Abstract
In the present work, a reduced-order modeling (ROM) framework based on a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is applied for the computation of transonic aileron buzz. The training data set for the specified ROM is obtained by performing forced-motion unsteady Reynolds-averaged Navier Stokes (URANS) simulations. Further, a Monte Carlo-based training procedure is applied in order to estimate statistical errors. In order to demonstrate the method’s fidelity, a two-dimensional aeroelastic model based on the NACA651213 airfoil is investigated at different flow conditions, while the aileron deflection and the hinge moment are considered in particular. The aileron is integrated in the wing section without a gap and is modeled as rigid. The dynamic equations of the rigid aileron rotation are coupled with the URANS-based flow model. For ROM training purposes, the aileron is excited via a forced motion and the respective aerodynamic and aeroelastic response is computed using a computational fluid dynamics (CFD) solver. A comparison with the high-fidelity reference CFD solutions shows that the essential characteristics of the nonlinear buzz phenomenon are captured by the selected ROM method.
Highlights
Unsteady aerodynamic and aeroelastic phenomena, such as flutter, buffet and buzz, determine the boundaries of the flight envelope of an aircraft
A reduced-order modeling (ROM) approach based on a recurrent neuro-fuzzy model (NFM) that is serially connected with a multilayer perceptron (MLP) neural network [12] is applied for the analysis of non-classical aileron buzz
Compared to the test case included in the training range, a larger disagreement between the computational fluid dynamics (CFD) and ROM result is visible for the initial deflection periods
Summary
Unsteady aerodynamic and aeroelastic phenomena, such as flutter, buffet and buzz, determine the boundaries of the flight envelope of an aircraft. For efficient development of a ROM, computational fluid dynamics (CFD)-based training data representing the input/output relationship of the underlying system are exploited by the application of a linear or a nonlinear system identification approach. ROMs based on fuzzy logic [21,22] yield accurate and reliable results for capturing weak aerodynamic nonlinearities as well as small perturbation flow characteristics. A ROM approach based on a recurrent neuro-fuzzy model (NFM) that is serially connected with a multilayer perceptron (MLP) neural network [12] is applied for the analysis of non-classical aileron buzz. Prior studies by Winter and Breitsamter [5] yield accurate results of the connected ROM approach for modeling nonlinear flow-induced characteristics in the transonic flight regime. A high-fidelity model of the aeroelastic system is defined by coupling the CFD aerodynamic model with the dynamics of the rigid aileron
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