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Energies 2017, 10(11), 1901; https://doi.org/10.3390/en10111901

An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

1,2,* , 1,2,* , 1,2
,
1,2
,
1,2
and
3
1
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
3
Institute of Water Resources and Hydropower Research, Northwest A&F University, Yangling 712100, China
*
Authors to whom correspondence should be addressed.
Received: 14 October 2017 / Revised: 10 November 2017 / Accepted: 12 November 2017 / Published: 19 November 2017
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Abstract

In this paper, a new methodology is proposed to reduce the vortex-induced vibration (VIV) and improve the performance of the stay vane in a 200-MW Francis turbine. The process can be divided into two parts. Firstly, a diagnosis method for stay vane vibration based on field experiments and a finite element method (FEM) is presented. It is found that the resonance between the Kármán vortex and the stay vane is the main cause for the undesired vibration. Then, we focus on establishing an intelligent optimization model of the stay vane’s trailing edge profile. To this end, an approach combining factorial experiments, extreme learning machine (ELM) and particle swarm optimization (PSO) is implemented. Three kinds of improved profiles of the stay vane are proposed and compared. Finally, the profile with a Donaldson trailing edge is adopted as the best solution for the stay vane, and verifications such as computational fluid dynamics (CFD) simulations, structural analysis and fatigue analysis are performed to validate the optimized geometry. View Full-Text
Keywords: intelligent optimization method; stay vane; vortex-induced vibration (VIV); extreme learning machine (ELM); computational fluid dynamics (CFD) intelligent optimization method; stay vane; vortex-induced vibration (VIV); extreme learning machine (ELM); computational fluid dynamics (CFD)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Peng, X.; Zhou, J.; Zhang, C.; Li, R.; Xu, Y.; Chen, D. An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine. Energies 2017, 10, 1901.

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