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.

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基于算法融合和多保真框架的翼型非线性非定常气动降阶模型

提出了一种基于算法融合和多保真框架的非线性非定常空气动力学降阶建模方法，以获得低成本、高精度的非定常空气动力学模型。该方法集成了传统算法、智能算法和多保真数据融合算法。该方法中，传统算法基于分离流理论，智能算法参考非线性自回归(NARX)方法，多保真数据融合算法使用不同保真度数据进行气动建模，可以缩短数据采集的时间成本. 在建模过程中，首先，具有NARX描述的多保真模型为非定常空气动力学提供了通用的智能算法框架。然后，基于分离流理论，构建低保真模型到高保真结果的修正方程，并采用基于混沌优化的布谷鸟算法进行参数辨识。为了验证该方法的有效性，建立了NACA0012翼型的非定常气动模型。使用低、中、高保真三种数据进行建模。低保真和中保真数据分别来自CFD-Euler求解器和CFD-RANS求解器，而高保真数据来自实验结果。然后，建立模型，其对非定常气动系数的预测与CFD结果和实验数据吻合较好。之后，将该模型应用于二维气弹系统，并将分岔和极限环响应分析与实验结果进行对比，进一步表明该模型能够准确捕捉低速大攻角流动范围内的主要流动特性。此外，研究了模型的收敛性；将模型的准确性和泛化能力以及适用范围与其他气动模型进行了比较，最后进行了讨论。