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

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

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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 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 bonviv (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 sofia barclay sexy 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.