0
Technical Briefs

Two-Step Optimization for Wind Turbine Blade With Probability Approach

[+] Author and Article Information
Ki-Hak Lee

School of Mechanical and Aerospace Engineering, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea; Wind Power Engineering Team, Doosan Heavy Industries and Construction Co., Ltd., 463-1 Jeonmin-dong, Yuseong-gu, Daejeon 305-811, Republic of Koreaki_hak.lee@doosan.com

Kyu-Hong Kim1

School of Mechanical and Aerospace Engineering and Institute of Advanced Aerospace Technology, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Koreaaerocfd1@snu.ac.kr

Dong-Ho Lee

School of Mechanical and Aerospace Engineering and Institute of Advanced Aerospace Technology, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Koreadonghlee@snu.ac.kr

Kyung-Tae Lee

School of Mechanical and Aerospace Engineering, Sejong University, 98 Gunja-dong, Kwangjin-ku, Seoul 143-747, Republic of Koreakntlee@sejong.ac.kr

Jong-Po Park

Wind Power Engineering Team, Doosan Heavy Industries and Construction Co., Ltd., 463-1 Jeonmin-dong, Yuseong-gu, Daejeon 305-811, Republic of Koreajongpo.park@doosan.com

1

Corresponding author.

J. Sol. Energy Eng 132(3), 034503 (Jun 29, 2010) (5 pages) doi:10.1115/1.4001671 History: Received May 26, 2009; Revised March 26, 2010; Published June 29, 2010; Online June 29, 2010

A horizontal-axis wind turbine blade is designed using two step optimization procedures with probability approach. For the efficient management of the multiple design variables required for the blade design, the design procedure is divided into two optimization steps. In step 1, the diameter and rotating speed of a blade are determined and design points are extracted from the design space. In step 2-1, blade shapes are optimized by using the strip theory with the minimum energy loss method. The capacity factor and the cost model for each optimized blade shape are calculated in steps 2-2 and 2-3, respectively. To find the global optimum point in the design space, the space is modified into a highly possible region through the use of the probability approach.

FIGURES IN THIS ARTICLE
<>
Copyright © 2010 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Schematic of the flow of design variable

Grahic Jump Location
Figure 2

Wind turbine blade design procedure

Grahic Jump Location
Figure 3

Contour of the objective functions in initial design space

Grahic Jump Location
Figure 4

Pareto front for multi-objective optimization

Grahic Jump Location
Figure 5

Multi-objective function and constraint contours along with the change in design space

Grahic Jump Location
Figure 6

Success probability distribution of the design variables

Grahic Jump Location
Figure 7

CDF of the multi-objective function

Grahic Jump Location
Figure 8

Chord length and twist angle distribution of the optimized blade along the normalized radius

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In