基于约束神经网络的气膜冷效分布预测方法

To improve the design efficiency of film cooling in turbine vane， a neural network constrained by Gaussian function was proposed to predict the cooling effectiveness distribution of single film row，and the distribution of multi film rows was predicted by combining the modified superposition principle. For a single film row on the flat-plate，three coefficients of the Gaussian function were predicted with the main flow turbulence degree，density ratio，blowing ratio，film inclined angle，length-width ratio and dimensionless flow direction distance taken as inputs，and then the dimensionless lateral distance was substituted into the predicted Gaussian function to calculate the cooling effectiveness. The prediction error of the Gauss function constrained neural network for the average cooling effectiveness of the test samples was only 5.70%，which was 67% lower than that of network without constraints. Based on the model of single film row and the correction coefficient of multi film rows superposition principle predicted according to the blowing ratio and dimensionless coordinates， the cooling effectiveness distribution of multi film rows was calculated. The prediction errors of the average cooling effectiveness were only 6.19% and 12.19% with the blowing ratio at 0.5 and 1， respectively. According to the results， the proposed method can predict the film cooling effectiveness distribution accurately.