Experiment / simulation integrated shape optimization using variable fidelity Kriging model approach

Yamazaki, Wataru

doi:10.1299/jamdsm.2017jamdsm0053

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…x n m = x n org ± s i stepsize (10) This leads to a matrix of n parameters and m inner steps. In the existing setup with n = 4 and m = 3, the size is 4 3 = 64 values for each outer step.…”

Section: Optimisation Methodsmentioning

confidence: 99%

“…Studies with scope to optimise the vertical-axis turbines concentrate mostly on the search for an optimal blade geometry [4][5][6][7][8][9][10]. Different studies mostly based on computational fluid dynamics (CFD) with fully automatised simulations [6] coupled with genetic algorithms were carried out [7,8], e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Significant improvements in the performance with an optimised blade shape were reported. Other studies were based on experiments coupled with simulations [9,10]. Also, the guiding vanes and collector devices are subject of the ongoing research [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental investigation of an optimised pitch control for a vertical‐axis turbine

Abbaszadeh

Hoerner

Maı̂tre

et al. 2019

IET Renewable Power Generation

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…x n m = x n org ± s i stepsize (10) This leads to a matrix of n parameters and m inner steps. In the existing setup with n = 4 and m = 3, the size is 4 3 = 64 values for each outer step.…”

Section: Optimisation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental investigation of an optimised pitch control for a vertical‐axis turbine

Abbaszadeh

Hoerner

Maı̂tre

et al. 2019

IET Renewable Power Generation

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show abstract

“…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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“…Thus, the POD-based re-parameterization approach is promising for efficient aerodynamic shape optimization while it requires a certain amount of computational cost to construct appropriate input database for POD. In this research, a variable fidelity (VF) concept, which is to utilize multi fidelity functional evaluation methods in an optimization system (Kennedy and O'Hagan, 2000;Alexandrov et al, 2001;Han and Görtz, 2012;Yamazaki and Mavriplis, 2013;Yamazaki, 2017), is introduced in the POD-based approach to further reduce the computational cost of multi-objective aerodynamic shape optimizations. So far, the VF optimization approaches were mostly proposed with advanced VF response surface models and/or sequential approximate optimization systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient multi-objective shape optimization using proper orthogonal decomposition with variable fidelity concept

Yamazaki

2020

JAMDSM

Self Cite

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A variable fidelity concept is introduced in a re-parameterization approach based on the proper orthogonal decomposition (POD) to efficiently solve multi-objective aerodynamic shape optimization problems. The re-parameterization approach enables to extract dominant shape deformation modes from a database of good designs and to reduce the number of design variables. The present variable fidelity approach is proposed by utilizing low-fidelity functional evaluations to select the good designs. The proposed approach is investigated in two multi-objective aerodynamic shape optimization problems of 2D airfoil in which the combinations of viscous/inviscid simulations or fine/coarse grid simulations are treated as the high/low-fidelity evaluation methods. It can be confirmed that dominant POD modes obtained from low-fidelity evaluations are qualitatively equivalent with that obtained from high-fidelity evaluations. Non-dominated solutions obtained from a conventional optimization approach can be reproduced with smaller numbers of design variables using the dominant POD modes. The computational costs to solve the multi-objective aerodynamic shape optimization problems can be dramatically reduced by introducing the variable fidelity concept.

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Experiment / simulation integrated shape optimization using variable fidelity Kriging model approach

Cited by 5 publications

References 15 publications

Experimental investigation of an optimised pitch control for a vertical‐axis turbine

Experimental investigation of an optimised pitch control for a vertical‐axis turbine

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

Efficient multi-objective shape optimization using proper orthogonal decomposition with variable fidelity concept

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