Parameter identification of airfoil systems using an elite-based clustering Jaya algorithm and incremental vibration responses

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.