Prediction of thermo-mechanical performance for effusion cooling by machine learning method

Fast and accurate evaluation of performance of cooling structures is very critical to the design of hot-section components in gas turbines. In the present study, machine learning method was developed to predict thermo-mechanical performance for effusion cooling on flat plate. The geometric and thermodynamic parameters including blowing ratio, density ratio, hole inclination angle, hole pitch and spacing were studied. Latin hypercube sampling was used for the design of numerical experiments, and the data sets for training model were generated by computational fluid dynamics (CFD) solutions. A kind of deep neural networks (DNNs) was used to predict the distributions of solid temperature and stress. Based on the outputs of neural network, the effects of the geometric and thermodynamic parameters on thermo-mechanical performance were investigated in detail. The results show that thermal stress decreases with the increases of density ratio and blowing ratio, while the increases of hole spacing and pitch cause the increase of thermal stress. Compared with the CFD results, the proposed DNN method has mean absolute percent error of 0.5% and 0.08% and root mean square error of 6.95 K and 5.1 MPa as applying for predicting solid temperature and stress. On the whole, machine learning is a promising method for modelling effusion cooling with high accuracy.