Flexible Approximation Model Approach for Bi-Level Integrated System Synthesis

Kim, Hongman; Ragon, S.; Soremekun, Grant; Malone, Brett; Sobieszczanski, J.

doi:10.2514/6.2004-4545

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…The surrogate models represent the coupling variables output from each subspace as functions of the shared variables, the coupling variables input to that subspace, and the weighting coefficients. Polynomial surrogate models are used in the original version of BLISS 2000 but Kriging surrogate models have also been used [45]. In principle, each subspace could be given the freedom to choose their own surrogate model.…”

Section: Other Methodsmentioning

confidence: 99%

Multidisciplinary design optimisation methods for automotive structures

Bäckryd¹,

Ryberg²,

Nilsson³

2017

Int. J. Automot. Mech. Eng.

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Multidisciplinary design optimisation (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously in order to make the solution process efficient. In this article, the formulations of existing MDO methods are compared and their suitability is assessed in relation to the characteristics of automotive structural applications. Both multi-level and single-level optimisation methods are considered. Multi-level optimisation methods distribute the design process but are complex. When optimising automotive structures, metamodels are often required to relieve the computational burden of detailed simulation models. The metamodels can be created by individual groups prior to the optimisation process, and thus offer a way of distributing work. Therefore, it is concluded that a single-level method in combination with metamodels is the most straightforward way of implementing MDO into the development of automotive structures. If the benefits of multi-level optimisation methods, in a special case, are considered to compensate for their drawbacks, analytical target cascading has a number of advantages over collaborative optimisation, but both methods are possible choices.

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Section: Other Methodsmentioning

confidence: 99%

Multidisciplinary design optimisation methods for automotive structures

Bäckryd¹,

Ryberg²,

Nilsson³

2017

Int. J. Automot. Mech. Eng.

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“…The resulting variants of BLISS are BLISS/A and BLISS/B, respectively. Other variations use surrogate models to compute post-optimality derivatives [67,159]. Compute optimal solution to system subproblem until 11 → 1: System optimization has converged Figure 11 shows the XDSM for BLISS, and the procedure is listed in Algorithm 5.…”

Section: E Bilevel Integrated System Synthesis (Bliss)mentioning

confidence: 99%

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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“…The demand function q(a, p) is determined by the spline functions and the demand functions in Equations (15) and (16). The cost function C(x, D) is determined by the sum of the costs described by Equation (14). Finally, the utility u(Π) = Π.…”

Section: Example: Motor Designmentioning

confidence: 99%

“…Model coordination and goal coordination are two common methods for the decomposition of large scale design optimization problems [12,13]. MDO problems have been the focus of decomposition approaches such as the bi-level integrated system synthesis (BLISS) approach [14], analytical target cascading [15,16], collaborative optimization [17], and coupled subspace optimization (CSSO) [18,19]. Yoshimura et al [20] decompose a multi-objective optimization problem into a hierarchy of problems that have two objectives.…”

Section: Introductionmentioning

confidence: 99%