Signal Interpolation Augmented Linear Nonintrusive Reduced-Order Model for Aeroelastic Applications

This study aims to develop a linear nonintrusive reduced-order model (NIROM) for facilitating combined fast parametric analysis and reconstruction of unsteady aerodynamic flow variables by using a novel signal interpolation approach, which is combined with a subspace-identification-based method. The NIROM is then applied to airfoil flutter and gust interaction problems where the assumption of linearized dynamics gives reasonably accurate estimates. In the traditional subspace-identification method, the training datasets of force coefficient signals corresponding to each flow parameter (like the Mach number and angle of attack) are required to generate the linear time-invariant system. The generation of the training signal for each flow parameter is the most computationally expensive process in any parametric reduced-order model (ROM). In the present work, the discrete empirical interpolation method (DEIM) combined with the radial basis function (RBF) method is used for the interpolation of training signals at a specific flow parameter to reduce the overall computational cost. The conventional nonintrusive approaches are generally constrained by the number of output channels, and hence not suitable for the reconstruction of unsteady surface force distribution or flowfield variables. A few suitable sensor locations using the DEIM are selected as output channels of the subspace-identification method to realize the full datasets of surface force distribution over the airfoil. Finally, a suitable algorithmic structure is proposed to achieve unsteady distributive flow-variable reconstruction and parametric variation simultaneously. The current signal interpolation-based NIROM is applied to a wing section with two- and four-degree-of-freedom structural motions.