Research on sparse identification method for aeroelastic dynamic response prediction

Nonlinear aeroelastic system has the characteristics of complex structure, difficult modeling and difficult calculation of dynamic response. For the analysis of nonlinear aeroelastic systems, model identification is a very attractive method. However, the models identified by traditional methods are often relatively complex and limited in scope of use, so it is necessary to develop an interpretable equivalent reduced model. In this paper, sparse regression method and sequential threshold least squares technique are used to establish sparse identification method for complex aeroelastic systems. This method has the ability to identify reduced models containing only required nonlinear terms through measurement data. Then, the sparse identification method is used to identify the binary wing with dead zone nonlinearity and cubic stiffness nonlinearity. The obtained model can provide rapid and accurate prediction of the response of the system according to the sensor measurement, and can also be used as an explicit surrogate model for aeroelastic optimization design, thus verifying the superiority of the proposed method.

Automated summary

Nonlinear aeroelastic systems are difficult to model and calculate due to their complex structure and dynamic response. Model identification is an attractive method for analyzing such systems. However, traditional methods often produce complex models with limited applicability, necessitating the development of interpretable reduced models. This paper proposes a sparse identification method for complex aeroelastic systems using the sparse regression method and sequential threshold least squares technique. The identified models contain only the necessary nonlinear terms based on measurement data. The method is applied to identify a binary wing with dead zone nonlinearity and cubic stiffness nonlinearity, and the resulting model enables rapid and accurate prediction of system response and serves as an explicit surrogate model for aeroelastic optimization design, demonstrating its superiority.