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Application of artificial neural networks to the design optimization of aerospace structural componentsThe application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.
Document ID
19930012642
Document Type
Technical Memorandum (TM)
Authors
Berke, Laszlo (NASA Lewis Research Center Cleveland, OH, United States)
Patnaik, Surya N. (Ohio Aerospace Inst. Brook Park., United States)
Murthy, Pappu L. N. (NASA Lewis Research Center Cleveland, OH, United States)
Date Acquired
September 6, 2013
Publication Date
March 1, 1993
Subject Category
STRUCTURAL MECHANICS
Report/Patent Number
NAS 1.15:4389
NASA-TM-4389
E-6994-1
Funding Number(s)
PROJECT: RTOP 505-63-5B
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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