Multi-objective topology optimization of multi-component continuum structures via a Kriging-interpolated level set approach

Guirguis, David; Hamza, Karim; Mahmood, Mahmood A.; Hegazi, Hesham A.; Saitou, Kazuhiro

doi:10.1007/s00158-014-1154-3

Cited by 51 publications

(39 citation statements)

References 48 publications

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“…In this study, the non-dominated sorting genetic algorithm II [18] proposed by Deb et al is employed for exploration because this algorithm is effective and widespread employed for many optimization problems [10,19]. Initially, a parent population P t D1 with the size of N is created randomly.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Temperature is set to be 0 at the inlet, and given by the Neumann condition at the outlet. Furthermore, the temperature at the solid-fluid interface is expressed by the third type boundary condition as described in Equation (10).…”

Section: Optimization Problems Of Maximizing Heat Transfermentioning

confidence: 99%

“…For example, Guirguis et al employed a Kriging-interpolated level set to obtain the distribution of (x) with a small number of the design variables and successfully applied GA to structural topology optimization [10]. In this study, the distribution of (x) is obtained by following procedure.…”

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confidence: 99%

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Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Yoshimura

Shimoyama

Misaka

et al. 2016

Numerical Meth Engineering

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SUMMARYA non-gradient-based approach for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the non-gradient-based topology optimization method in flow problems, this research focuses on two single-objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and one multiobjective optimization problem, which combines earlier two single-objective optimization problems. The shape of flow channels is represented by the level set function. The pressure loss and the heat transfer performance of the channels are evaluated by the Building-Cube Method code, which is a Cartesian-mesh CFD solver. The proposed method resulted in an agreement with previous study in the single-objective problems in its topology and achieved global exploration of non-dominated solutions in the multi-objective problems.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Optimization Problems Of Maximizing Heat Transfermentioning

confidence: 99%

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Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Yoshimura

Shimoyama

Misaka

et al. 2016

Numerical Meth Engineering

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“…The level-set method has been extended to multimaterial topology optimization following two main approaches: the extended variational multilevel sets approach [15][16][17][18][19] and the extended piecewise-constant variational level set approach [20,21]. Recently, Kriging metamodels have been also incorporated in level set-based topology optimization [22][23][24]. The automatic changes of the topology through breaking and merging also require the level-set function to be re-initialized during the update operation in order to achieve appropriate numerical accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis

Liu

Detwiler

Tovar

2017

Journal of Mechanical Design

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This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation, and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization method. The proposed approach is applied to nonlinear, multi-objective design problems for crashworthiness.

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“…Grujicic, Arakere, et al, get new optimal structure with polymer metal hybrid (PMH) material through topology optimization [4]. Structure optimization method of the dynamic characteristic includes two categories which are done based on surrogate model [5][6] and integrate software solvers [7][8]. The optimization based on surrogate model is used to solve the large scale optimization problems, which is efficient but the initial model is complex.…”

Section: Introductionmentioning

confidence: 99%

Lightweight optimization design of automotive aluminum alloy front bumper anti-collision beam

Wang¹

2015

Advances in Computer Science Research

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Abstract. The design of aluminum alloy front bumper beam is based on steel bumper. The process is divided into two stages. The first stage is topology optimization, and the second stage is size optimization. Two thin-walled, hollow aluminum bumpers with reinforced ribs is obtained after topology optimization. Take the thickness of all surfaces and the distance of two reinforced ribs as design variables to conduct size optimization, while maintain the performances of crashworthiness and stiffness. After optimization two type anti-collision aluminum alloy beams are obtained. The comparison results show that the performances of two type anti-collision aluminum alloy beam are better than steel beam, and the mass is smaller. The performance of second type anti-collision beam is better than the first type, but the mass is bigger.

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Multi-objective topology optimization of multi-component continuum structures via a Kriging-interpolated level set approach

Cited by 51 publications

References 48 publications

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis

Lightweight optimization design of automotive aluminum alloy front bumper anti-collision beam

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