Hydrodynamic performance of vertical axis hydrokinetic turbine based on Taguchi method

Yong Ma, Yuanyao Zhu, Aiming Zhang, Chao Hu, Sen Liu and Zhengyu Li

Renewable Energy, 2022, vol. 186, issue C, 573-584

Abstract: Hydropower generation is an important part of global renewable energy development. The vertical axis hydrokinetic turbine (VAHT) is a typical device to capture the kinetic energy of water. The optimization of its hydrodynamic performance can improve power coefficient. In this study, the Taguchi method was used to optimize the typical parameters of the vertical axis turbine, i.e., airfoil (NACA), pitch angle (β), enwinding ratio (ϖ), solidity ratio (σ), and small shaft position (O). An orthogonal array with five parameters and four levels was established; then, sixteen runs were simulated using computational fluid dynamics (CFD) software. A signal-to-noise (S/N) ratio analysis of the CFD simulation results was carried out, and the hydrodynamic performance of the optimized VAHT was studied. The results show that the influence strength order of each parameter on the power coefficient of the VAHT is featured by the relation NACA >σ > O > ϖ > β. According to the S/N ratio, the optimal combination of the five factors is as follows: NACA = 0020, β = 0°, ϖ = 1.25, σ = 0.382, and O = 0.5. In comparison with the initial design, the optimized minimum self-starting torque coefficient of the turbine is increased by 15.9%, which means that the self-starting performance is optimized. The power coefficient (CP) of optimized turbine is 0.1951, which is 17.59% higher than in the initial design, and the power coefficient fluctuation is 87.56% lower. Finally, the research findings could provide a reference for the optimal design of vertical axis turbines.

Keywords: Vertical axis turbine; Hydrodynamic performance; Power coefficient; Self-starting capability; Power coefficient fluctuation; Taguchi method (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:186:y:2022:i:c:p:573-584

DOI: 10.1016/j.renene.2022.01.037

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