Efficient multi-fidelity reduced-order modeling for nonlinear flutter prediction

Flutter prediction is an important part of aircraft design. However, high-fidelity predictions for transonic flutter are difficult to make because of the associated computational costs. This paper proposes a multi-fidelity reduced-order modeling (MFROM) framework for flutter predictions to achieve high-fidelity simulations with limited computational costs. The high-fidelity data were obtained from a Navier–Stokes-equation-based solver, while the low-fidelity data were taken from an Euler-equation-based flow solver. By employing a multi-fidelity neural network trained with two types of data, this methodology enables nonlinear predictions for transonic results. To demonstrate the multi-fidelity process, a widely used pitching and plunging airfoil case is considered. Verification of the approach was performed by comparing with results from time-domain aeroelastic solvers. The results showed that the proposed multi-fidelity neural network modeling framework could realize online predictions of unsteady aerodynamic forces and flutter responses across multiple Mach numbers. Compared with the typical multi-fidelity method, the proposed neural network has a higher accuracy and a stronger generalization capability. Finally, the potential of the method to reduce the computational effort of high-fidelity aeroelastic analysis was demonstrated. •The effectiveness of multi-fidelity aerodynamic model across multiple Mach numbers is verified.•Simulation results using multi-fidelity neural networks have stronger nonlinear prediction capability.•Online correction of aerodynamic models can reduce the requirements for offline data.•Multi-fidelity modeling methods can improve the efficiency of nonlinear flutter predictions by more than 3 times.