Current researches on aircraft design aim to reduce airplanes and components weights, optimizing aircraft performances and contributing to the challenge of reducing fuel consumption and operational costs. In this perspective novel materials and technologies are developed, but also advances in design methods and tools. Generative Design is a novel approach to automatically optimize component design. The design process has to be designed itself to achieve the optimal solution, in relation to design parameters, requirements and limits. Which peculiar features justify considering this technique to be a substantial step forward with respect to classical MDO? Could Generative Design be only an important, but not particularly differentiated approach for the design of (aerospace) structures and possibly systems of a higher level? For example, when the design goal is to find the best configuration of a structure, does generative design lead to the discovery of new concepts, or types of structures, or it is a particular application of genetic algorithms to topological optimization? This paper aims to contribute to give an answer to the previous questions. Specifically, the generative design approach is expected to be able to select between basic concepts and use these as the basic instructions and ingredients of a recipe for the design of a new system. By these considerations, in this paper, we revised the improvements brought by Generative Design principles within the traditional design procedure in aeronautics, considering Additive Manufacturing technology.

Generative design: Advanced design optimization processes for aeronautical applications / Bagassi, S.; Lucchi, F; De Crescenzio, F.; Persiani, F.. - CD-ROM. - (2016), pp. 1-7. (Intervento presentato al convegno 30th Congress of the International Council of the Aeronautical Sciences, ICAS 2016 tenutosi a Daejeon Convention Center (DCC), kor nel 2016).

Generative design: Advanced design optimization processes for aeronautical applications

BAGASSI, SARA;LUCCHI, FRANCESCA;DE CRESCENZIO, FRANCESCA;PERSIANI, FRANCO
2016

Abstract

Current researches on aircraft design aim to reduce airplanes and components weights, optimizing aircraft performances and contributing to the challenge of reducing fuel consumption and operational costs. In this perspective novel materials and technologies are developed, but also advances in design methods and tools. Generative Design is a novel approach to automatically optimize component design. The design process has to be designed itself to achieve the optimal solution, in relation to design parameters, requirements and limits. Which peculiar features justify considering this technique to be a substantial step forward with respect to classical MDO? Could Generative Design be only an important, but not particularly differentiated approach for the design of (aerospace) structures and possibly systems of a higher level? For example, when the design goal is to find the best configuration of a structure, does generative design lead to the discovery of new concepts, or types of structures, or it is a particular application of genetic algorithms to topological optimization? This paper aims to contribute to give an answer to the previous questions. Specifically, the generative design approach is expected to be able to select between basic concepts and use these as the basic instructions and ingredients of a recipe for the design of a new system. By these considerations, in this paper, we revised the improvements brought by Generative Design principles within the traditional design procedure in aeronautics, considering Additive Manufacturing technology.
2016
30th Congress of the International Council of the Aeronautical Sciences, ICAS 2016
1
7
Generative design: Advanced design optimization processes for aeronautical applications / Bagassi, S.; Lucchi, F; De Crescenzio, F.; Persiani, F.. - CD-ROM. - (2016), pp. 1-7. (Intervento presentato al convegno 30th Congress of the International Council of the Aeronautical Sciences, ICAS 2016 tenutosi a Daejeon Convention Center (DCC), kor nel 2016).
Bagassi, S.; Lucchi, F; De Crescenzio, F.; Persiani, F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/584290
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