TY - JOUR
T1 - Knowledge discovery in multidisciplinary design space for regional-jet wings using data mining
AU - Chiba, Kazuhisa
AU - Jeong, Shinkyu
AU - Obayash, Shigeru
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Data mining is an important facet of solving multi-objective optimization problems. In the present study, two data mining techniques were applied to a large-scale, real-world multidisciplinary design optimization (MDO) problem to provide knowledge regarding the design space. The use of MDO in the aerodynamics, structure, and aeroelasticity of a regional-jet wing was carried out using high-fidelity evaluation models with an adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis of three objectives. All solutions evaluated during the evolution were analyzed for the influence of design variables using a self-organizing map (SOM) and a functional analysis of variance (ANOVA) to extract key features of the design space. As SOM and ANOVA compensate for respective disadvantages, the design knowledge could be obtained more clearly by combinating them. Although the MDO results showed inverted gull-wings as non-dominated solutions, one of the key features found by data mining was a 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.
AB - Data mining is an important facet of solving multi-objective optimization problems. In the present study, two data mining techniques were applied to a large-scale, real-world multidisciplinary design optimization (MDO) problem to provide knowledge regarding the design space. The use of MDO in the aerodynamics, structure, and aeroelasticity of a regional-jet wing was carried out using high-fidelity evaluation models with an adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis of three objectives. All solutions evaluated during the evolution were analyzed for the influence of design variables using a self-organizing map (SOM) and a functional analysis of variance (ANOVA) to extract key features of the design space. As SOM and ANOVA compensate for respective disadvantages, the design knowledge could be obtained more clearly by combinating them. Although the MDO results showed inverted gull-wings as non-dominated solutions, one of the key features found by data mining was a 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.
KW - Analysis of variance
KW - Data mining
KW - Multidisciplinary design exploration
KW - Self-organizing map
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U2 - 10.2322/tjsass.50.181
DO - 10.2322/tjsass.50.181
M3 - Article
AN - SCOPUS:80052496592
VL - 50
SP - 181
EP - 192
JO - Transactions of the Japan Society for Aeronautical and Space Sciences
JF - Transactions of the Japan Society for Aeronautical and Space Sciences
SN - 0549-3811
IS - 169
ER -