Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow

An aerodynamic and structural integrated design optimization method of composite wind turbine blade based on multidisciplinary design optimization (MDO) is presented. The optimization aims to reduce the mass of blade under some constraints, including the power and deflection at the rated wind speed, and the strength and deflection under ultimate case. The design variables include parameters both in aerodynamic and structural disciplines. In order to keep the shape of blade smooth,the chord and twist distributions are controlled by the Bezier function in the optimization process. 3D parameterization of blade was carried out in Finite Element Analysis (FEA) software. Considering tip-loss and hub-loss, aerodynamic analysis was performed by using Blade Element Momentum (BEM) theory. Finite Element Method (FEM) was used in structural analysis. Multi-island Genetic Algorithm (MIGA) which has excellent exploration abilities was used to optimize wind turbine blade. RBF meta-model was construct to approximate the accurate structural analysis model by Optimal Latin Hypercube DOE sample points. An example was given to verify the method in this paper. The result shows that the optimization method has good optimization efficiency and the RBF meta-model could reduce the computational cost a lot.

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Multi-Objective Design Optimization of Wind Turbine Blade Bearing

Invertis Journal of Science & Technology ◽

10.5958/2454-762x.2021.00012.3 ◽

2021 ◽

Vol 14 (3) ◽

pp. 114-121

Author(s):

Prasun Bhattacharjee ◽

Rabin K. Jana ◽

Somenath Bhattacharya

Keyword(s):

Wind Turbine ◽

Design Optimization ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Multi Objective

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Optimisation framework for distinctive vertical axis wind turbine blade generation using hybrid multi-objective genetic algorithms & deep neural networks

AIAA Aviation 2019 Forum ◽

10.2514/6.2019-3241 ◽

2019 ◽

Author(s):

Joe Joseph ◽

Pranshu Pant

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Wind Turbine ◽

Turbine Blade ◽

Deep Neural Networks ◽

Vertical Axis ◽

Wind Turbine Blade ◽

Vertical Axis Wind Turbine ◽

Multi Objective

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Identification of Wind Turbine Response to Turbulent Inflow Structures

Volume 1: Fora, Parts A, B, C, and D ◽

10.1115/fedsm2003-45360 ◽

2003 ◽

Cited By ~ 10

Author(s):

M. Maureen Hand ◽

Neil D. Kelley ◽

Mark J. Balas

Keyword(s):

Wind Turbine ◽

Turbine Blade ◽

Wind Turbine Blade ◽

Load Parameter ◽

Fatigue Load ◽

Large Magnitude ◽

Turbulent Structures ◽

Turbulent Fluctuations ◽

Statistical Measures ◽

Turbulent Inflow

The National Renewable Energy Laboratory conducted an experiment to obtain detailed wind measurements and corresponding wind turbine measurements in order to establish a causal relationship between coherent turbulent structures and wind turbine blade fatigue loads. Data were collected for one entire wind season from October 2000 to May 2001. During this period, the wind turbine operated under atmospheric conditions that support the formation of coherent turbulent structures 31% of the time. Using the equivalent fatigue load parameter as a measure of wind turbine blade fatigue and using statistical measures of the turbulent fluctuations of the wind, general correlation between the turbulence and the wind turbine response is shown. Direct correlation cannot be resolved with 10-minute statistics for several reasons. Multiple turbulent structures can exist within a 10-minute record, and the equivalent fatigue load parameter is essentially a 10-minute statistic that cannot estimate turbine response to individual turbulent structures. Large-magnitude turbulent fluctuations in the form of instantaneous Reynolds stresses do not necessarily correspond directly to large-magnitude blade root moment amplitudes. Thus, additional work must be done to quantify the negative turbine response and to correlate this response to turbulent inflow parameters over time scales less than 10 minutes.

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Structural design optimization of a wind turbine blade using the genetic algorithm

Engineering Optimization ◽

10.1080/0305215x.2021.1973450 ◽

2021 ◽

pp. 1-18

Author(s):

Sang-Lae Lee ◽

Sang Joon Shin

Keyword(s):

Genetic Algorithm ◽

Wind Turbine ◽

Design Optimization ◽

Turbine Blade ◽

Structural Design ◽

Wind Turbine Blade ◽

Structural Design Optimization

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Multidisciplinary Design Optimization of a Hybrid Composite Wind Turbine Blade

ASME 2011 International Manufacturing Science and Engineering Conference, Volume 2 ◽

10.1115/msec2011-50204 ◽

2011 ◽

Author(s):

Jin Woo Lee ◽

Sathya N. Gangadharan ◽

Maj Mirmirani ◽

Amanda Raffa

Keyword(s):

Wind Turbine ◽

Design Optimization ◽

Turbine Blade ◽

Hybrid Composite ◽

Multidisciplinary Design Optimization ◽

Excitation Frequency ◽

Wind Turbine Blade ◽

Multidisciplinary Design ◽

Manufacturing Cost ◽

Wind Condition

A multidisciplinary design optimization (MDO) process of a large scale hybrid composite wind turbine blade is developed. Multiple objectives are considered in this design optimization: maximize length of blade, minimize weight and manufacturing cost. A wind turbine blade is divided into regions and the layup sequences for each region are considered as design variables. Applied load due to extreme wind condition for rotor rotation and rotor stop condition are considered for finite element analysis (FEA) to evaluate the structural strength. The structural stiffness is designed and illustrated so that the natural frequency of the blade does not coincidence with the excitation frequency of the wind turbine. A process of obtaining an optimum hybrid composite laminate layup and an optimum length of wind turbine blade is developed in this research.

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