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Velocity field reconstruction in the mixing region of swirl sprays using general regression neural network

Ghorbanian, K ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1115/1.1852472
  3. Publisher: 2005
  4. Abstract:
  5. A general regression neural network technique is proposed for design optimization of pressure-swirl injectors. Phase doppler anemometry measurements for velocity distributions are used to train the neural network. An overall optimized value for the width of the probability is determined. The velocity field in the extrapolation regime is reconstructed with an accuracy of 93%. Excellent agreement between the predicted values and the measurements is obtained. The results indicate that the capability of performing designand optimization studies for pressure-swirl injectors with sufficient accuracy exists by applying modest amount of data in conjunction with an overall optimized value for the width of the probability. Copyright © 2005 by ASME
  6. Keywords:
  7. Algorithms ; Anemometers ; Charge coupled devices ; Extrapolation ; Mathematical models ; Optimization ; Probability ; Radial basis function networks ; Regression analysis ; Spraying ; Structural design ; Swirling flow ; Velocity measurement ; General regression neural network (GRNN) ; Phase doppler anemometer (PDA) ; Swirl sprays ; Velocity field reconstruction ; Computational fluid dynamics
  8. Source: Journal of Fluids Engineering, Transactions of the ASME ; Volume 127, Issue 1 , 2005 , Pages 14-23 ; 00982202 (ISSN)
  9. URL: https://asmedigitalcollection.asme.org/fluidsengineering/article-abstract/127/1/14/455334/Velocity-Field-Reconstruction-in-the-Mixing-Region?redirectedFrom=fulltext