Multidisciplinary optimization methods for aircraft preliminary design

Kroo, Ilan; Altus, Steve; Robert, Braun; Peter, Gage; Sobieski, I. P.

doi:10.2514/6.1994-4325

Cited by 311 publications

(162 citation statements)

References 4 publications

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“…Collaborative optimisation has mainly been used for aerospace applications, for example, aircraft preliminary design [20] and launch vehicles [28]. However, in a recent article, Xue et al [13] use CO for weight reduction of a car.…”

Section: Collaborative Optimisationmentioning

confidence: 99%

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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: Collaborative Optimisationmentioning

confidence: 99%

“…An early description of CO was published by Kroo et al [20] and further refined by Braun [21]. The method can handle coupling variables, but a simplified variant without coupling variables is presented here.…”

Section: Collaborative Optimisationmentioning

confidence: 99%

Multidisciplinary design optimisation methods for automotive structures

Bäckryd¹,

Ryberg²,

Nilsson³

2017

Int. J. Automot. Mech. Eng.

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“…Proposed by Kroo, Braun et al of Stanford University through the Compatibility Constraint Optimization Algorithm, the strategy of Collaborative Optimization is a MDO method featuring bi-level optimization structures [5].…”

Section: A the Basic Ideas Of Collaborative Optimizationmentioning

confidence: 99%

Multidisciplinary Collaborative Optimization Design for Oil Tanker Concept Design

Song¹,

Lin²

2014

Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEK

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Abstract-The idea of multidisciplinary design optimization is applied to the solution of the optimization design of oil tankers. The multidisciplinary design optimization model is established according to the multi-objective and multi-constraints characteristics of the design of oil tanker. The optimization model is solved by using the collaborative optimization algorithm, and its solution is completed. The numerical results indicate that the collaborative optimization can be successfully applied to dealing with the efficiency of the optimization design as well as the problems of an oil tanker, which lays a foundation to solve more complex engineering systems.

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“…Eighties of last century, Multidisciplinary Design Optimization (MDO) rise in the aerospace field. Scientists as Sobieski and Kroo have done some pioneering work in this area [1][2][3]. MDO is a design methodology that utilizes collaborative mechanism to fully explore the interaction between engineering systems design to design the complex products and subsystems.…”

Section: Introductionmentioning

confidence: 99%

Improvement Multidisciplinary Collaborate Optimization based on Simulated Annealing and Artificial Neural Networks

Ning¹,

Zhao²

2015

TOCSJ

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Multidisciplinary Design Optimization (MDO) is the most active fields in the current complex engineering system design. Forcing to the defects of traditional Collaborative Optimization, such as unable to convergence or falling into local optimum, we propose a Collaborate Optimization based on Simulated Annealing and Artificial Neural Networks, (SA-ANN-CO). The SA-ANN-CO algorithm inherit the parallel distribution strategy of tradition Collaborative Optimization, and then establish accuracy Artificial Neural Networks models by Latin Hypercube Experimental design replace the realistic model of sub-disciplines to reduce computing costs and smooth numerical noise. The possibility of falling into local solutions is reduced by using the Simulated Annealing algorithm in system-level. Two classic test examples results show that, SA-ANN-CO algorithm has good robustness and can quickly and effectively converge to the global optimum solution, which provides a effective way for complex engineering systems design.

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Multidisciplinary optimization methods for aircraft preliminary design

Cited by 311 publications

References 4 publications

Multidisciplinary design optimisation methods for automotive structures

Multidisciplinary design optimisation methods for automotive structures

Multidisciplinary Collaborative Optimization Design for Oil Tanker Concept Design

Improvement Multidisciplinary Collaborate Optimization based on Simulated Annealing and Artificial Neural Networks

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