Shape optimization of airfoils in transonic flow using a multi-objective genetic algorithm

Chen, Xiaomin; Agarwal, Ramesh K.

doi:10.1177/0954410013500613

Cited by 10 publications

(1 citation statement)

References 11 publications

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“…With respect to three types of design variables, structural design optimization can be classified into three categories, namely, size optimization, shape optimization, and topology optimization. And different types of optimization problems solved by different stochastic search algorithms have been presented by researches, such as size optimization, 3,4,8,12,16–19 size and shape optimization, 20–29 or size, shape, and topology optimization. 30–35…”

Section: Introductionmentioning

confidence: 99%

Combined size and shape optimization of truss structures using subset simulation optimization

Tee

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents an approach to solve the combined size and shape design optimization problems using recently developed subset simulation optimization for both continuous and discrete design variables. Except for the componentwise Metropolis–Hasting algorithm, a recently developed adaptive conditional sampling algorithm is also employed as an alternative approach for generating new conditional samples (candidate designs) for each simulation level, which enhances the accuracy and stability of the optimization process. Besides, a double-criterion sorting algorithm is used to handle the design constraints and integrate them in the generation of conditional samples during the Markov Chain Monte Carlo simulation, and the inverse transform method is employed to deal with the discrete design variables. Totally, four numerical examples are considered, including a 15-bar 2D truss, an 18-bar 2D truss, a 39-bar 3D truss and a truss-type landing gear of an unmanned aerial vehicle. The optimal designs obtained from subset simulation optimization using either the componentwise Metropolis–Hasting algorithm or the adaptive conditional sampling algorithm succeed in substantially reducing the weights of the truss-type structures under design constraints in terms of the member stress, the Euler buckling and the nodal displacement. The computational results indicate the proposed method can be taken as an alternative tool for structural optimization design on truss structures when involving the combined size and shape design.

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