Implementation and performance issues in collaborative optimization

Braun, Robert D.; Gage, Peter; Kroo, Ilan; Sobiesiki, I

doi:10.2514/6.1996-4017

Cited by 132 publications

(71 citation statements)

References 21 publications

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“…Therefore, it is possible to address the FD&C integration/interaction challenge, and take advantage of the concurrent structure to increase freedom in the design space. Among many different MDO strategies, Collaborative Optimization (CO) 13 shown in Figure 2 has been found to be one suitable alternative to include flight dynamics and control in the design process. CO is a bi-level optimization scheme that decouples the design process by providing the common design variables and disciplinary coupling interactions all at once in an upper level, eliminating the need for an a priori process that accumulates all the disciplinary data required to perform FD&C analyses.…”

Section: A Multidisciplinary Design Integrationmentioning

confidence: 99%

Multidisciplinary Optimization Framework for Control-Configuration Integration in Aircraft Conceptual Design

Perez

Liu

Behdinan

2006

Journal of Aircraft

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The emerging flight-by-wire and flight-by-light technologies increase the possibility of enabling and improving aircraft design with excellent handling qualities and performance across the flight envelope. As a result, it is desired to take into account the dynamic characteristics and automatic control capabilities at the early conceptual stage. In this paper, an integrated control-configured aircraft design sizing framework is presented. It makes use of multidisciplinary design optimization to overcome the challenges which the flight dynamics and control integration present when included with the traditional disciplines in an aircraft sizing process.A commercial aircraft design example demonstrates the capability of the proposed methodology. The approach brings higher freedom in design, leading to aircraft that exploit the benefits of control configuration. It also helps to reduce time and cost in the engineering development cycle.Nomenclaturē c mean aerodynamic chord, ft r reference signal u control vector

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Section: A Multidisciplinary Design Integrationmentioning

confidence: 99%

Multidisciplinary Optimization Framework for Control-Configuration Integration in Aircraft Conceptual Design

Perez

Liu

Behdinan

2006

Journal of Aircraft

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“…In CO, the discipline optimization subproblems are made independent of each other by using copies of the coupling and shared design variables [138,139]. These copies are then shared with all the disciplines during every iteration of the solution procedure.…”

Section: Collaborative Optimization (Co)mentioning

confidence: 99%

“…The simplest and most effective CO adjustment involves relaxing the system subproblem equality constraints to inequalities with a relaxation tolerance; this was originally proposed by Braun et al [139]. This approach was also successful in other test problems [144,145], where the tolerance is a small fixed number, usually 10 −6 .…”

Section: Collaborative Optimization (Co)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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“…This causes the Lagrange multipliers associated with the compatibility constraints to tend to zero, resulting in numerical problems that adversely affect convergence when using gradient-based optimizers. The existence of multiple subspace solution regions can also produce inaccuracies in the system-level Jacobian (Braun et al 1996) and further hinder convergence. Sobieski and Kroo (2000) suggested using response surfaces to model the discipline optimizations as a solution to some of the system-level convergence difficulties.…”

Section: Collaborative Optimizationmentioning

confidence: 99%

“…In the case of collaborative optimization, there are at least four major variants (Alexandrov and Lewis 2002;Sobieski and Kroo 2000;Braun et al 1996;Braun and Kroo 1997). Though many of the implementations have been used to solve specific problems, there has been no study that thoroughly tested each implementation in a statistically significant manner.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking multidisciplinary design optimization algorithms

2009

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A comparison of algorithms for multidisciplinary design optimization (MDO) is performed with the aid of a new software framework. This framework, pyMDO, was developed in Python and is shown to be an excellent platform for comparing the performance of the various MDO methods. pyMDO eliminates the need for reformulation when solving a given problem using different MDO methods: once a problem has been described, it can automatically be cast into any method. In addition, the modular design of pyMDO allows rapid development and benchmarking of new methods. Results generated from this study provide a strong foundation for identifying the performance trends of various methods with several types of problems.

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Implementation and performance issues in collaborative optimization

Cited by 132 publications

References 21 publications

Multidisciplinary Optimization Framework for Control-Configuration Integration in Aircraft Conceptual Design

Multidisciplinary Optimization Framework for Control-Configuration Integration in Aircraft Conceptual Design

Multidisciplinary Design Optimization: A Survey of Architectures

Benchmarking multidisciplinary design optimization algorithms

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