Optimization of coupled systems - A critical overview of approaches

Balling, Richard; Sobieszczanski‐Sobieski, Jaroslaw

doi:10.2514/3.13015

Cited by 263 publications

(101 citation statements)

References 20 publications

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“…What most authors refer to as AAO, following the lead of Cramer et al [60], others label as the simultaneous analysis and design (SAND) [88,77] problem. Our AAO problem is most like what Cramer et al refer to as simply "the most general formulation" [60].…”

Section: B the All-at-once (Aao) Problem Statementmentioning

confidence: 99%

“…All three have been known in the literature for a long time [89,88,60,77]. In the next three subsections, we describe how each architecture is derived and the relative advantages and disadvantages of each.…”

Section: B the All-at-once (Aao) Problem Statementmentioning

confidence: 99%

“…This architecture has also been referred to in the literature as fully integrated optimization [61] and nested analysis and design [77]. The resulting optimization problem is minimize f 0 (x, y (x, y))…”

Section: E Multidisciplinary Feasible (Mdf)mentioning

confidence: 99%

“…Some of the previous classifications were based on which constraints could be controlled by the optimizer [77,121]. Alexandrov and Lewis [121] used the term "closed" to indicate that a set of constraints cannot be satisfied via explicit action of the optimizer, and "open" to indicate that it can.…”

Section: B Classificationmentioning

confidence: 99%

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Multidisciplinary Design Optimization: A Survey of Architectures

2013

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Multidisciplinary design optimization (MDO) is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of MDO, various methods (architectures) have been developed and applied to solve MDO problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitates the understanding of the various architectures, and how they relate to each other.A classification of the MDO architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both a theoretical and experimental perspective. For each architecture, several applications to the solution of engineering design problems are cited. The result is a comprehensive but straightforward introduction to MDO for non-specialists, and a reference detailing all current MDO architectures for specialists.

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Section: B the All-at-once (Aao) Problem Statementmentioning

confidence: 99%

Section: B the All-at-once (Aao) Problem Statementmentioning

confidence: 99%

Section: E Multidisciplinary Feasible (Mdf)mentioning

confidence: 99%

Section: B Classificationmentioning

confidence: 99%

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Multidisciplinary Design Optimization: A Survey of Architectures

2013

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“…The mathematical programming approach to decomposition-based design is referred to as Multidisciplinary Design Optimization (MDO) (Alexandrov 2005). A large number of MDO papers has been published during the last decades, see for instance Cramer et al (1994), Balling and Sobieszczanski-Sobieski (1996), Sobieszczanski-Sobieski and Haftka (1997), Tosserams et al (2009), Martins and Lambe (2013), and Allison and Herber (2014) for reviews of selected topics. A substantial research focus has been on the mathematical formulation and organization of the coordination problem assuming a particular decomposition structure.…”

Section: Introductionmentioning

confidence: 99%

Decomposition analysis of the multidisciplinary coupling in LED System-in-Package design using a DSM and a specification language

Borst

Etman

Gielen³

et al. 2016

Struct Multidisc Optim

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LED System-in-Package (SiP) aims to reduce manufacturing and material costs of LED lighting products through integration of components into one single package, based on semiconductor technology. This introduces multidisciplinary coupling in the system behavior which requires reconsideration of the existing LED design decomposition practice. This paper presents our method to do a decomposition analysis of the multidisciplinary coupling structure for an industry scale LED SiP design problem. The innovative aspect of our method is the use of a specification language to specify the input-output (binary) relationships between design variables, behavioral responses, objective and constraint responses. A design structure matrix, representing the mutual linkage, is automatically generated from the specification. The rows and columns of the DSM are subsequently re-ordered using partitioning and sequencing algorithms to provide insight in the coupling structure. The method is illustrated by means of a simplified example. This paper is a revised and extended version of proceedings paper "Decomposition-based analysis of the multi-physical coupling structure in LED System-in-Package design" presented by the authors at the 10th World Congress on Structural and Multidisciplinary Optimization, May 19-24, 2013 Subsequently, the results for a recently manufactured LED SiP design prototype are presented.

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Multidisciplinary Design Optimization Architectures

2015

Multidisciplinary Design Optimization Supported by Knowledge Based Engineering

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Optimization of coupled systems - A critical overview of approaches

Cited by 263 publications

References 20 publications

Multidisciplinary Design Optimization: A Survey of Architectures

Multidisciplinary Design Optimization: A Survey of Architectures

Decomposition analysis of the multidisciplinary coupling in LED System-in-Package design using a DSM and a specification language

Multidisciplinary Design Optimization Architectures

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