Journal of Advanced Mechanical Design, Systems, and Manufacturing
Online ISSN : 1881-3054
ISSN-L : 1881-3054
Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration
Kazuhisa CHIBAShigeru OBAYASHIHiroyuki MORINO
ジャーナル フリー

2008 年 2 巻 3 号 p. 396-407


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 multi-objective 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 self-organizing 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.

© 2008 by The Japan Society of Mechanical Engineers
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