Artificial intelligence aided design of film cooling scheme on turbine guide vane

In recent years, artificial intelligence (AI) technologies have been widely applied in many different fields including in the design, maintenance, and control of aero-engines. The air-cooled turbine vane is one of the most complex components in aero-engine design. Therefore, it is interesting to adopt the existing AI technologies in the design of the cooling passages. Given that the application of AI relies on a large amount of data, the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning. It includes the parameterized three-dimensional (3-D) geometrical modeling, automatic meshing and computational fluid dynamics (CFD) batch automatic simulation of different film cooling structures through customized developments of UG, ICEM and Fluent. It is demonstrated that the trained artificial neural network (ANN) can predict the cooling effectiveness on the external surface of the turbine vane. The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model. Using this established method, a multi-output model is constructed based on which a simple tool can be developed. It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.