Aircraft morphing wing design by using partial topology optimization

Sleesongsom, Suwin; Bureerat, Sujin; Tai, Kang

doi:10.1007/s00158-013-0944-3

Cited by 27 publications

(24 citation statements)

References 28 publications

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“…The design variables can be classified into two types: the shape variables (such as the control points of aerofoil) and the structural variables (such as the location and dimension of actuators). The optimisation methods can be classified into three types: topology optimisation algorithms [114][115] [116], parameter optimisation (gradient-based optimisation algorithms [117] [119]), and evolutionary optimisation (algorithms [120]- [132]).…”

Section: Optimisation Designmentioning

confidence: 99%

“…The compliant wing leading-edges with different fibre ply-orientations were generated by the optimality criteria (OC) method and sensitivity filtering technique. Multi-objective population-based incremental learning method was used to optimise the percentage of change in lift effectiveness, buckling factor, and mass of a structure subject to design constraints including divergence and flutter speeds, buckling factors, and stresses[116].…”

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confidence: 99%

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A review of modelling and analysis of morphing wings

Zhao

Ronch

et al. 2018

Progress in Aerospace Sciences

226

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Morphing wings have a large potential to improve the overall aircraft performances, in a way like natural flyers do. By adapting or optimising dynamically the shape to various flight conditions, there are yet many unexplored opportunities beyond current proof-of-concept demonstrations. This review discusses the most prominent examples of morphing concepts with applications to two and threedimensional wing models. Methods and tools commonly deployed for the design and analysis of these concepts are discussed, ranging from structural to aerodynamic analyses, and from control to optimisation aspects. Throughout the review process, it became apparent that the adoption of morphing concepts for routine use on aerial vehicles is still scarce, and some reasons holding back their integration for industrial use are given. Finally, promising concepts for future use are identified.

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Section: Optimisation Designmentioning

confidence: 99%

mentioning

confidence: 99%

A review of modelling and analysis of morphing wings

Zhao

Ronch

et al. 2018

Progress in Aerospace Sciences

226

View full text Add to dashboard Cite

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“…Large-scale optimization generally refers to the solution of optimization problems that involve hundreds or even thousands of decision variables [45]- [47]. As pointed by Weise et al in [48], several factors make large-scale optimization problems extremely difficult.…”

Section: A Basic Concepts In Large-scale Optimizationmentioning

confidence: 99%

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

Cheng

Jin

Olhofer

et al. 2017

IEEE Trans. Cybern.

246

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Abstract-The interests in multi-and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multi-and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multi-and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multi-and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multi-and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and non-uniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multi-and manyobjective evolutionary algorithms are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new evolutionary algorithms dedicated to large-scale multi-and many-objective optimization.

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“…Later, an alternative optimizer is evolutionary algorithms due to the fact they are robust, simple to use, derivative-free, and free from intermediate pseudo densities [8]. Complicated problems, such as partial topology, simultaneous topology, shape, and sizing optimization, can be performed within one optimization run [3,8,16,17] by using such algorithms. In this paper [8], they presented the comparative performance of multi-objective evolutionary algorithms (MOEAs) for solving structural topology optimization test problems based on ground element filtering technique.…”

Section: Topological Designs With Ground Element Filteringmentioning

confidence: 99%

Topology Optimisation Using MPBILs and Multi-Grid Ground Element

Sleesongsom

Bureerat

2018

Applied Sciences

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This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions.

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Aircraft morphing wing design by using partial topology optimization

Cited by 27 publications

References 28 publications

A review of modelling and analysis of morphing wings

A review of modelling and analysis of morphing wings

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

Topology Optimisation Using MPBILs and Multi-Grid Ground Element

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