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http://dx.doi.org/10.5139/JKSAS.2009.37.5.425

Study of Neural Network Training Algorithm Comparison and Prediction of Unsteady Aerodynamic Forces of 2D Airfoil  

Kang, Seung-On (서울대학교 기계항공공학부 대학원)
Jun, Sang-Ook (서울대학교 기계항공공학부 대학원)
Park, Kyung-Hyun (서울대학교 기계항공공학부 대학원)
Jeon, Yong-Hee (서울대학교 기계항공공학부 대학원)
Lee, Dong-Ho (서울대학교 기계항공공학부/항공우주신기술연구소)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.37, no.5, 2009 , pp. 425-432 More about this Journal
Abstract
In this study, the ability of neural network in modeling and predicting of the unsteady aerodynamic force coefficients of 2D airfoil with the data obtained from Euler CFD code has been confirmed. Neural network models are constructed based on supervised training process using Levenberg-Marquardt algorithm, combining this into genetic algorithm, hybrid genetic algorithm and the efficiency of the two cases are analyzed and compared. It is shown that hybrid-genetic algorithm is more efficient for neural network of complex system and the predicted properties of the unsteady aerodynamic force coefficients of 2D airfoil by the neural network models are confirmed to be similar to that of the numerical results and verified as suitable representing reduced models.
Keywords
Neural Network; Levenberg-Marquardt Algorithm; Hybrid Genetic Algorithm; Unsteady Aerodynamic Force; Reduced Model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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