Two-dimensional film-cooling effectiveness prediction based on deconvolution neural network

Abstract

For film cooling in high-pressure turbines, it is vital to predict the temperature distribution and film cooling effectiveness on the blade surface downstream of the cooling hole. This temperature distribution and film cooling effectiveness depend on the interaction between the hot mainstream and the coolant jet. However, it is difficult to correlate accurately due to the complex mechanism. Based on deep learning techniques, a theoretic model using Deconvolutional Neural Network (Deconv NN) was developed to model the non-linear and high-dimensional mapping between coolant jet parameters and the surface temperature distribution on a flat plate. Computational Fluid Dynamics (CFD) was utilized to provide data for the training models. The input of the model includes blowing ratio, density ratio, hole inclination angle and hole diameters etc. With rigorous testing and validation, it is found that the predicted results are in good agreement with results from CFD. It is compared against the existing semi-empirical correlations and other machine learning techniques, such as support vector machine method. Dataset with different size is tested. The results suggest that the performance and robustness of Deconv NN is much better than other methods.