Computationally efficient and stable order reduction methods for a large-scale model of MEMS piezoelectric energy harvester
Computationally efficient and stable order reduction methods for a large-scale model of MEMS piezoelectric energy harvester
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DOI：
10.1016/j.microrel.2015.02.003
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发表时间：
2015-04-01
影响因子：
1.6
通讯作者：
Bechtold, T.
中科院分区：
文献类型：
--
作者：
Kudryavtsev, M.;Rudnyi, E. B.;Bechtold, T.
在这项工作中，我们提出了用于基于 MEMS 的压电能量收集器的大规模多端口模型的新颖模型降阶方法。这些技术计算效率高，并生成稳定的降阶模型。提出的第一种方法结合了基于 Krylov 子空间的模型简化和所得系统的 Schur 补变换。第二种方法包括基于模型降阶的结构保留 Krylov 子空间。我们在谐波仿真以及简化采集器模型与电源管理电路的联合仿真过程中展示了采集器设备的全尺寸模型和降阶模型之间的出色匹配。 (C) 2015 Elsevier Ltd. 保留所有权利。
In this work, we present novel model order reduction approaches for a large-scale multiport model of a MEMS-based piezoelectric energy harvester. These techniques are computationally efficient and generate stable reduced order models. The first method proposed combines model reduction based on Krylov subspaces and a Schur complement transformation of the resulting system. The second method includes structure preserving Krylov subspaces based model order reduction. We demonstrate an excellent match between the full-scale and the reduced order models of the harvester device during harmonic simulation and the co-simulation of the reduced harvester model together with the power management circuitry. (C) 2015 Elsevier Ltd. All rights reserved.