TY - JOUR
T1 - Parameter identification of airfoil systems using an elite-based clustering Jaya algorithm and incremental vibration responses
AU - Ding, Zhenghao
AU - Zhang, Yuxuan
AU - Lu, Zhongrong
AU - Xia, Yong
N1 - Funding Information:
This work is supported by the Key Area R&D Program of Guangdong Province (Project No. 2019B111106001), the National Key R&D Program (Project No. 2019YFB1600700), and the PolyU Postdoctoral Matching Fund (Project No. W18P).
Funding Information:
Funding was provided by Key Area R&D Program of Guangdong Province (2019B111106001), National Key R&D Program (2019YFB1600700), and PolyU Postdoctoral Matching Fund (W18P).
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/7
Y1 - 2022/7
N2 - Airfoil systems generally exhibit a variety of nonlinearity under different wind speeds. To effectively draft strategies to restrain undesirable nonlinearity, airfoil systems’ parameters must be figured out beforehand. In this article, a novel cluster-based Jaya algorithm is proposed to identify airfoil systems’ dimension parameters, nonlinear parameters, and vibration frequencies. In the proposed algorithm, the improvement is focused on introducing the elite-based clustering framework to balance the algorithm’s exploration and exploitation to enhance the convergence rate. To improve the effectiveness and efficiency of the proposed algorithm, eight benchmark functions are introduced to test and compared with other latest optimizations. The comparison results show that the proposed algorithm has better performance in convergence rate and accuracy. Afterward, the proposed algorithm is applied to identify airfoil systems by minimizing the incremental acceleration response-based objective function. Different wind speeds are considered in the numerical simulation of the airfoil system, which reveals bifurcation, quasi-periodic oscillation, and chaos. In all cases, the proposed method yields accurate and robust results even when noisy data are used.
AB - Airfoil systems generally exhibit a variety of nonlinearity under different wind speeds. To effectively draft strategies to restrain undesirable nonlinearity, airfoil systems’ parameters must be figured out beforehand. In this article, a novel cluster-based Jaya algorithm is proposed to identify airfoil systems’ dimension parameters, nonlinear parameters, and vibration frequencies. In the proposed algorithm, the improvement is focused on introducing the elite-based clustering framework to balance the algorithm’s exploration and exploitation to enhance the convergence rate. To improve the effectiveness and efficiency of the proposed algorithm, eight benchmark functions are introduced to test and compared with other latest optimizations. The comparison results show that the proposed algorithm has better performance in convergence rate and accuracy. Afterward, the proposed algorithm is applied to identify airfoil systems by minimizing the incremental acceleration response-based objective function. Different wind speeds are considered in the numerical simulation of the airfoil system, which reveals bifurcation, quasi-periodic oscillation, and chaos. In all cases, the proposed method yields accurate and robust results even when noisy data are used.
KW - Airfoil system
KW - Jaya algorithm
KW - Nonlinear vibration
KW - Parameter identification
UR - http://www.scopus.com/inward/record.url?scp=85134050638&partnerID=8YFLogxK
U2 - 10.1007/s00158-022-03308-8
DO - 10.1007/s00158-022-03308-8
M3 - Journal article
AN - SCOPUS:85134050638
SN - 1615-147X
VL - 65
JO - Structural and Multidisciplinary Optimization
JF - Structural and Multidisciplinary Optimization
IS - 7
M1 - 209
ER -