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Krylov-Subspace-Based Order Reduction Methods Applied to Generate Compact-Electro-Thermal Models for MEMS

机译:基于krylov-subspace的订单减少方法应用于MEMS产生紧凑型电热模型

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The high power dissipation density in today s miniature electronic/mechanical systems makes on-chip thermal management crucial. In order to achieve quick-to-evaluate, yet accurate electro-thermal models, needed for the thermal management of microsystems, model order reduction is necessary. In this paper, we use Krylov-subspace methods for the order reduction of a electro-thermal MEMS model, illustrated by a novel type of micropropulsion device. Comparison between different moment-matching algorithms including a new two-sided Arnoldi algorithm, is performed.
机译:今天的微型电子/机械系统中的高功耗密度使片上热管理至关重要。为了实现微系统热管理所需的快速评估,且精确的电热模型,需要进行模型顺序。在本文中,我们使用Krylov-sublace方法来减少电热MEMS模型的顺序,通过一种新颖的微生物装置所示。执行包括新的双面Arnoldi算法的不同时机匹配算法之间的比较。

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