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

Computer Modeling in Engineering & Sciences 2021年第1期126卷 261-273页

作者： nantiwat pholdee Sujin Bureerat Weerapon Nuantong Sustainable Infrastructure Research and Department of Mechanical Engineering Khon Kaen UniversityKhon Kaen40002Thailand Department of Mechatronics Engineering Rajamangala University of Technology Isan Khon Kaen CampusKhon Kaen40000Thailand

Horizontal axis wind turbines are some of the most widely used clean energy generators in the *** axis wind turbine blades need to be designed for optimization in order to maximize efficiency and simultaneously minimize the cost of *** work presents the optimization of new MEXICO blades for a horizontal axis wind turbine at the wind speed of 10 m/*** 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 *** fluid dynamics was used for the aerodynamic ***-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 *** results obtained in this study are discussed and compared with those obtained from the original *** was found that the Kriging model with linear regression gives better results than the Kriging model with second-order polynomial *** optimum blade obtained in this study showed better performance than the original blade at a low wind speed of 10 m/s.

关键词： Wind turbine optimization kriging genetic algorithms Gaussian

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Airfoil Shape Optimisation Using a Multi-Fidelity Surrogate-Assisted Metaheuristic with a New Multi-Objective Infill Sampling Technique

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Computer Modeling in Engineering & Sciences 2023年第12期137卷 2111-2128页

作者： Cho Mar Aye Kittinan Wansaseub Sumit Kumar Ghanshyam G.Tejani Sujin Bureerat Ali R.Yildiz nantiwat pholdee Sustainable and Infrastructure Research and Development Center Department of Mechanical EngineeringFaculty of EngineeringKhon Kaen UniversityKhon Kaen40002Thailand Department of Mechanical Engineering Faculty of EngineeringMahasarakham UniversityMahasarakham44150Thailand Australian Maritime College College of Science and EngineeringUniversity of TasmaniaLaunceston7248Australia Department of Mechanical Engineering School of TechnologyGSFC UniversityVadodaraGujarat391750India Department of Mechanical Engineering Bursa Uludag UniversityBursa16059Türkiye

This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape *** optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry *** Fluid Dynamic(CFD)and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value.A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model *** validate and further assess the proposed methods,a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied with multi-fidelity simulation,while their numerical performance is *** results obtained show that the proposed technique is the best performer for the demonstrated airfoil shape *** to this study,applying multi-fidelity with multi-objective infill sampling criteria for surrogate-assisted optimization is a powerful design tool.

关键词： Multi-fidelity modelling differential evolution kriging infill sampling criteria metaheuristics

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