A fast prediction model of blade flutter in turbomachinery based on graph convolutional neural network

Abstract

With the development of gas turbine towards higher load capacity and improved efficiency, blade flutter has emerged as a significant obstacle in the further development of turbomachinery due to fluid-structure coupling phenomenon. However, the traditional numerical simulation methods lengthen the design cycle and reduce the optimization efficiency due to the expensive time and resource cost. To solve this problem, a fast prediction model of blade flutter based on the graph convolutional neural network is proposed. The blade flutter prediction graph convolutional neural network containing two sub-networks. The flow field prediction network is used to realize prediction from design variables to flow field parameters. The flutter parameter identification network is adopted to realize identification from flow field parameters to 15 flutter parameters. The prediction performance for physical fields, modal forces, aerodynamic damping and aeroelastic stability are discussed. The influence of training size is explored. The results show that the network can not only accurately capture the trends and details of the physical field, but also obtain the accurate modal force curve, so as to calculate the aerodynamic damping parameters and finally judge the aeroelastic stability.