Design Methods and Strategies for Forward and Inverse Problems of Turbine Blades Based on Machine Learning

To study the feasibility of using machine learning technology to solve the forward problem (prediction of aerodynamic parameters) and the inverse problem (prediction of geometric parameters) of turbine blades, this paper built a forward problem model based on backpropagation artificial neural networks (BP-ANNs) and an inverse problem model based on radial basis function artificial neural networks (RBF-ANNs). The S2 (a stream surface obtained by extending a radial curve in turbo blades) calculation program was used to generate the dataset for single-stage turbo blades, and the back propagation algorithm was used to train the model. The parameters of five blade sections in a single-stage turbine were selected as inputs of the forward problem model, including stagger angle, inlet geometric angle, outlet geometric angle, wedge angle of leading edge pressure side, wedge angle of leading edge suction side, wedge angle of trailing edge, rear bending angle, and leading edge diameter. The outputs are efficiency, power, mass flow, relative exit Mach number, absolute exit Mach number, relative exit flow angle, absolute exit flow angle and reaction degree, which are eight aerodynamic parameters. The inputs and outputs of the inverse problem model are the opposite of that of the forward problem model. The models can accurately predict the aerodynamic parameters and geometric parameters, and the mean square errors (MSEs) of the forward problem test set and the inverse problem test set are 0.001 and 0.000 35, respectively. This study shows that machine learning technology based on neural networks can be flexibly applied to the design of forward and inverse problems of turbine blades, and the models built by this method have practical application value in regression prediction problems.