Scholarly
    Communications Project


Document Type:Dissertation
Name:Vladimir Olegovich Balabanov
Email address:balabanv@apollo.aoe.vt.edu
URN:1997/00232
Title:Development of Approximations for HSCT Wing Bending Material Weight using Response Surface Methodology
Degree:Doctor of Philosophy
Department:AOE
Committee Chair: Raphael T. Haftka
Chair's email:haftka@ufl.edu
Committee Members:Bernard Grossman, co-chair
Layne T. Watson
William H. Mason
Owen Hughes
Rakesh K. Kapania
Keywords:Structural optimization, Optimization, Finite element analysis, Finite element optimization, Design, Response Surface, Design of experiments, Multidisciplinary optimization, Parallel computing
Date of defense:June 9, 1997
Availability:Release the entire work immediately worldwide.

Abstract:

A procedure for generating a customized weight function for wing bending material weight of a High Speed Civil Transport (HSCT) is described. The weight function is based on HSCT configuration parameters. A response surface methodology is used to fit a quadratic polynomial to data gathered from a large number of structural optimizations. To reduce the time of performing a large number of structural optimizations, coarse-grained parallelization with a master-slave processor assignment on an Intel Paragon computer is used. The results of the structural optimization are noisy. Noise reduction in the structural optimization results is discussed. It is shown that the response surface filters out this noise. A statistical design of experiments technique is used to minimize the number of required structural optimizations and to maintain accuracy. Simple analysis techniques are used to find regions of the design space where reasonable HSCT designs could occur, thus customizing the weight function to the design requirements of the HSCT, while the response surface itself is created employing detailed analysis methods. Analysis of variance is used to reduce the number of polynomial terms in the response surface model function. Linear and constant corrections based on a small number of high fidelity results are employed to improve the accuracy of the response surface model. Configuration optimization of the HSCT employing a customized weight function is compared to the configuration optimization of the HSCT with a general weight function.

List of Attached Files

Disser.pdf


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