HSCT configuration design using response surface approximations of supersonic Euler aerodynamics

Knill, Duane L.; Giunta, Anthony A.; Baker, Chuck A.; Grossman, Bernard; Mason, William H.; Haftka, Raphael T.; Watson, Layne T.

doi:10.2514/6.1998-905

Cited by 14 publications

(15 citation statements)

References 21 publications

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“…In addition to other research conducted with higher fidelity aerodynamic RS models [24], work has been done on structural RS models for a HSCT configuration optimization over a small design box [25]. Work is currently in progress to use the RSM presented to construct structural RS models for the large design box used in this study.…”

Section: Discussionmentioning

confidence: 99%

HSCT configuration design space exploration using aerodynamic response surface approximations

Chuck

Grossman

Raphael

et al. 1998

7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

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A method has been developed to generate and use polynomial approximations to the range and cruise drag components in a highly constrained, multidisciplinary design optimization of a High Speed Civil Transport configuration. The method improves optimization performance by eliminating the numerical noise present in the analyses through the use of response surface methodology. In our implementation, we fit quadratic polynomials within variable bounds to data gathered from a series of numerical analyses of different aircraft designs. Because the HSCT optimization process contains noise and suffers from a nonconvex design space even when noise is filtered out, multiple optimization runs are performed from different starting points with and without the response surface models in order to evaluate their effectiveness. It is shown that response surface methodology facilitates design space exploration, allowing improvements in terms of both convergence performance and computational effort when multiple starting points are required. *å ç

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Section: Discussionmentioning

confidence: 99%

HSCT configuration design space exploration using aerodynamic response surface approximations

Chuck

Grossman

Raphael

et al. 1998

7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

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“…The optimal design variable values and weights are given in Tables 3 and 4. Further details of these cases are presented by Knill et al 33 Because of computational expense, the full quadratic Euler RS models are not created for the 20-variable design. This is exactly the situation for which the reduced-term incremental RS models were developed.…”

Section: Optimization Resultsmentioning

confidence: 98%

Response Surface Models Combining Linear and Euler Aerodynamics for Supersonic Transport Design

Knill

Giunta

Baker

et al. 1999

Journal of Aircraft

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100

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A method has been developed to efficiently implement supersonic aerodynamic predictions from Euler solutions into a highly constrained, muItidisciplinary design optimization of a High-Speed Civil Transport. The method alleviates the large computational burden associated with performing computational fluid dynamics analyses through the use of variable-complexity modeling techniques, response surface (RS) methodologies, and coarse-grained parallel computing. Using information gained from lower-fidelity aerodynamic models, reduced-term RS models representing a correction to the linear theory RS model predictions are constructed using Euler solutions. Studies into 5-, 10-, 15-, and 20-variable design problems show that accurate results can be obtained with the reduced-term models at a fraction of the cost of creating the full-term quadratic RS models. Specifically, a savings of 255 CPU hours out of 392 CPU hours required to create the full-term RS model is obtained for the 20-variable problem on a single 75-MHz IP21 processor of a Silicon Graphics, Inc. Power Challenge. Nomenclaturec jk -response surface model coefficients g(x) = vector of optimization constraint values K = drag polar shape parameter m = number of design variables N = number of points used to evaluate response surface model error n = number of terms in the response surface model n p -number of processors used on a parallel computer Design Center for Advanced Vehicles. p = number of experimental design points q = number of candidate sample sites R LE = leading-edge radius parameter ffus, = fuselage radius at /th axial location 5 LE/ = inboard leading-edge length s TEj = inboard trailing-edge length (//c)break = thickness-to-chord ratio at leading-edge break (tlc\ ool = thickness-to-chord ratio at wing root 0/c) tip = thickness-to-chord ratio at wing tip WC-TOGW = corrected takeoff gross weight Wfue, = fuel weight WTOGW = takeoff gross weight Wwing = wing weight x = ra-dimensional vector of design variable values (*/c)max-r = chordwise location of maximum thickness Xj = jth design variable *max = vector of upper bounds on design variable values *min = vector of lower bounds on design variable values v = observed response value y = predicted response value Vnac = spanwise location of inboard nacelle AC Do = correction to linear theory value of the drag polar shape parameter A^f = correction to linear theory value of the drag polar shape parameter AW^i = correction to fuel weight A;y nac = distance between nacelles A LE/ = inboard leading-edge sweep angle ALE O = outboard leading-edge sweep angle A T E 7 = inboard trailing-edge sweep angle

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“…Second order correction methods that require second derivatives of the high-and low fidelity response were introduced by Eldred et al 92 implemented in the DAKOTA software 63 and compared to other metamodeling techniques. 93 Knill et al 94 stated that the gradient-based low fidelity model correction procedure was effective in reducing the computational cost but it was adversely affected by the presence of numerical noise in aerodynamic and structural response values. To circumvent this, Giunta et al 95 suggested to initially perform a thorough design space exploration utilizing a low fidelity model, and identify and exclude "non-sense" regions arriving at a much reduced ribbon-like domain in the design space.…”

Section: Multi-level and Multi-fidelity Approximationsmentioning

confidence: 99%

Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not

Simpson

Toropov

Balabanov

et al. 2008

12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

243

148

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The use of metamodeling techniques in the design and analysis of computer experiments has progressed remarkably in the past two decades, but how far have we really come? This is the question that we investigate in this paper, namely, the extent to which the use of metamodeling techniques in multidisciplinary design optimization have evolved in the two decades since the seminal paper on Design and Analysis of Computer Experiments by Sacks et al. As part of this review, we examine the motivation for advancements in metamodeling techniques from both a historical perspective and the research itself. Based on current thrusts in the field, we emphasize multi-level/multi-fidelity approximations and ensembles of metamodels, as well as the availability of metamodels within commercial software and for design space exploration and visualization in this review. Our closing remarks offer insight into future research directions-nearly the same ones that have motivated us in the past. I.

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HSCT configuration design using response surface approximations of supersonic Euler aerodynamics

Cited by 14 publications

References 21 publications

HSCT configuration design space exploration using aerodynamic response surface approximations

HSCT configuration design space exploration using aerodynamic response surface approximations

Response Surface Models Combining Linear and Euler Aerodynamics for Supersonic Transport Design

Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not

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