Reliability-Based Design Optimization Wind Turbine to Reduce Levelized Cost of Energy

Professor K.K. Choi and his team have recently collaborated with Professor Hiroyuki Sugiyama and his student Huaxia Li at the University of Iowa to extend it to reliability-based design optimization of wind turbine drivetrain using multibody gear dynamics simulation considering wind load uncertainty. They have improved the gear fatigue life reliability from 8.3% to 97.725% while increasing the gear weight by 1.4%.

Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty.Renewable Energy Global Innovations

reliability-based-design-optimization-of-wind-turbine-blades-for-fatigue-life-under-dynamic-wind-load-uncertainty22-renewable-energy-global-innovations

 

 

 

Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty.Renewable Energy Global Innovations

 

About the author

Weifei Hu received his B.S. (2008) from Zhejiang University, China, M.S. (2010) from Hanyang University, South Korea, and Ph.D. (2015) from University of Iowa, USA, all in mechanical engineering.

Currently, he is a postdoctoral research associate at Cornell University, Ithaca, New York.  Dr. Hu specializes in a wide range of wind energy topics including wind turbine aerodynamics and structure analysis, fatigue analysis of wind turbine composite materials, wind gust detection, wind turbine condition monitoring, and reliability-based design optimization (RBDO) of wind turbine systems.

He is a technical committee member and the secretary (2016-2017) of the Renewable and Advanced Energy Systems committee in the Power Division of ASME. 

About the author

Hyunkyoo Cho earned his B.S. (2003) and M.S (2005) from Seoul National University, South Korea, in Naval Architecture and Ocean Engineering. He received Ph.D. (2014) from University of Iowa in Mechanical Engineering.

In addition, Dr. Cho has five years of industry experience at the Samsung Heavy Industries, South Korea. Currently, he is working as a Postdoctoral Research Scholar and an Adjunct Assistant Professor at University of Iowa. His research has focused on design optimization under input variability and uncertainty, which includes reliability analysis, reliability-based design optimization (RBDO), RBDO using insufficient input data, and applications of RBDO to engineering projects.

About the author

Dr. K.K. Choi is Roy J. Carver Professor in the Mechanical and Industrial Engineering Department at the University of Iowa.  He was appointed as a World Class University Professor at the Seoul National University in Korea during 2008-2013.

His research areas are uncertainty quantification, reliability analysis, reliability-based design optimization, design sensitivity analysis, and mathematical theory of optimization and its applications.  He has co-authored 364 papers, including 152 journal papers in leading national and international engineering journals.

He has co-authored several graduate engineering texts (Design Sensitivity Analysis of Structural System, 1986; Methods of Engineering Mathematics, 1993; Design Sensitivity Analysis of Linear and Nonlinear Structural Systems – Two Volume, 2004).

At the University of Iowa, he is a founding member of the Iowa Board of Regents approved Center for Computer Aided Design (CCAD).  He has served as Associate Director (1990-93), Deputy Director (1993-95), and Director (1995-2003) of CCAD.  He is associate editor of five national and international journals including Journal of Mechanics Based Design of Structures and Machines and Journal of Optimization Theory and Applications.

He is Fellow of American Society of Mechanical Engineers (ASME), Fellow of American Institute of Aeronautics and Astronautics (AIAA), Fellow of Society of Automotive Engineering (SAE), and President Elect of the International Society for Structural and Multidisciplinary Optimization (ISSMO, 2007-2011). 

Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty

Journal Reference

Structural and Multidisciplinary Optimization, October 2016, Volume 54, Issue 4, pp 953–970.

Weifei Hu, K. K. Choi, Hyunkyoo Cho.

Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, USA

Abstract

This paper studies reliability-based design optimization (RBDO) of a 5-MW wind turbine blade for designing reliable as well as economical wind turbine blades. A novel dynamic wind load uncertainty model has been developed using 249 groups of wind data to consider wind load variation over a large spatiotemporal range. The probability of fatigue failure during a 20-year service life is estimated using the uncertainty model in the reliability-based design optimization process and is reduced to meet a desired target reliability. Meanwhile, the cost of composite materials used in the blade is minimized by optimizing the composite laminate thicknesses of the blade.

In order to obtain the reliability-based design optimization optimum design efficiently, deterministic design optimization (DDO) of the 5-MW wind turbine blade is carried out first using the mean wind load obtained from the wind load uncertainty model. The reliability-based design optimization is then initiated from the DDO optimum. During the reliability-based design optimization iterations, fatigue hotspots for reliability-based design optimization are identified among the laminate section points.

For an efficient reliability-based design optimization process, surrogate models of 10-min fatigue damages D10 at the hotspots are accurately created using the Kriging method. Using the wind load uncertainty model and surrogate models, probability of fatigue failure during a 20-year lifespan at the hotspots and the design sensitivities are calculated at given design points. Using the probability of fatigue failure and design sensitivity, reliability-based design optimization of the 5-MW wind turbine blade has been successfully carried out, satisfying the target probability of failure of 2.275 %.

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