Wind turbine design through evolutionary algorithms based on surrogate CFD methods

Díaz-Casás, Vicente; Becerra, J. A.; Peña, Fernando López; Duro, Richard J.

doi:10.1007/s11081-012-9187-1

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Two types of correlation functions can be used for the SM: the exponential (Ornstein-Uhlenbeck process) and the Gaussian correlation function, given by Eq. 11and (12), respectively.…”

Section: = ( )mentioning

confidence: 99%

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Surrogate modelling for high-lift multi-element hydrofoil shape optimization of a hydrokinetic turbine blade

Rubio-Clemente¹,

Aguilar²,

Chica³

2020

REPQJ

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The hydrodynamic shape of a blade is one of the most important factors in the design process of a horizontal axis hydrokinetic turbine that influences its performance. The present work is focused on the design and hydrodynamic analysis of a high-lift system using the optimization method of surrogate models and computational fluid dynamics (CFD) analysis. The parameters that affect the amount of the lift and the drag force that a hydrofoil can generate are the gap, the overlap, the flap deflection angle (δ), the flap chord length (C2) and the angle of attack of the hydrofoil (α). These factors were varied to examine the turbine performance in terms of the ratio between the lift (CL) and the drag coefficient (CD), and the minimum negative pressure coefficient (min Cpre) in order to avoid the cavitation inception. For this propose, surrogate models were implemented to analyse the CFD results and find the optimal combination of the design parameters of the high-lift hydrofoil. The traditional Eppler 420 hydrofoil was utilized for the design of the multi-element profile, which was composed of a main element and a flap. The multi-element design selected as optimal had a gap of 2.825 %C1, an overlap of 8.52 %C1, a δ of 19.765˚, a C2 of 42.471 %C1 and a α of-4˚, where C1 refers to the chord length of the main element. In comparison with the traditional Eppler 420 hydrofoil, CL/CD ratio increases from 39.050 to 42.517.

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“…Two types of correlation functions can be used for the SM: the exponential (Ornstein-Uhlenbeck process) and the Gaussian correlation function, given by Eq. 11and (12), respectively.…”

Section: = ( )mentioning

confidence: 99%

“…SM are successfully used by various researches to optimize wind turbine performance [11][12]. These authors reported that SM are very efficient in comparison with the conventional optimization methods [11][12]. However, the optimization of multi-element hydrofoils for hydrokinetic turbines using SM has not been employed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Surrogate modelling for high-lift multi-element hydrofoil shape optimization of a hydrokinetic turbine blade

Rubio-Clemente¹,

Aguilar²,

Chica³

2020

REPQJ

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“…The optimization problem in this work attempts to minimize the mass of the wind turbine blade while the design variables are the chord and twist distributions. Other wind turbine design based on CFD and surrogate models are demonstrated in [12,13], while some of the other surrogate models used for wind turbine blade optimization in the literature include the KG model [12] and artificial neural networks (ANN) [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Kriging Surrogate-Based Genetic Algorithm Optimization for Blade Design of a Horizontal AxisWind Turbine

Pholdee¹,

SujinBureerat²,

Nuantong³

2021

Computer Modeling in Engineering &Amp; Sciences

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Horizontal axis wind turbines are some of the most widely used clean energy generators in the world. Horizontal axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultaneously minimize the cost of energy. This work presents the optimization of new MEXICO blades for a horizontal axis wind turbine at the wind speed of 10 m/s. The optimization problem is posed to maximize the power coefficient while the design variables are twist angles on the blade radius and rotating axis positions on a chord length of the airfoils. Computational fluid dynamics was used for the aerodynamic simulation. Surrogate-assisted optimization was applied to reduce computational time. A surrogate model called a Kriging model, using a Gaussian correlation function along with various regression models, was applied while a genetic algorithm was used as an optimizer. The results obtained in this study are discussed and compared with those obtained from the original model. It was found that the Kriging model with linear regression gives better results than the Kriging model with second-order polynomial regression. The optimum blade obtained in this study showed better performance than the original blade at a low wind speed of 10 m/s.

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“…Therefore, the automatic optimization methodologies using the geometrical parameters of the cross-sections of the draft tube were previously examined, but these methods had not considered the geometrical parameters such as the median section affecting significantly on the performance of the draft tube [7][8][9]. While, the multi-objective optimization methods have been employed in the optimization design of turbomachinery [10][11][12], and the optimization methods in the engineering design have been also suggested using the CFD, DOE technique and multi-objective genetic algorithm [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of Draft Tube in Francis Turbine Using DOE, RBF and NSGA-II

Mun¹,

Ba²,

Yue³

et al. 2017

Preprint

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Abstract:In order to improve the performance of the draft tube in hydraulic turbine, a multi-objective optimization method for the draft tube is developed by combining the design of experiment (DOE), the radial basis function (RBF) and the non-dominated sorting genetic algorithm (NSGA-II) in this paper. The geometrical design variables of the median section in the draft tube and the cross section in its exit diffuser are considered as design parameters in this optimization, which objective function is to maximize the pressure recovery factor (Cp) and minimize the energy loss coefficient (ζ). The limited numbers of design matrix required for the shape optimization of the draft tube is generated by optimal Latin hypercube (OLH) method of the DOE technique, of which performances are evaluated through computational fluid dynamic (CFD) numerical simulation. For reducing of the computational consumption, the approximate model is used based on the RBF. The Pareto optimal solutions are finally performed using the NSGA-II for obtaining the best geometrical parameters of the draft tube. The optimization result of the draft tube shows a marked performance improvement over the original, which verifies the theoretical validity and feasibility of the proposed method in this paper.

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Wind turbine design through evolutionary algorithms based on surrogate CFD methods

Cited by 10 publications

References 13 publications

Surrogate modelling for high-lift multi-element hydrofoil shape optimization of a hydrokinetic turbine blade

Surrogate modelling for high-lift multi-element hydrofoil shape optimization of a hydrokinetic turbine blade

Kriging Surrogate-Based Genetic Algorithm Optimization for Blade Design of a Horizontal AxisWind Turbine

Multi-objective Optimization of Draft Tube in Francis Turbine Using DOE, RBF and NSGA-II

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