Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem
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- @Article{dong:2019:AS,
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author = "Guirong Dong and Xiaozhe Wang and Dianzi Liu",
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title = "Metaheuristic Approaches to Solve a Complex Aircraft
Performance Optimization Problem",
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journal = "Applied Sciences",
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year = "2019",
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volume = "9",
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number = "15",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/9/15/2979",
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DOI = "doi:10.3390/app9152979",
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abstract = "The increasing demands for travelling comfort and
reduction of carbon dioxide emissions have been
considered substantially in the stage of conceptual
aircraft design. However, the design of a modern
aircraft is a multidisciplinary process, which requires
the coordination of information from several specific
disciplines, such as structures, aerodynamics, control,
etc. To address this problem with adequate accuracy,
the multidisciplinary analysis and optimisation (MAO)
method is usually applied as a systematic and robust
approach to solve such complex design issues arising
from industries. Since MAO method is tedious and
computationally expensive, genetic programming
(GP)-based metamodelling techniques incorporating MAO
are proposed as an effective approach to minimise the
wing stiffness of a large aircraft subject to
aerodynamic, aeroelastic and stability constraints in
the conceptual design phase. Based on the linear
small-disturbance theory, the state-space equation is
employed for stability analysis. In the process of
multidisciplinary analysis, aeroelastic response
simulations are performed using Nastran. To construct
metamodels representing the responses of the interests
with high accuracy as well as less computational
burden, optimal Latin hypercube design of experiments
(DoE) is applied to determine the optimised
distribution of sampling points. Following that,
parametric optimisation is carried out on metamodels to
obtain the optimal wing geometry shape, elastic axis
positions and stiffness distribution, and then the
solution is verified by finite element simulations.
Finally, the superiority of the GP-based metamodel
technique over genetic algorithm is demonstrated by
multidisciplinary design optimisation of a
representative beam-frame wing structure in terms of
accuracy and efficiency. The results also show that GP
metamodel-based strategy for solving MAO problems can
provide valuable insights to tailoring parameters for
the effective design of a large aircraft in the
conceptual phase.",
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notes = "also known as \cite{app9152979}",
- }
Genetic Programming entries for
Guirong Dong
Xiaozhe Wang
Dianzi Liu
Citations