Topology Optimization Method for Asymmetrical Rotor Using Cluster and Cleaning Procedure

Ishikawa, Takeo; Mizuno, Shota; Krita, Nobuyuki

doi:10.1109/tmag.2017.2665441

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…Using a modified p-norm distance, stress functions have been grouped to reduce the number of constraints in stress-constrained topology optimization [21]. A clustering method in a genetic algorithm is also being reported for the design of rotor topologies [27,28]. Clustering in topology optimization has been utilized by researchers at the Honda Research Institute Europe GmbH to reduce the dimension in a multi-dimensional feature space [4].…”

Section: Md-19-1029mentioning

confidence: 99%

Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

Detwiler

et al. 2019

Journal of Mechanical Design

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This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.

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Section: Md-19-1029mentioning

confidence: 99%

Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

Detwiler

et al. 2019

Journal of Mechanical Design

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“…The exponential function in (4) is introduced to relax a discontinuous function for discrete material states (i.e., iron or coil) into the continuous function. Here, the continuous function is required to apply a gradient-based optimization algorithm; refer to interpolation functions in topology optimization to control material states in [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The proposed interpolation functions in (3) and (4) could determine the number of coil turns together with the material state.…”

Section: Optimization Strategymentioning

confidence: 99%

“…The structural optimization of permanent magnet material was studied in [9][10][11][12] to determine the optimal magnet shape and magnetization directions. In [13][14][15][16][17], both iron and permanent magnet materials were simultaneously designed using structural optimization techniques. In [18,19], all three device components (i.e., iron, permanent magnet, and coil) were co-designed for maximizing the magnetic force of electromagnetic actuators.…”

Section: Introductionmentioning

confidence: 99%

“…In [18,19], all three device components (i.e., iron, permanent magnet, and coil) were co-designed for maximizing the magnetic force of electromagnetic actuators. While the structural optimization techniques [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] are certain to be an efficient approach for enhancing the magnetic force of electromechanical devices, they have a limitation that the magnetic field distribution is indirectly handled through optimizing the shape and configuration of the device components.…”

Section: Introductionmentioning

confidence: 99%

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Magnetic Force Enhancement Using Air-Gap Magnetic Field Manipulation by Optimized Coil Currents

Lee

2019

Applied Sciences

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This paper presents an air-gap magnetic field manipulation by optimized coil currents for a magnetic force enhancement in electromechanical devices. The external coil is designed near the device air-gap for manipulating the magnetic field distribution. The distribution of external coil currents is then optimized for maximizing the magnetic force in the tangential direction to the air-gap line. For the optimization, the design domain near air-gap is divided into small areas, and design variables are assigned at each small design area. The design variables determines not only the strength of coil current density (i.e., number of coil turns) but also whether the material state is coil or iron. In a benchmark actuator example, it is shown that 11.12% force enhancement is available by manipulating the air-gap magnetic field distribution using the optimized coil current. By investigating the magnetic field distribution, it is confirmed that the optimized coil current manipulated the magnetic field, forwarding a focused and inclined distribution that is an ideal distribution for maximizing the magnetic force.

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“…Using a modified P-norm distance, stress functions have been grouped to reduce the number of constraints in stress-constrained topology optimization [42]. A clustering method in a genetic algorithm is also being reported for the design of rotor topologies [43,44]. Clustering in topology optimization has been utilized by researchers at the Honda Research Institute Europe GmbH to reduce the dimension in a (multi-dimensional) local state features space [45].…”

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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Topology Optimization Method for Asymmetrical Rotor Using Cluster and Cleaning Procedure

Cited by 11 publications

References 15 publications

Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

Magnetic Force Enhancement Using Air-Gap Magnetic Field Manipulation by Optimized Coil Currents

Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis

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