Application of Neural Network Model in Calculation of Axial Compressor Direct Problems
In order to solve the problem of poor performance prediction and difficulty in updating tradi-tional deviation angle and total pressure loss coefficient empirical models in the direct problem analysis process of axial compressors in different blade shapes and wide operating conditions,based on the accu-mulated blade shape simulation calculation results and flow field parameters measured in cascade and component level experiments,a back propagation(BP)neural network model has been used instead of the commonly used empirical model.A new compressor direct problem analysis program is obtained on the basis of improving original calculation program,and the direct problem of axial compressors is solved and analyzed by this program.As for improving the prediction accuracy of the neural network model,the BP neural network is trained from the basic parameters to the deviation angle and the total pressure loss coefficient by using the flow field parameters in a certain range as data sets,which controls the relative error of predictions between 6％and 2％.Computational verification is performed for two axial compres-sor examples.Comparing the results of the new analysis program with the experimental results and the previous calculation results,it is found that the prediction results of the Stage 37 blade outlet parameters by this method are consistent with the experimental results,and the prediction error of the overall per-formance of the high speed 4-stage axial compressor is less than 7％.
axial flow compressorpositive problem analysisneural networkflow loss predictingstre-amline curvature method
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