非线性非理想压电能量采集器的深度学习数值优化

IF 1.6 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Andreas Hegendörfer, P. Steinmann, J. Mergheim
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引用次数: 0

摘要

这一贡献涉及在考虑非线性应力约束的情况下,在三角形类冲击激励下,机械和电气非线性和非理想压电能量采集器(PEH)的采集能量的数值优化。在优化问题中,考虑了配备Greinacher电路或标准电路的双晶机电结构,并引入了不同的电气和机械设计变量。使用非常精确的耦合有限元电子电路模拟器方法,生成深度神经网络(DNN)训练数据,允许对目标函数进行计算高效的评估。随后,使用DNN的遗传算法被应用于寻找优化收获能量的电气和机械设计变量。研究发现,在最大可能的机械应力下获得了最大的收获能量,并且Greinacher电路的最佳存储电容器比标准电路的存储电容器小得多,而两种配置的总收获能量相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical Optimization of a Nonlinear Nonideal Piezoelectric Energy Harvester Using Deep Learning
This contribution addresses the numerical optimization of the harvested energy of a mechanically and electrically nonlinear and nonideal piezoelectric energy harvester (PEH) under triangular shock-like excitation, taking into account a nonlinear stress constraint. In the optimization problem, a bimorph electromechanical structure equipped with the Greinacher circuit or the standard circuit is considered and different electrical and mechanical design variables are introduced. Using a very accurate coupled finite element-electronic circuit simulator method, deep neural network (DNN) training data are generated, allowing for a computationally efficient evaluation of the objective function. Subsequently, a genetic algorithm using the DNNs is applied to find the electrical and mechanical design variables that optimize the harvested energy. It is found that the maximum harvested energy is obtained at the maximum possible mechanical stresses and that the optimum storage capacitor for the Greinacher circuit is much smaller than that for the standard circuit, while the total harvested energy by both configurations is similar.
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来源期刊
Journal of Low Power Electronics and Applications
Journal of Low Power Electronics and Applications Engineering-Electrical and Electronic Engineering
CiteScore
3.60
自引率
14.30%
发文量
57
审稿时长
11 weeks
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