Multi-material topology optimization for thermal buckling criteria

Wu, Chi; Fang, Jianguang; Li, Qing

doi:10.1016/j.cma.2018.08.015

Cited by 63 publications

(19 citation statements)

References 48 publications

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“…Each partial optimization criterion contributes to the total value of the complex objective function. Then, the significance of its contribution is determined by the user with the help of a weighting factor for each partial criterion [37].…”

Section: Proposed Methodsmentioning

confidence: 99%

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

et al. 2020

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This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies.The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N 2 ).

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Section: Proposed Methodsmentioning

confidence: 99%

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

et al. 2020

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“…Test methods generally involve expensive costs, so relatively few experimental tests are conducted, which are often employed to validate analytical methods and numerical methods [ 3 ]. Various numerical methods have been proposed to deal with the buckling problems with complex geometry topology [ 4 , 5 , 6 , 7 , 8 , 9 ]. Although topology optimization can achieve novel designs that possess excellent buckling resistance ability, the buckling load is determined and hard to change once designed.…”

Section: Introductionmentioning

confidence: 99%

CFRP Origami Metamaterial with Tunable Buckling Loads: A Numerical Study

et al. 2021

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Origami has played an increasingly central role in designing a broad range of novel structures due to its simple concept and its lightweight and extraordinary mechanical properties. Nonetheless, most of the research focuses on mechanical responses by using homogeneous materials and limited studies involving buckling loads. In this study, we have designed a carbon fiber reinforced plastic (CFRP) origami metamaterial based on the classical Miura sheet and composite material. The finite element (FE) modelling process’s accuracy is first proved by utilizing a CFRP plate that has an analytical solution of the buckling load. Based on the validated FE modelling process, we then thoroughly study the buckling resistance ability of the proposed CFRP origami metamaterial numerically by varying the folding angle, layer order, and material properties, finding that the buckling loads can be tuned to as large as approximately 2.5 times for mode 5 by altering the folding angle from 10° to 130°. With the identical rate of increase, the shear modulus has a more significant influence on the buckling load than Young’s modulus. Outcomes reported reveal that tunable buckling loads can be achieved in two ways, i.e., origami technique and the CFRP material with fruitful design freedoms. This study provides an easy way of merely adjusting and controlling the buckling load of lightweight structures for practical engineering.

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“…In the past decades, a variety of topology optimization methods such as level set method [4][5], phase field method [6][7] bi-directional evolutionary structural optimization (BESO) method [8][9], and variable density method [10][11] has been extensively and deeply researched. These topology optimization methods gradually expanded from single-material to multi-materials [12][13][14][15]. Guo el al.…”

Section: Introductionmentioning

confidence: 99%

Multi-material Topology Optimization Based on Multiple SIMP of Variable Density Method

Jiao¹,

Li²

2021

Preprint

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In this paper a multiple SIMP (solid isotropic material with penalization model) of variable density method is proposed to solve the problem of muli-material topology optimization. All candidate materials including void material are arranged in descending order of elastic modulus. The material conversion scheme of multiple SIMP is based on the elastic modulus of the candidate material after interpolation. Based on the guide-weight method, the iterative criterion of muti-material topology optimization is derived from the Kuhn-Tucker condition. Three examples show that the proposed method is effective and robust to solve the problem of muti-material topology optimization.

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Multi-material topology optimization for thermal buckling criteria

Cited by 63 publications

References 48 publications

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

CFRP Origami Metamaterial with Tunable Buckling Loads: A Numerical Study

Multi-material Topology Optimization Based on Multiple SIMP of Variable Density Method

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