Robust Optimization of Wind Turbine’s Airfoil Under Geometric Uncertainty using Surrogate-Assisted Memetic Algorithm
Yohanes Bimo Dwianto1, Pramudita Satria Palar2, Lavi Rizki Zuhal3
1Yohanes Bimo Dwianto, Bandung Institute of Technology, Jl. Ganesha No, West Java Indonesia.
2Pramudita Satria Palar, Bandung Institute of Technology, Jl. Ganesha No, West Java Indonesia.
3Lavi Rizki Zuhal, Bandung Institute of Technology, Jl. Ganesha No, West Java Indonesia.
Manuscript received on 25 May 2019 | Revised Manuscript received on 03 June 2019 | Manuscript Published on 22 June 2019 | PP: 364-367 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C10750283S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Robust optimization was conducted for wind turbine’s airfoil under geometric uncertainty in subsonic region. Since the performance of a wind turbine is highly affected by the aerodynamic characteristics of the blade’s airfoil, aerodynamic efficiency is considered as the optimization objective. More exactly, the mean and standard deviation value of lift-to-drag ratio are the objective functions in this work. By utilizing an advanced optimization framework of Single-Surrogate Multi-Objective Memetic Algorithm (SS-MOMA), some airfoil shapes which are insensitive to geometric uncertainty were obtained. By observing the physical shape of some solutions, it was found that airfoil with higher leading edge radius, maximum upper curve thickness, and curvature tend to produce higher mean of lift-to-drag ratio, while thinner airfoil with sloping curvature tend to produce lower standard deviation of lift-to-drag ratio.
Keywords: Robust Optimization, Geometric Uncertainty, Surrogate-Assisted Memetic Algorithm, Airfoil Shape Optimization, SS-MOMA.
Scope of the Article: Algorithm Engineering