Abstract: Film cooling is an effective way to enhance the high temperature resistance of turbine blades and indirectly increase the inlet temperature of the turbine. Currently, the mainstream design method of the film cooling hole layout is to select and optimize the initial schemes with Computational Fluid Dynamics (CFD), followed by model experiments. However, this method has a long design period and thus is time-consuming. The traditional empirical formula method for rapid evaluation of cooling efficiency is problematic in that the function form is complex, the fitting precision is low and the applicable range of parameters is narrow. This paper designs a deep neural network with a Multi-Layer Perceptron model (MLP) based on the principle of deep learning, and establishes a prediction model for adiabatic film cooling efficiency. The calculation of CFD is utilized to train the network, and the results obtained from the modeling efforts indicate that the deep learning model has a fitting degree of more than 0.95 on the training set and the verification set, and more than 0.99 on the test set. Hence, it can effectively identify the abstract features of the data set with high precision and good generalization ability. Additionally, on the premise of meeting the requirement of sufficient precision, the deep learning model takes only 1/1000 of the time of CFD to complete the prediction process, exhibiting a good application prospect in the rapid evaluation of cooling layout performance.

Key words: film cooling, adiabatic film cooling efficiency, Computational Fluid Dynamics (CFD), neural networks, deep learning, Multi-Layer Perceptron (MLP), prediction