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Axial compressor performance map prediction using artificial neural network

Ghorbanian, K ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1115/GT2007-27165
  3. Publisher: 2007
  4. Abstract:
  5. The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural network such as multilayer perceptron network, radial basis function network, general regression neural network, and a rotated general regression neural network proposed by the authors are considered. Two different models are utilized in simulating the performance map. The results indicate that while the rotated general regression neural network has the least mean error and best agreement to the experimental data, it is however limited to curve fitting application. On the other hand, if one considers a tool for curve fitting as well as for interpolation and extrapolation applications, multilayer perceptron network technique is the most powerful candidate. Further, the compressor efficiency based on the multilayer perceptron network technique is determined. Excellent agreement between the predictions and the experimental data is obtained. Copyright © 2007 by ASME
  6. Keywords:
  7. Axial compressors ; Regression neural networks ; Computer simulation ; Multilayer neural networks ; Radial basis function networks ; Regression analysis ; Compressors
  8. Source: 2007 ASME Turbo Expo, Montreal, Que., 14 May 2007 through 17 May 2007 ; Volume 6 PART B , 2007 , Pages 1199-1208 ; 079184790X (ISBN); 9780791847909 (ISBN)
  9. URL: https://asmedigitalcollection.asme.org/GT/proceedings-abstract/GT2007/47950/1199/363744