Aerodynamic Optimization of Airfoils Using Adaptive Parameterization and Genetic Algorithm

Ebrahimi, Morteza; Jahangirian, A.

doi:10.1007/s10957-013-0442-1

Cited by 35 publications

(16 citation statements)

References 11 publications

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“…As there is no direct precedent in the literature with regards to the design of aerofoils where only the C L − α is prescribed at multiple op-erating points, a bespoke solution has been developed. However, general aerofoil design methodologies based on optimisation algorithms are relatively common and can be used to inform this work [21,22].…”

Section: Direct Aerofoil Replacement Methodologymentioning

confidence: 99%

Rotor Scaling Methodologies for Small Scale Testing of Floating Wind Turbine Systems

Martin

Day

Gilmour

2015

Volume 9: Ocean Renewable Energy

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Two scaling methodologies are presented to address the dissimilitude normally experienced when attempting to measure global aerodynamic loads on a small scale wind turbine rotor from a full scale reference. The first, termed direct aerofoil replacement (DAR), redesigns the profile of the blade using a multipoint aerofoil optimisation algorithm, which couples a genetic algorithm (GA) and XFOIL, such that the local non-dimensional lift force is similar to the full scale. Correcting for the reduced Reynolds number in this manner allows for the non-dimensional chord and twist distributions to be maintained at small scale increasing the similitude of the unsteady aerodynamic response; an inherent consideration in the study of the aerodynamic response of floating wind turbine rotors. The second, the geometrically free rotor design (GFRD) methodology, which utilises the Python based multi-objective GA DEAP and blade-element momentum (BEM) code CCBlade, results in a more simplistic but less accurate design.Numerical simulations of two rotors, produced using the defined scaling methodologies, show an excellent level of similarity of the thrust and reasonably good torque matching for the DAR rotor to the full scale reference. The GFRD rotor design is more simplistic, and hence more readily manufacturable, than the DAR, however the aerodynamic performance match to the full scale turbine is relatively poor.

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Section: Direct Aerofoil Replacement Methodologymentioning

confidence: 99%

Rotor Scaling Methodologies for Small Scale Testing of Floating Wind Turbine Systems

Martin

Day

Gilmour

2015

Volume 9: Ocean Renewable Energy

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“…The optimal aerodynamic shape can be solved by x * = arg min x f (x), where x is an aerodynamic shape (expressed in this case by the latent codes and the Bézier curve parameters) and f (x) is some performance measure defined over x (e.g., lift, drag, etc.). Since the function f is usually non-convex, methods such as EA or SBO are often used for optimization [57][58][59][60]. These methods search for the global optimum by exploring the design space X.…”

Section: A the Optimization Problem In The Latent Spacementioning

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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“…Actually, the boundary values of the input parameters have widespread orders. For example, the domain of changes for the rst PARSEC parameter (r LE ) is [0.006-0.0115] where the domain of the fth parameter ( TE ) is [0][1][2][3][4][5][6][7][8][9][10]. This causes the e ects of the higherorder parameters to be much more than those of the lower-order parameters during the training process.…”

Section: Pre-processing the Training Datamentioning

confidence: 99%

“…The applications of this method to the airfoil shape optimization are presented in [1,2,[5][6][7]. However, the unfavorable key about GA is the computational time consumed in aerodynamic shape optimization problems when Computational Fluid Dynamic (CFD) methods are used for tness function calculation.…”

Section: Introductionmentioning

confidence: 99%

An Optimum Neural Network for Evolutionary Aerodynamic Shape Design

2017

Self Cite

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Abstract. Two new techniques are proposed to enhance the estimation abilities of the conventional Neural Network (NN) method in its application to the tness function estimation of aerodynamic shape optimization with the Genetic Algorithm (GA). The rst technique is pre-processing the training data in order to increase the training accuracy of the Multi-Layer Perceptron (MLP) approach. The second technique is a new structure for the network to improve its quality through a modi ed growing and pruning method. Using the proposed techniques, one can obtain the best estimations from the NN with less computational time. The new methods are applied for optimum design of a transonic airfoil and the results are compared with those obtained from the accurate Computational Fluid Dynamics (CFD) tness evaluator and with the conventional MLP NN approach. The numerical experiments show that using the new method can reduce the computational time signi cantly while achieving improved accuracy.

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Aerodynamic Optimization of Airfoils Using Adaptive Parameterization and Genetic Algorithm

Cited by 35 publications

References 11 publications

Rotor Scaling Methodologies for Small Scale Testing of Floating Wind Turbine Systems

Rotor Scaling Methodologies for Small Scale Testing of Floating Wind Turbine Systems

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

An Optimum Neural Network for Evolutionary Aerodynamic Shape Design

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