Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade

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@Article{ARSLAN:2023:engappai,

• author = "Sibel Arslan and Kemal Koca",
• title = "Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade",
• journal = "Engineering Applications of Artificial Intelligence",
• volume = "123",
• pages = "106210",
• year = "2023",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2023.106210",
• URL = "https://www.sciencedirect.com/science/article/pii/S0952197623003949",
• keywords = "genetic algorithms, genetic programming, Automatic programming, Artificial bee colony programming, Aerodynamic coefficients, Power efficiency, Wind turbine blade",
• abstract = "Automatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are used for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for RTrain2 and RTest2 (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time",

}

Genetic Programming entries for Sibel Arslan Kemal Koca

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