HSCT wing design through multilevel decomposition

Roehl, Peter; Mavris, Dimitri N.; Schrage, Daniel P.

doi:10.2514/6.1995-3944

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…The response surface methodology (RSM) approach is not the only way of integrating structural optimization in the overall HSCT design process. For example, a multilevel decomposition approach was successfully used by R ohl et al 14 and an interlacing factor approach was used by a design group at Virginia Tech 9 . A broad survey of methods is presented in Ref.…”

Section: Response Surface Approachmentioning

confidence: 99%

Dependence of optimal structural weight on aerodynamic shape for a High Speed Civil Transport

Balabanov¹,

Kaufman²,

Knill³

et al. 1996

6th Symposium on Multidisciplinary Analysis and Optimization

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A procedure for generating a customized weight function for wing bending material weight of the High Speed Civil Transport (HSCT) is described. The weight function is based on the shape parameters. A response surface methodology is used to t a quadratic polynomial to data gathered from a large number of structural optimizations. The results of the structural optimization are noisy. Noise reduction in the structural optimization results is discussed. Several techniques are used to minimize the number of required structural optimizations and to maintain accuracy. Simple analysis techniques are used to nd 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 surfaces themselves are created employing detailed analysis methods. Intervening variables and analysis of variance are used to reduce the number of polynomial terms in the response surface model functions. Minimum variance and minimum bias procedures for creation of response surfaces are compared. Con guration optimization of the HSCT employing customized weight functions with di erent response surfaces are compared.

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Section: Response Surface Approachmentioning

confidence: 99%