利用试验设计法建立翼型气动特性的人工神经网络模型

建立了翼型气动特性预测的BP(Back Propagation)神经网络模型,重点研究了3种选取训练样本的试验设计(DOE)法:完全析因法、正交设计法和均匀设计法,对BP神经网络预测精度的影响,利用所建立的BP神经网络对FX63-137翼型几何型线进行了优化设计。研究结果表明:在因素数和水平数较少时,完全析因法、正交设计法及均匀设计法的平均测试误差分别为0.002%、0.029%、0.023%;在因素和水平数较多时,完全析因法的样本规模太大而不再适合,正交设计法和均匀设计法的平均测试误差分别为0.42%和0.15%,均匀设计法的预测精度更高,更适合于翼型气动特性预测的人工神经网络模型。优化后翼型的升阻比在迎角为0°~18°范围内均高于原始翼型,在迎角为1°、4°和15°时升阻比分别提高了4.38%、1.38%和5.51%。该研究方法及成果可以应用于翼型的多参数优化设计。

Artificial Neural Network Model of Airfoil Aerodynamic Performance Using Design of Experiments

Back propagation (BP) neural networks are established to predict the aerodynamic performance of an airfoil. Three typical types of design of experiments (DOE) methods, i.e., the factorial, orthogonal and uniform DOE, are employed to sample the training airfoils. The emphasis is laid upon the effect of the three DOE methods on the prediction accuracy of BP neural networks. The established BP neutral networks are finally applied to the optimization design of airfoil FX 63-167.The results show that, the factorial, orthogonal and uniform DOE have test-sample-averaged errors of 0.002%, 0.029% and 0.023% respectively in the case of small numbers of factors and levels. In the case of large numbers of factors and levels, the factorial DOE is unaccep-table due to excessive training points, while the orthogonal and uniform DOE have errors of 0.42% and 0.15%, respectively, indicating that the uniform DOE is the best method among the three, which can be coupled with the BP neural network for the prediction of the airfoil aerodynamic performance. The optimized airfoil shows a higher lift-drag ratio than the original one within the wide range of angles of attack from 0° to 18°. The lift-drag ratio increases by 4.38%, 1.38% and 5.51% respectively at angles of attack of 1°, 4° and 15°. The proposed method and conclusion can be extended to the multi-parameter optimization design of the turbo-machinery.