Topology optimization for fluid–thermal interaction problems under constant input power

Matsumori, Tadayoshi; Kondoh, Tsuguo; Kawamoto, Atsushi; Nomura, Tsuyoshi

doi:10.1007/s00158-013-0887-8

Cited by 152 publications

(60 citation statements)

References 29 publications

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“…Matsumori et al pointed out that the current optimization problem is non-convex and has many local optima [27]. The present method confirms the same issue and copes with that by GA. GA finds several solutions as a local optimum with the same topology of the results reported in previous study [12].…”

Section: Resultssupporting

confidence: 73%

Topology Optimization Using a Kriging-Assisted Genetic Algorithm With a Novel Level Set Representation Approach

Yoshimura¹,

Shimoyama

Misaka³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Section: Resultssupporting

confidence: 73%

Topology Optimization Using a Kriging-Assisted Genetic Algorithm With a Novel Level Set Representation Approach

Yoshimura¹,

Shimoyama

Misaka³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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“…The previous study [27,28] reports the optimal solution has large islands inside the channel. Thus, it is required to collocate the control points so as to be able to represent large island.…”

Section: Problem Definitionmentioning

confidence: 95%

“…The Prandtl number is set to be 6.78. Each dimensionless number is chosen to compare the present study with the previous study [27,28].…”

Section: Optimization Problems Of Maximizing Heat Transfermentioning

confidence: 99%

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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“…Jing et al [93] has used BEM and level-set method for 2D heat conduction problems. Matsumori et al [94] published fluidthermal interaction problems under constant input power. Kontoleontos et al [95] published an adjoint-based constrained topology optimization for viscous flows, including heat transfer.…”

Section: -2015mentioning

confidence: 99%

Heat Exchanger Design with Topology Optimization

Manuel¹,

Lin²

2017

Heat Exchangers - Design, Experiment and Simulation

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Topology optimization is proving to be a valuable design tool for physical systems, especially for structural systems. However, its application in the field of heat transfer is less evident but is constantly progressing. In this chapter, we would like to introduce topology optimization in the context of heat exchanger design to the general reader. We also provide a chronological review of available literature to see the current progress of topology optimization in the field of heat transfer and heat exchanger design. We expect that topology optimization will prove to be a valuable tool in heat exchanger design for the coming years.

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Topology optimization for fluid–thermal interaction problems under constant input power

Cited by 152 publications

References 29 publications

Topology Optimization Using a Kriging-Assisted Genetic Algorithm With a Novel Level Set Representation Approach

Topology Optimization Using a Kriging-Assisted Genetic Algorithm With a Novel Level Set Representation Approach

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

Heat Exchanger Design with Topology Optimization

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