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

Yoshimura, Mitsuo; Shimoyama, Koji; Misaka, Takashi; Obayashi, Shigeru

doi:10.1002/nme.5295

Cited by 39 publications

(20 citation statements)

References 29 publications

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“…Yoshimura et al [47] proposed a gradient-free approach using a genetic algorithm to update a very coarse design using a Kriging surrogate model coupled to an immersed method known as the Building-Cube Method. Pereira et al [48] applied a density-based approach to Stokes flow using polygonal elements and supplying a freely available code for fluid topology optimisation.…”

Section: Steady Laminar Flowmentioning

confidence: 99%

“…Qian and Dede [120] introduced a constraint on the tangential thermal gradient around discrete heat sources with the goal of reducing thermal stress due to non-uniform expansion. Yoshimura et al [47] proposed a gradient-free approach using a genetic algorithm and a Kriging surrogate model coupled to an immersed method known as the Building-Cube Method. Haertel and Nellis [121] developed a plane two-dimensional fully-developed flow model for topology optimisation of air-cooled heat sinks.…”

Section: Forced Convectionmentioning

confidence: 99%

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A Review of Topology Optimisation for Fluid-Based Problems

Alexandersen

Andreasen

2020

Fluids

185

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This review paper provides an overview of the literature for topology optimisation of fluid-based problems, starting with the seminal works on the subject and ending with a snapshot of the state of the art of this rapidly developing field. “Fluid-based problems” are defined as problems where at least one governing equation for fluid flow is solved and the fluid–solid interface is optimised. In addition to fluid flow, any number of additional physics can be solved, such as species transport, heat transfer and mechanics. The review covers 186 papers from 2003 up to and including January 2020, which are sorted into five main groups: pure fluid flow; species transport; conjugate heat transfer; fluid–structure interaction; microstructure and porous media. Each paper is very briefly introduced in chronological order of publication. A quantititive analysis is presented with statistics covering the development of the field and presenting the distribution over subgroups. Recommendations for focus areas of future research are made based on the extensive literature review, the quantitative analysis, as well as the authors’ personal experience and opinions. Since the vast majority of papers treat steady-state laminar pure fluid flow, with no recent major advancements, it is recommended that future research focuses on more complex problems, e.g., transient and turbulent flow.

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Section: Steady Laminar Flowmentioning

confidence: 99%

Section: Forced Convectionmentioning

confidence: 99%

A Review of Topology Optimisation for Fluid-Based Problems

Alexandersen

Andreasen

2020

Fluids

185

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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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“…If the reduced design space is then combined with techniques that reduce the analysis computational cost (see for example [11,12,13]) the result may be a computationally tractable approach for performing topology optimization using finite differencing, automatic differentiation or even gradient-free optimization methods. A recent demonstration of this idea was presented by Yoshimura et al [14] where topology optimization for a multi-objective thermal-fluid problem was performed using a genetic algorithm, assisted by a Kriging surrogate model. Another example was presented by Bujny et al [15], which used the beam implicit function representation introduced by Guo et al [16] combined with an evolutionary strategy to optimize a structure for an impact scenario.…”

Section: Introductionmentioning

confidence: 99%

Design parameterization for topology optimization by intersection of an implicit function

Dunning

2017

Computer Methods in Applied Mechanics and Engineering

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This paper introduces a new approach to topology optimization where the structural boundary is defined by the intersection of an implicit signed-distance function and a cutting surface. The cutting surface is discretized using finite element shape-function polynomials and the nodal values become the design variables during optimization. Thus, the design parameterization is separated from the analysis discretization, which enables a reduction in the number of design variables, compared with methods that use element-wise variables. The proposed method can obtain solutions with smooth boundaries and the parameterization allows solution by standard optimization methods. Several 2D and 3D examples are used to demonstrate the effectiveness of the method, including minimization of compliance and complaint mechanism problems. The results show that the method can obtain good solutions to well-known problems with smooth, clearly defined boundaries and that this can be achieved using significantly fewer design variables compared with element-based methods.

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

Cited by 39 publications

References 29 publications

A Review of Topology Optimisation for Fluid-Based Problems

A Review of Topology Optimisation for Fluid-Based Problems

Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis

Design parameterization for topology optimization by intersection of an implicit function

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