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A Subsonic Aircraft Design Optimization With Neural Network And Regression Approximators
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Author :National Aeronautics and Space Administration (NASA) Publisher :Createspace Independent Publishing Platform ISBN 13 :9781721668342 Total Pages :26 pages Book Rating :4.6/5 (683 download)
Book Synopsis A Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators by : National Aeronautics and Space Administration (NASA)
Download or read book A Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-06-21 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated. Patnaik, Surya N. and Coroneos, Rula M. and Guptill, James D. and Hopkins, Dale A. and Haller, William J. Glenn Research Center NASA/TM-2004-213059, AIAA Paper 2004-4606, E-14504
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