In this paper, a stochastic optimization of a horizontal-axis composite wind turbine blade is performed. Wind energy has become widely popular in recent decades as an alternative source of energy due to its environmental benefits. And, many studies have been devoted to finding an optimal design for wind turbine blades that can produce sufficient energy with low costs as possible using deterministic optimization. However, the actual responses of the wind turbine blades obtained using the deterministic approach are highly sensitive to the uncertainties that are introduced during the design phase - due to lack of information or data - as well as during fabrication and transportation to the installation site. These uncertainties have detrimental effects on the performance and reliability of wind turbine blades, which demands consideration of uncertainties during the design phase. Therefore, the goal of this paper is to present a stochastic optimization framework to obtain an optimal wind turbine blade that is less sensitive to uncertainties and meets the safety requirements even in the presence of uncertainties. To this end, a surrogate-based optimization framework using high accuracy and computationally efficient techniques such as Gaussian Process (GP) regression and Polynomial Chaos Expansion (PCE) are implemented. Furthermore, a global sensitivity analysis using Sobol indices is carried out for potential dimension reduction and computational savings.

The stochastic optimization problem being tackled in this study includes the maximization of annual energy production (AEP) using less material as possible (weight of the rotor blade) while satisfying the reliability constraints on static strength, maximum tip displacement, and fatigue life. The design variables considered are the rotor length (blade span), and chord and twist distribution of the blade whereas the wind loading (mean speed, turbulence intensity), material properties, and thickness are considered as the uncertain parameters.

Currently, the authors have successfully generated a parametric model of wind turbine rotor blade in ANSYS Parametric Language (APDL) and have validated the APDL code with a baseline NREL 5 MW (\\textit{61.0 m} long) wind turbine blade studied by the SANDIA National Laboratory researchers. The ongoing work includes the integration of the blade element momentum (BEM) being coded in MATLAB and the Xfoil software to obtain the aerodynamic loads, which will also allow for estimation of the power generation. Similarly, the APDL will be integrated into the stochastic optimization framework to facilitate the updating of the rotor blade geometry and for the finite element analysis to estimate the rotor blade response to aerodynamic loads.

Therefore, this study will provide an optimum wind turbine blade design that is less sensitive to uncertainties and will satisfy the reliability constraints in unforeseen conditions. This will also lead to greater returns by using fewer resources as possible and accelerate the adoption of wind turbines as an alternative source of energy, thereby reducing the use of fossil fuels.

Keywords: Stochastic optimization, Wind turbine blade, Uncertainty, Blade element momentum, Composite structures.


Stochastic Optimization of a Horizontal-Axis Composite Wind Turbine Blade

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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