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Optimal Thin-Film Topology Design for Specified Temperature Profiles in Resistive Heaters

机译:电阻加热器中特定温度曲线的最佳薄膜拓扑设计

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In this paper, we optimized the topology of a thin-film resistive heater as well as the electrical potential of the electrodes on the boundaries. The objective was to minimize the difference between the actual and prescribed temperature profiles. The thin-film thickness was represented by 100 design variables, and the electrical potential at each electrode were also design variables. The topology optimization problem (inverse problem) has been solved with two methods, i.e., with a genetic algorithm (GA) and with a conjugate gradient method using adjoint and sensitivity problems (CGA). The genetic algorithm used here was modified in order to prevent nonconvergence due to the nonuniqueness of topology representation. The conjugate gradient method used in inverse conduction was extended to cope with our electrothermal problem. The GA and CGA methods started with random topologies and random electrical potential values at electrodes. Both the CGA and GA succeeded in finding optimal thin-film thickness distributions and electrode potential values, even with 100 topology design variables. For most cases, the maximum discrepancy between the optimized and prescribed temperature profiles was under 0.5℃, relative to temperature profiles of the order of 70℃. The CGA method was faster to converge, but was more complex to implement and sometimes led to local minima. The GA was easier to implement and was more unlikely to lead to a local minimum, but was much slower to converge.
机译:在本文中,我们优化了薄膜电阻加热器的拓扑结构以及边界上电极的电势。目的是最小化实际温度曲线与规定温度曲线之间的差异。薄膜厚度用100个设计变量表示,每个电极上的电势也是设计变量。已经通过两种方法解决了拓扑优化问题(反问题),即,利用遗传算法(GA)和使用伴随和敏感性问题的共轭梯度法(CGA)。为了防止由于拓扑表示的不唯一性引起的不收敛,对此处使用的遗传算法进行了修改。逆传导中使用的共轭梯度法得到扩展,以解决我们的电热问题。 GA和CGA方法从电极的随机拓扑和随机电势值开始。 CGA和GA都成功地找到了最佳的薄膜厚度分布和电极电势值,即使有100个拓扑设计变量也是如此。在大多数情况下,相对于70℃左右的温度曲线,最优化温度曲线与规定温度曲线之间的最大差异在0.5℃以下。 CGA方法收敛速度更快,但实现起来较为复杂,有时会导致局部最小值。遗传算法更易于实施,不太可能导致局部最小值,但收敛速度却慢得多。

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