Considering the poor accuracy computation of the compressor lower speed characteristic in aero-engine startup modeling

a method of compressor characteristic extension combining exponent extrapolation method with support vector machine (SVM) was proposed. Firstly

the mechanism and limitation of exponent extrapolation method were analyzed. Then the known data of compressor higher speed characteristics were used as SVM training set

and the data of lower speed characteristic obtained by exponent extrapolation method were used as the test set. Meanwhile

in order to reduce the nonlinearity of the original data in SVM

the compressor characteristic was converted into the characteristic represented by outlet flow parameters. The lower speed characteristics were predicted and obtained by using a kind of cross validation algorithm to select the parameters of SVM and train the SVM model. The comparative analysis with the characteristics obtained by using only exponent extrapolation method shows that using the method of compressor characteristic extension combining with exponent extrapolation method with SVM

the maximum relative error is reduced by about 2.8%

and the characteristic extension precision is effectively improved.