Neural network reconstruction method of compressor characteristics with continued fraction expanded data

Original two-dimensional constant rotating speed curve data points of aero-engine compressor were expanded using continued fraction interpolation.Rotating speed characteristic data through twice network training was increased and BP(back progagation) neural network model reconstruction in three-dimensional space was performed.According to the space distribution of compressor characteristics data,pressure ratio function was introduced,computational domain was adjusted,input and output data were defined and dimension of hidden layer was determined by cut-and-try method.Particle swarm optimizing algorithm based on seek advantage and avoid disadvantage principle was used to optimize the initial weight and threshold of neural network,and to establish the integral surrogate model of compressors was taken pressure ratio and efficiency characteristics.In the end,some aero-engine low pressure compressor for example to conduct the model reconstruction method.The check and verification results indicate the surrogate model established by the method proposed is more accurate than traditional two-dimensional interpolation and common BP neural network model.The reconstruction model can increase computational accuracy and boost iteration speed when used in solving aero-engine mathematic model based on general characteristic curve of compressor such as trial and error procedure,coordinate method and components method,therefore it is of engineering value.