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Information Journal Paper

Title

REAL TIME DATA MONITORING OF WIND TUNNEL CALIBRATION AND ITS EXTENTION, BASED ON GENERAL REGRESSION NEURAL NETWORK

Pages

  11-23

Abstract

 Wind tunnel tests with good flow conditions have great roles in the design and optimization of models of flying vehicles. It is crucial to obtain test section flow parameters, such as velocity and pressure distribution, as well as uniformity along with the direction of the flow in the CALIBRATION tests. This process is very expensive and time consuming due to large Mach number range, especially for relatively large sizes of WIND TUNNEL test section. To overcome these problems, the CALIBRATION tests were done in the specified sections and Mach number and the results were extracted for other sections and Mach numbers by SOFT COMPUTING. With REAL TIME MONITORING of WIND TUNNEL data, based on GRNN and the network training, the related data in other sections and Mach numbers were estimated.It was shown that the GRNN is more capable with respect to classical methods for estimating non-linear functions, such as least square method. Cost, time, and WIND TUNNEL CALIBRATION tests were decreased extensively using this intelligent technique.

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    Cite

    APA: Copy

    HASSANI AHANGAR, M.R., KANGAVARI, M.R., GHADAK, F., & SOLTANI, M.R.. (2010). REAL TIME DATA MONITORING OF WIND TUNNEL CALIBRATION AND ITS EXTENTION, BASED ON GENERAL REGRESSION NEURAL NETWORK. AEROSPACE MECHANICS JOURNAL, 6(3 (21) (DYNAMICS, VIBRATIONS AND CONTROL)), 11-23. SID. https://sid.ir/paper/101811/en

    Vancouver: Copy

    HASSANI AHANGAR M.R., KANGAVARI M.R., GHADAK F., SOLTANI M.R.. REAL TIME DATA MONITORING OF WIND TUNNEL CALIBRATION AND ITS EXTENTION, BASED ON GENERAL REGRESSION NEURAL NETWORK. AEROSPACE MECHANICS JOURNAL[Internet]. 2010;6(3 (21) (DYNAMICS, VIBRATIONS AND CONTROL)):11-23. Available from: https://sid.ir/paper/101811/en

    IEEE: Copy

    M.R. HASSANI AHANGAR, M.R. KANGAVARI, F. GHADAK, and M.R. SOLTANI, “REAL TIME DATA MONITORING OF WIND TUNNEL CALIBRATION AND ITS EXTENTION, BASED ON GENERAL REGRESSION NEURAL NETWORK,” AEROSPACE MECHANICS JOURNAL, vol. 6, no. 3 (21) (DYNAMICS, VIBRATIONS AND CONTROL), pp. 11–23, 2010, [Online]. Available: https://sid.ir/paper/101811/en

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    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
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