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Evolving parametric aircraft models for design exploration and optimisation

delete2014-10-01
delete28
PRE
AI
J
Jonathan Byrne *
P
Philip Cardiff
A
Anthony Brabazon
M
Michael O’Neill
DOI:10.1016/j.neucom.2014.04.004delete
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摘要

摘要

En 中文
Traditional CAD tools generate a static solution to a design problem. Parametric systems allow the user to explore many variations on that design theme. Such systems make the computer a generative design tool and are already used extensively as a rapid prototyping technique in architecture and aeronautics. Combining a design generation tool with an analysis software and an evolutionary algorithm provides a methodology for optimising designs. This work combines NASA's parametric aircraft design tool (OpenVSP) with a fluid dynamics solver (OpenFOAM) to create and analyse aircraft An evolutionary algorithm is then used to generate a range of aircraft that maximise lift and reduce drag while remaining within the framework of the original design. Our approach allows the designer to automatically optimise their chosen design and to generate models with improved aerodynamic efficiency. Different components on three aircraft models are varied to highlight the ease and effectiveness of the parametric model optimisation. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Parametric design
Optimisation
Evolutionary algorithms
Computational fluid dynamics

期刊

Neurocomputing 封面图
Neurocomputing
IF:
6.5
论文数:
2.5W
被引数:
6.5W

机构

U
university college dublin
学者数:
2.6W
论文数: 2.2W
被引数: 22
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