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Reducing wind tunnel data for flowfield study over the wing-canard configuration using neural network

Hoseini, A. A ; Sharif University of Technology | 2004

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  1. Type of Document: Article
  2. Publisher: 2004
  3. Abstract:
  4. The objective of this paper is study of flowfield over the wing of a coplanar and close-coupled wing-canard configuration model using neural network, in order to reduce measurement points and experiment time. The results are based on flowfield pressure and wing surface pressure measurements at difference angles of attack, no sideslip and difference angles of canard. A GRNN (General Regression Neural Network) algorithm is developed to determine the shape and trajectory of vortices over this model. This network uses the total pressure coefficients of flowfield over the wing of this model that are obtained by total pressure probe as its input. The data presented to the network are processed to predict the pressure of flowfield in unknown points over the wing. The approach immediately reduced the cost of testing with acceptable accuracy
  5. Keywords:
  6. Flowfield ; GRNN ; Neural Network ; Vortex ; Wing-Canard Configuration
  7. Source: 42nd AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, 5 January 2004 through 8 January 2004 ; 2004 , Pages 8371-8377
  8. URL: https://arc.aiaa.org/doi/abs/10.2514/6.2004-727