Artificial Neural Network Modeling of Unsteady Aerodynamic Characteristics of Aircraft at High Attack Angle
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摘要: 大攻角气动特性预测与气动建模是新型飞行器提升飞行性能的重要内容.以轴对称导弹简化模型为研究对象，首先采用计算流体力学方法，对70°大攻角状态的非定常气动特性进行数值模拟，计算方法基于RANS的N-S方程，湍流模型采用SA模型，对流场采用有限体积法离散，无黏项采用Roe通量差分分裂格式，黏性项采用中心差分，时间推进采用LU-SGS格式的双时间步法.飞行器运动模式采用强迫振荡的方式，对5种不同振荡频率进行了非定常数值计算，并记录每一内迭代周期最终的气动力和力矩数值.其次，以CFD预测结果作为气动建模的样本，采用动导数模型、多项式模型等传统方法，进行气动建模，并分析其有效性和精度.最后采用神经网络方法对大攻角非定常气动力进行建模，并和动导数模型、多项式模型进行精度对比.结果表明，基于神经网络的人工智能气动建模方法具有较高的精度和适应性.该方法为飞行器大攻角非定常非线性气动建模，大攻角飞行稳定性分析与控制提供理论参考.Abstract: The simulation and modeling of aerodynamic characteristics at high angle of attack is one of the most important research areas for new concept aircraft. Based on simplified missile model, firstly, aerodynamic characteristics of 70å ttack angle were numerically simulated by RANS-based CFD method with SA turbulence model. The finite volume method was used to discretize the N-S formulation. The LU-SGS dual time-stepping algorithm was used for time marching. The unsteady calculations with five different oscillation frequencies were carried out in the mode of forced oscillation, and the final aerodynamic data in each iteration period were recorded. Secondly, based on CFD results, traditional methods such as dynamic derivative model and polynomial model were used for aerodynamic modeling, and the validity and accuracy were analyzed. Finally, dynamic derivative model, polynomial model and neural network mode were used to modeling aerodynamic characteristics. The results show that the artificial intelligence aerodynamic modeling method based on neural network has higher accuracy and adaptability. This method provides theoretical technical support for the unsteady nonlinear aerodynamic modeling, and the stability analysis and control of aircrafts at high angle of attack.
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图 1 神经网络气动建模算法流程
Figure 1. Flowchart of the neural network aerodynamic modeling algorithm
表 1 动导数建模结果
Table 1. Aerodynamic derivative modeling results
samples Cmz0 Cmzα Cmzωz 1 Hz -0.993 5 -0.001 0 -1.935 6 2 Hz -0.995 0 -0.000 9 -1.937 4 3 Hz -1.002 9 -0.001 0 -1.953 2 5 Hz -0.992 9 -0.000 8 -2.001 5 10 Hz -0.993 5 -0.001 0 -1.935 6 
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表 2 3阶多项式建模结果
Table 2. 3rd order polynomial modeling results
samples Cmz0 Cmzα Cmzωz 1 Hz -0.926 7 -0.000 5 -1.951 6 2 Hz -0.926 0 -0.000 7 -1.953 7 3 Hz -0.930 7 -0.001 0 -1.955 5 5 Hz -0.961 9 -0.000 8 -2.004 5 10 Hz -0.858 3 -0.000 6 -2.066 8
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表 3 非定常气动建模精度对比
Table 3. Comparison of unsteady aerodynamic modeling accuracy
samples dynamic derivatives model/% 3rd polynomial model/% neural networks model/% 1 Hz 9.67 1.3 0.47 2 Hz 10.02 1.17 0.49 3 Hz 12.09 1.57 0.42 5 Hz 12.44 1.04 0.52 10 Hz 9.67 1.04 0.46 all data 10.1 1.6 0.5
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