Abstract
A reduced order modeling method based on algorithm fusion and multifidelity framework for nonlinear unsteady aerodynamics is proposed to obtain a low-cost and high-precision unsteady aerodynamic model. This method integrates the traditional algorithm, intelligent algorithm, and multifidelity data fusion algorithm. In this method, the traditional algorithm is based on separated flow theory, the intelligent algorithm refers to the nonlinear autoregressive (NARX) method, and the multifidelity data fusion algorithm uses different fidelity data for aerodynamic modeling, which can shorten the time cost of data acquisition. In the process of modeling, firstly, a multifidelity model with NARX description provides a general intelligent algorithm framework for unsteady aerodynamics. Then, based on the separated flow theory, the correction equation from low-fidelity model to high-fidelity result is constructed, and the cuckoo algorithm based on chaos optimization is used to identify the parameters. In order to verify the effectiveness of the method, an unsteady aerodynamic model of NACA0012 airfoil is established. Three kinds of data with low, medium, and high fidelity are used for modeling. The low-fidelity and medium-fidelity data is obtained from the CFD-Euler solver and CFD-RANS solver, respectively, while the high-fidelity data comes from the experimental results. Then, the model is established, and its prediction of unsteady aerodynamic coefficients is in good agreement with the CFD results and the experimental data. After that, the model is applied to a two-dimensional aeroelastic system, and the bifurcation and limit cycle response analysis are compared with the experimental results, which further shows that the model can accurately capture the main flow characteristics in the flow range of low speed and high angle of attack. In addition, the convergence of the model is studied; the accuracy and generalization ability as well as applicability scope of the model are compared with other aerodynamic models and finally discussed.
Highlights
Most of the problems in aircraft aerodynamic design are closely related to unsteady aerodynamic forces
This paper proposes a reduced order model (ROM) for unsteady aerodynamics, which combines the intelligent algorithm with a traditional mathematical model and can consider the aerodynamic nonlinearity at a high angle of attack
A multifidelity modeling framework for low-speed nonlinear unsteady aerodynamic ROM is presented in this paper, which combines traditional and intelligent aerodynamic modeling methods
Summary
Most of the problems in aircraft aerodynamic design are closely related to unsteady aerodynamic forces. The model based on wind tunnel and flight test is difficult to reflect the aerodynamic characteristics of all the flight states or motion forms we are interested in, and the high-fidelity CFD calculation may take a lot of time to obtain enough data, so it is difficult to directly use these aerodynamic models in flight simulation. Both of the two methods have their own shortcomings, which makes them difficult to be applied to engineering practice To solve this problem, this paper proposes a reduced order model (ROM) for unsteady aerodynamics, which combines the intelligent algorithm with a traditional mathematical model and can consider the aerodynamic nonlinearity at a high angle of attack. The multifidelity framework for nonlinear unsteady aerodynamic ROM based on traditional and intelligent algorithm fusion is introduced It can be divided into the following parts. Ã zðx1Þ y2LFðx2Þ, ð6Þ where the multiplicative corrector ρðx1Þ and the additive corrector zðx1Þ are both time-varying functions, which can be identified from high-fidelity data
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