Prediction Method of Unsteady Flow Load of Compressor Stator under Working Condition Disturbance

Abstract

Due to the complexity of the compressor operating conditions and the existence of various disturbances and unsteady effects in the flow field, the analysis of compressor stator vibration characteristics becomes particularly critical. The convolutional neural network model combined with a transient CFD method was introduced to solve the difficulty of analyzing the flow load of the compressor stator blade. This paper mainly focuses on two key points: the complex change of the aerodynamic load and the accurate prediction of the blade excitation. Considering the stator–rotor interference, the unsteady effects, and the variable working condition characteristics, the random disturbance analysis model of the flow field boundary was generated to simulate the unsteady flow excitation of the stator under complex working conditions. By establishing the neural network of boundary disturbance and flow excitation characteristics, the prediction model was trained and generated under the support of large-scale data. The most important role of the model was to establish the end-to-end data mapping between the disturbance condition and the aerodynamic load of the stator blade. The conclusions demonstrate that the introduction of an airflow disturbance is helpful to obtain the excitation characteristics of the stator under complex working conditions. The model established in this paper based on 1000 groups of disturbed working condition data can effectively predict the aerodynamic load of the blades under complex working conditions. In addition, the construction of the model is beneficial for saving a lot of computing resources, and the prediction accuracy also reaches a good level. The method presented in this paper provides a reference for the vibration analysis of the compressor stator.