Optimization of thick wind turbine airfoils using a genetic algorithm

Jeong, Jae-Ho; Kim, Soohyun

doi:10.1007/s12206-018-0622-x

Cited by 28 publications

(13 citation statements)

References 16 publications

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“…A popular PBO method in aerodynamic shape optimization is the genetic algorithm (GA) [14,17,35,36]. It mimics the process of biological evolution through mutation, recombination, and reproduction of different designs.…”

Section: Gradient-free Methodsmentioning

confidence: 99%

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Chen¹,

Chiu²,

Fuge³

2020

Preprint

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Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because of the high dimensionality of the design space. One approach to combat this problem is to reduce the design space dimension by obtaining a new representation. This requires a parametric function that compactly and sufficiently describes useful variation in shapes. We propose a deep generative model, Bézier-GAN, to parameterize aerodynamic designs by learning from shape variations in an existing database. The resulted new parameterization can accelerate design optimization convergence by improving the representation compactness while maintaining sufficient representation capacity. We use the airfoil design as an example to demonstrate the idea and analyze Bézier-GAN's representation capacity and compactness. Results show that Bézier-GAN both (1) learns smooth

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Section: Gradient-free Methodsmentioning

confidence: 99%

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Chen¹,

Chiu²,

Fuge³

2020

Preprint

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“…Evolutionary algorithms (EA) are gradient-free optimization algorithms that mimic the process of biological evolution through mutation, recombination, and reproduction of different designs. Genetic algorithms (GA), a type of EA, is widely used in aerodynamic shape optimization [12,15,20]. Work has also been done to augment GA with the Bees algorithm [21] and adaptive mutation rates [22], resulting in more accurate optimization and/or faster convergence.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

Chen

Chiu

Fuge

2019

AIAA Scitech 2019 Forum

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Global optimization of aerodynamic shapes requires a large number of expensive CFD simulations because of the high dimensionality of the design space. One means to combat that problem is to reduce the dimension of the design space-for example, by constructing low dimensional parametric functions (such as PARSEC and others)-and then optimizing over those parameters instead. Such approaches require first a parametric function that compactly describes useful variation in airfoil shape-a non-trivial and error-prone task. In contrast, we propose to use a deep generative model of aerodynamic designs (specifically airfoils) that reduces the dimensionality of the optimization problem by learning from shape variations in the UIUC airfoil database. We show that our data-driven model both (1) learns realistic and compact airfoil shape representations and (2) empirically accelerates optimization convergence by over an order of magnitude.

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“…Therefore, to withstand the increased load while maintaining the aerodynamic performance of the blades, the optimal design and placement of airfoils in the spanwise direction are two of the most important aspects of blade design [4]. Rotor blades are designed based on a combination of several airfoils with different thickness values depending on their spanwise position on the blade [5]. To minimize the aerodynamic load on the rotor blade as it becomes longer, the outboard rotor blade is usually made sharper, which increases the blade root bending moment.…”

Section: Introductionmentioning

confidence: 99%

Pix2Pix and Deep Neural Network-Based Deep Learning Technology for Predicting Vortical Flow Fields and Aerodynamic Performance of Airfoils

2023

Self Cite

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Traditional computational fluid dynamics (CFD) methods are usually used to obtain information about the flow field over an airfoil by solving the Navier–Stokes equations for the mesh with boundary conditions. These methods are usually costly and time-consuming. In this study, the pix2pix method, which utilizes conditional generative adversarial networks (cGANs) for image-to-image translation, and a deep neural network (DNN) method were used to predict the airfoil flow field and aerodynamic performance for a wind turbine blade with various shapes, Reynolds numbers, and angles of attack. Pix2pix is a universal solution to the image-to-image translation problem that utilizes cGANs. It was successfully implemented to predict the airfoil flow field using fully implicit high-resolution scheme-based compressible CFD codes with genetic algorithms. The results showed that the vortical flow fields of the thick airfoils could be predicted well using the pix2pix method as a result of deep learning.

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Optimization of thick wind turbine airfoils using a genetic algorithm

Cited by 28 publications

References 16 publications

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

Pix2Pix and Deep Neural Network-Based Deep Learning Technology for Predicting Vortical Flow Fields and Aerodynamic Performance of Airfoils

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