Framework for Multidisciplinary Design Based on Response-Surface Approximations

Batill, Stephen M.; Stelmack, Marc A.; Sellar, Richard S.

doi:10.2514/2.2436

Cited by 26 publications

(12 citation statements)

References 11 publications

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“…One popular approximation technique for the design of complex, multi-variable problems is the response surface approximation [8]. Response surfaces typically approximate quantities such as the lift and drag of the vehicle as a function of the design variables, e.g., geometric parameters, in terms of simple functional forms.…”

Section: Introductionmentioning

confidence: 99%

A Karhunen–Loève least-squares technique for optimization of geometry of a blunt body in supersonic flow

Brooks

Powers

2004

Journal of Computational Physics

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Section: Introductionmentioning

confidence: 99%

A Karhunen–Loève least-squares technique for optimization of geometry of a blunt body in supersonic flow

Brooks

Powers

2004

Journal of Computational Physics

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“…Metamodels however are an approximate stand-in for the analyses and they still need to be explored, and this exploration still needs to be coordinated by some method. For example, Batill et al (1999) use metamodels to facilitate CSSO, one of the founding MDO methods that was first developed without metamodels. More discerning uses of metamodels will be discussed later in the context of applications of game theory to collaborative design.…”

Section: Points and Point Setsmentioning

confidence: 99%

Bayesian Network Classifiers for Set-Based Collaborative Design

Shahan

Seepersad

2012

Journal of Mechanical Design

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Complex engineering design problems are often decomposed into a set of interdependent, distributed subproblems that are solved by domain-specific experts. These experts must resolve couplings between the subproblems and negotiate satisfactory, system-wide solutions. Set-based approaches help resolve these couplings by systematically mapping satisfactory regions of the design space for each subproblem and then intersecting those maps to identify mutually satisfactory system-wide solutions. In this paper, Bayesian network classifiers are introduced for mapping sets of promising designs, thereby classifying the design space into satisfactory and unsatisfactory regions. The approach is applied to two example problems—a spring design problem and a simplified, multilevel design problem for an unmanned aerial vehicle (UAV). The method is demonstrated to offer several advantages over competing techniques, including the ability to represent arbitrarily shaped and potentially disconnected regions of the design space and the ability to be updated straightforwardly as new information about the satisfactory design space is discovered. Although not demonstrated in this paper, it is also possible to interface the classifier with automated search and optimization techniques and to combine expert knowledge with the results of quantitative simulations when constructing the classifiers.

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“…A wide variety of approximations have been used for MDO problems in general 50,51,55,60 and for aerodynamics optimization. 100,101 This variety includes response surfaces, neural networks, DACE models, kriging methods, and low-fidelity methods.…”

Section: Aerodynamic Approximationsmentioning

confidence: 99%

“…Numerous multilevel formulations have been developed, including concurrent subspace optimization (CSSO), 60 collaborative optimization (CO), 61 MAESTRO, 62 and bilevel integrated systems synthesis (BLISS).…”

Section: Fig 10 Collaborative Optimization (Co) Formulationmentioning

confidence: 99%

Multidisciplinary design optimization techniques - Implications and opportunities for fluid dynamics research

Zang

Green

1999

30th Fluid Dynamics Conference

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A challenge for the fluid dynamics community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. The past decade has witnessed substantial growth in the research field of Multidisciplinary Design Optimization (MDO). MDO is a methodology for the design of complex engineering systems and subsystems that coherently exploits the synergism of mutually interacting phenomena. As evidenced by the papers, which appear in the biannual AIAA/USAF/NASA/ISSMO Symposia on Multidisciplinary Analysis and Optimization, the MDO technical community focuses on vehicle and system design issues. This paper provides an overview of the MDO technology field from a fluid dynamics perspective, giving emphasis to suggestions of specific applications of recent MDO technologies that can enhance fluid dynamics research itself across the spectrum, from basic flow physics to full configuration aerodynamics.

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Framework for Multidisciplinary Design Based on Response-Surface Approximations

Cited by 26 publications

References 11 publications

A Karhunen–Loève least-squares technique for optimization of geometry of a blunt body in supersonic flow

A Karhunen–Loève least-squares technique for optimization of geometry of a blunt body in supersonic flow

Bayesian Network Classifiers for Set-Based Collaborative Design

Multidisciplinary design optimization techniques - Implications and opportunities for fluid dynamics research

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