p:2217-2232

DOI: 10.1016/j.renene.2019.07.116

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 ().