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Topology optimization for the microstructure design of dielectric materials

机译：介电材料微观结构设计的拓扑优化

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Recent studies on the design of artificial materials have generated great interest in their application to improve the performance of electromagnetic devices such as electrostatic actuators, waveguides, antennas, and so on. To efficiently design such artificial materials so that desirable properties are maximized, a systematic design method is required. This paper discuss a topology optimization method for the design of microstructures made from dielectric materials.Various research on inverse homogenization problems has described composite material microstructure designs used to develop materials that have extreme properties such as a negative thermal expansion coefficient [1], or a negative Poisson's ratio [2], and materials with a prescribed value of a constitutive tensor such as Young's modulus [3], magnetic permeability [4], dielectric permittivity [5], and so on.In [5], a design methodology for the microstructure of dielectric materials that demonstrate a prescribed effective permittivity was reported, where a genetic algorithm (GA) was used in the optimization procedure.In this work, a gradient-based topology optimization method is applied, instead of meta-heuristic approaches such as GAs, to the microstructure design of dielectric materials, and the design variables are updated based on sensitivities efficiently obtained using the adjoint variable method.
机译：人造材料设计的最新研究引起了人们极大的兴趣，希望将其用于改善电磁设备（如静电致动器，波导，天线等）的性能。为了有效地设计这种人造材料，以使期望的性能最大化，需要一种系统的设计方法。本文讨论了一种用于介电材料制成的微结构设计的拓扑优化方法。关于逆均质化问题的各种研究描述了复合材料的微结构设计，这些设计用于开发具有极端特性（例如负热膨胀系数[1]或a）的材料。负泊松比[2]和具有本构张量规定值的材料，例如杨氏模量[3]，磁导率[4]，介电常数[5]等。在[5]中，一种设计方法报道了证明具有规定有效介电常数的介电材料的微观结构，其中在优化过程中使用了遗传算法（GA）。在这项工作中，应用了基于梯度的拓扑优化方法，而不是诸如亚启发式方法遗传算法，用于介电材料的微观结构设计，并根据使用高效获得的灵敏度更新设计变量伴随变量方法。

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