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Modeling of gas turbine combustor using dynamic neural network

Lahroodi, M ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1115/IMECE2006-15737
  3. Publisher: American Society of Mechanical Engineers (ASME) , 2006
  4. Abstract:
  5. This paper presents an Artificial Neural Network (ANN) - based modeling technique for prediction of outlet temperature, pressure and mass flow rate of gas turbine combustor. ANN technique has been developed and used to model temperature, pressure and mass flow rate as a nonlinear function of fuel flow rate to the combustion chamber. Results obtained by present modeling are compared with those obtained by experiment. A quantitative analysis of modeling technique has been carried out using different evaluation indices; namely, Mean-Square-Quantization-Error (MSQE) and actual percentage error. The results show the effectiveness and capability of the proposed modeling technique with reasonable accuracies of about 95 percent. Copyright © 2006 by ASME
  6. Keywords:
  7. Combustion chambers ; Computer simulation ; Fuel injection ; Mean square error ; Neural networks ; Actual percentage error ; Different evaluation indices ; Fuel flow rate ; Outlet temperature ; Gas turbines
  8. Source: 2006 ASME International Mechanical Engineering Congress and Exposition, IMECE2006, Chicago, IL, 5 November 2006 through 10 November 2006 ; 2006 ; 10716947 (ISSN); 0791837904 (ISBN); 9780791837900 (ISBN)
  9. URL: https://asmedigitalcollection.asme.org/IMECE/proceedings-abstract/IMECE2006/47683/1229/322961