基于机器学习的单孔绝热气膜冷却效率分布预测研究

In order to study the method and characteristics of applying machine learning to predict the adiabatic film cooling efficiency of single film cooling hole, a machine learning model based on up-sampling convolutional neural network (CNN) was built. The numerical simulation data used for training and verification was generated. The model was trained by supervised learning method. The training uses back propagation algorithm and Adam optimizer based on stochastic gradient descent. The blowing ratio, mainstream turbulence intensity, injection angle, hole shape, and size of the hole were set as inputs for the model. The contour of adiabatic film cooling efficiency was considered as output for the model. The prediction results of the model show that the model based on the up-sampling convolutional neural network performs well on the prediction problem (absolute error of pixels is about 0.05 in test set). Besides, training suggestions for networks of this kind were given. The study show that for contour regression objective, it is flexible for CNN to generate reliable predictions, hence this method has better engineering application value.