Efficient prediction of transonic flutter boundaries for varying Mach number and angle of attack via LSTM network

Abstract

Transonic aeroelastic analysis can be carried out accurately and efficiently by using the aerodynamic Reduced-Order Modeling (ROM) approach. However, the efficiency and generalization capability of traditional time-dependent ROM should be further enhanced, especially when dealing with the case for varying flight parameters. For such a purpose, a set of flight samples for different Mach numbers and mean angles of attack in transonic regime are selected to cover the concerned parameter space. Subsequently, a typical filtered white Gaussian noise is used as the input signal to excite the dynamical behavior of the aerodynamic system via the direct Computational Fluid Dynamic (CFD) technique, and the corresponding input and output data at all the flight samples are used as the training data set. Afterwards, based on the CFD training data set, the dynamical relationship between aerodynamic output and displacement input for varying Mach number and mean angle of attack can be approximately fitted by using the Long Short Term Memory (LSTM) network, which is a time-series prediction approach of deep learning method. Finally, the transonic flutter boundaries of a NACA 64A010 airfoil are investigated to assess the validity of the proposed approach. The comparison with CFD results shows that, the ROM can predict the unsteady aerodynamic response and aeroelastic characteristics well with low computation cost. In particular, the flutter boundaries of the concerned airfoil at different Mach numbers and mean angles of attack are obtained, due to the absence of time-delay term in surrogate model, the generalization capacity and modeling efficiency of the ROM are improved.