Topology Optimization Based on Regularized Level-Set Function for Solving 3-D Nonlinear Magnetic Field System With Spatial Symmetric Condition

Okamoto, Yoshifumi; Wakao, Shinji; Sato, Shuji

doi:10.1109/tmag.2015.2492978

Cited by 22 publications

(17 citation statements)

References 11 publications

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“…Thus, (5) can be rewritten as (6). Although |∇φ| = 1 becomes unsatisfied after updating the level-set function, this problem is avoidable by applying a regularization technique (5) .…”

Section: Advection Of the Materials Boundariesmentioning

confidence: 99%

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Design Optimization of Primary Core in Induction Heating Roll by the Combination of 2D Level-set Method and 3D Coupled Magnetic-Thermal FEM

et al. 2018

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We present a novel practical design optimization for the primary core in an induction heating roll, which significantly affects the heating performance. To optimize the 3D eddy current field problem within an acceptable CPU time, we effectively combine 2D magnetostatic optimization and 3D coupled magnetic-thermal FEM. For the 2D optimization, we adopt the level-set method, which has an advantage of being able to derive a feasible shape. However, the disadvantage is that its result occasionally falls into a local optimal solution. To overcome this disadvantage, we propose conducting the initial conceptual design with the linear level-set method before using the nonlinear level-set method to expand the search domain. Furthermore, we incorporate the parallelized move-limit strategy into the level-set method to prevent the configuration from undergoing excess deformation by the operation for the area constraint. Finally, the 2D optimal shape obtained using our new level-set method is converted into a straight line cross-sectional configuration to improve manufacturability, and the final 3D design is created with slits. The final 3D design obtained as a result of the 3D FEM successfully improves both the heating speed and temperature uniformity on the surface of the heating part under the same conditions of input AC current and material volume.

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“…Thus, (5) can be rewritten as (6). Although |∇φ| = 1 becomes unsatisfied after updating the level-set function, this problem is avoidable by applying a regularization technique (5) .…”

Section: Advection Of the Materials Boundariesmentioning

confidence: 99%

“…If (13) is not satisfied after updating the level-set function, we correct level-set function by solving (14) with regard to γ (5) .…”

Section: Considering Area Constraintmentioning

confidence: 99%

Design Optimization of Primary Core in Induction Heating Roll by the Combination of 2D Level-set Method and 3D Coupled Magnetic-Thermal FEM

et al. 2018

Self Cite

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“…[2][3][4][5][6][7][8] Topology optimization methods for electric machines and devices are divided mainly into level set methods 2-5 and on-off based methods using heuristic search. Particularly, topology optimization is an approach that does not require defining design variables which represent device shapes, e.g.…”

Section: Introductionmentioning

confidence: 99%

A Topology Optimization Method for Electric Machines and Devices through Submodular Maximization

Satô

Fujita

2019

IEEJ Trans. FM

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This paper presents a topology optimization method using a greedy algorithm for submodular maximization. This method is based on a shape representation using the normalized Gaussian network. The weight coefficients of Gaussians are discretized to +1/−1, and then their values are greedily inverted. Hence, the computational cost of the present method is relatively smaller than that of evolutionary algorithms. The present method is applied to a magnetic shield optimization problem. It is shown that Pareto solutions can be obtained by the present method. In addition, it can be found from the numerical results that the stochastic greedy algorithm can effectively reduce the computational time compared with the conventional greedy algorithm. As a result, it is shown that a 3-D optimization problem with over 3000 design variables can be solved within acceptable computational time. K E Y W O R D S greedy algorithm, submodular function, topology optimization Electron Comm Jpn. 2019;102:3-11.

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“…length, arc degree, and so on, while allowing for morphological variation, thus emancipating design from bias and burden of the past patterns, and promising a breakthrough in electric machines and devices. Topology optimization was first proposed in the field of structural dynamics, but recently, there is extensive research on applying this approach to optimization of electric machines and devices …”

Section: Introductionmentioning

confidence: 99%

“…Topology optimization methods for electric machines and devices are divided mainly into level set methods and on‐off based methods using heuristic search . In the former case, shape boundaries are expressed in terms of level set functions, shape boundary is moved toward improvement of objective function so as to optimize shape.…”

Section: Introductionmentioning

confidence: 99%

A topology optimization method for electric machines and devices through submodular maximization

Satô

Fujita

2019

Elect Comm in Japan

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Topology Optimization Based on Regularized Level-Set Function for Solving 3-D Nonlinear Magnetic Field System With Spatial Symmetric Condition

Cited by 22 publications

References 11 publications

Design Optimization of Primary Core in Induction Heating Roll by the Combination of 2D Level-set Method and 3D Coupled Magnetic-Thermal FEM

Design Optimization of Primary Core in Induction Heating Roll by the Combination of 2D Level-set Method and 3D Coupled Magnetic-Thermal FEM

A Topology Optimization Method for Electric Machines and Devices through Submodular Maximization

A topology optimization method for electric machines and devices through submodular maximization

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