The stall flutter characters of high-aspect-ratio composite wing are investigated, and the effects of structure geometric nonlinearity and stiffness couple created by composite anisotropy on them also are discussed. F...The stall flutter characters of high-aspect-ratio composite wing are investigated, and the effects of structure geometric nonlinearity and stiffness couple created by composite anisotropy on them also are discussed. Firstly, the high-aspect-ratio wing is modeled as a composite thin-walled closed section Euler beam whose displacement and rotation both could be of finite value, and the nonlinear dynamic equations is build up on it with all the effects of geometric nonlinearity, aerodynamic nonlinearity and anisotropy of material being considered. Then vibration equations are deduced through perturbing the dynamic equations at wing＇s equilibrium position, and coupled with unsteady stall aerodynamic model and ONERA model, to obtain the nonlinear stall flutter analysis equations of wing. Finally, the flutter stabilities with various wind speeds are determined by the harmonic balance method. With several exampies, the validity of the stall flutter model is proved, and the significant effects of geometric nonlinearity on the stall flutter various characters as wall as the effects of ply angle on the stall flutter speed and frequency also are discussed.展开更多
The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model(ROM) framework f...The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model(ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit(GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation(LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.展开更多
文摘The stall flutter characters of high-aspect-ratio composite wing are investigated, and the effects of structure geometric nonlinearity and stiffness couple created by composite anisotropy on them also are discussed. Firstly, the high-aspect-ratio wing is modeled as a composite thin-walled closed section Euler beam whose displacement and rotation both could be of finite value, and the nonlinear dynamic equations is build up on it with all the effects of geometric nonlinearity, aerodynamic nonlinearity and anisotropy of material being considered. Then vibration equations are deduced through perturbing the dynamic equations at wing＇s equilibrium position, and coupled with unsteady stall aerodynamic model and ONERA model, to obtain the nonlinear stall flutter analysis equations of wing. Finally, the flutter stabilities with various wind speeds are determined by the harmonic balance method. With several exampies, the validity of the stall flutter model is proved, and the significant effects of geometric nonlinearity on the stall flutter various characters as wall as the effects of ply angle on the stall flutter speed and frequency also are discussed.
基金supported by the National Natural Science Foundation of China(No.11672018).
文摘The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model(ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit(GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation(LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.
