Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Obayashi, Shigeru; Jeong, Shinkyu; Chiba, Kazuhisa; Morino, Hiroyuki

doi:10.2322/tjsass.50.1

Cited by 21 publications

(24 citation statements)

References 24 publications

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“…Recently, data mining technique is applied for the optimization result as the hypothetical design database to obtain the fruitful design knowledge efficiently [1,2]. As the combination between the optimization and data mining is a sequence process, it is called as MO design exploration (MODE) [3] or multidisciplinary design exploration (MDE) instead of MDO in the present study. Although Ref.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

Chiba

Obayashi

Morino

2008

JAMDSM

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Data mining is an important facet of solving multi-objective optimization problem. Because it is one of the effective manner to discover the design knowledge in the multiobjective optimization problem which obtains large data. In the present study, data mining has been performed for a large-scale and real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on the adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the tradeoffs and influence of design variables using a selforganizing map to extract key features of the design space. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.

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Section: Introductionmentioning

confidence: 99%

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

Chiba

Obayashi

Morino

2008

JAMDSM

Self Cite

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“…Obayashi et al [2,3] proposed the multiobjective design exploration framework, which consists of the Kriging Model, multiobjective genetic algorithm, analysis of variance, and a self-organizing map. It can explore a broad part of the decision space and derive many Pareto solutions (alternatives) in a reasonable time through Kriging metamodeling of the objective functions.…”

Section: Introductionmentioning

confidence: 99%

Design Mode Analysis of Pareto Solution Set for Decision-Making Support

Hiwa

Hiroyasu

Miki

2014

Journal of Applied Mathematics

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Decision making in engineering design problems is challenging because they have multiple and conflicting criteria and complex correlation between design parameters. This study proposes a decision-making support methodology named design mode analysis, which consists of data clustering and principal component analysis (PCA). A design mode is indicated by the eigenvector obtained by PCA and reveals the dominant design parameters in a given dataset. The proposed method is a general framework to obtain the design modes from high-dimensional and large datasets. The effectiveness of the proposed method is verified on the conceptual design problem of the hybrid rocket engine.

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“…MDO was originally developed for designing aircraft and has more recently been applied to diverse design applications including ship building and automobile engineering. 1,2) Meanwhile, active ongoing investigations have more recently focused on the development of an MDO framework which ensures the fundamental function of multidisciplinary design and additional advanced features such as high fidelity analysis and design, automation of the design process, a simultaneous group collaboration environment, etc. For example, an efficient MDO framework requires the organization of various analysis tools and other resources, local and global optimization aids, Computer Aided Design (CAD) software, Database Management Systems (DBMS) and user-friendly Graphical User Interfaces (GUI).…”

Section: Introductionmentioning

confidence: 99%

A Systematic Approach for Quantitative Analysis of Multidisciplinary Design Optimization Framework

Kim

Park

Lee

et al. 2010

Trans. Japan Soc. Aero. S Sci.

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An efficient Multidisciplinary Design and Optimization (MDO) framework for an aerospace engineering system should use and integrate distributed resources such as various analysis codes, optimization codes, Computer Aided Design (CAD) tools, Data Base Management Systems (DBMS), etc. in a heterogeneous environment, and need to provide user-friendly graphical user interfaces. In this paper, we propose a systematic approach for determining a reference MDO framework and for evaluating MDO frameworks. The proposed approach incorporates two well-known methods, Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD), in order to provide a quantitative analysis of the qualitative criteria of MDO frameworks. Identification and hierarchy of the framework requirements and the corresponding solutions for the reference MDO frameworks, the general one and the aircraft oriented one were carefully investigated. The reference frameworks were also quantitatively identified using AHP and QFD. An assessment of three in-house frameworks was then performed. The results produced clear and useful guidelines for improvement of the in-house MDO frameworks and showed the feasibility of the proposed approach for evaluating an MDO framework without a human interference.

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Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Cited by 21 publications

References 24 publications

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

Design Mode Analysis of Pareto Solution Set for Decision-Making Support

A Systematic Approach for Quantitative Analysis of Multidisciplinary Design Optimization Framework

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