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A New MEMS Stochastic Model Order Reduction Method: Research and Application

机译:一种新的MEMS随机模型降阶方法:研究与应用

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摘要

Modeling and simulation of MEMS devices is a very complex tasks which involve the electrical, mechanical, fluidic, and thermal domains, and there are still some uncertainties that need to be accounted for during the robust design of MEMS actuators caused by uncertain material and/or geometric parameters. According to these problems, we put forward stochasticmodel order reduction method under random input conditions to facilitate fast time and frequency domain analyses; the method makes use of polynomial chaos expansions in terms of the random input variables for the matrices of a finite element model of the system and then uses its transformation matrix to reduce the model; the method is independent of the MOR algorithm, so it is seamlessly compatible with MOR method used in popular finite element solvers. The simulation results verify the method is effective in large scale MEMS design process.
机译:MEMS器件的建模和仿真是一项非常复杂的任务,涉及电,机械,流体和热域,在由不确定的材料和/或材料引起的MEMS执行器稳健设计过程中,仍需要考虑一些不确定性几何参数。针对这些问题,提出了随机输入条件下的随机模型降阶方法,以利于快速时域和频域分析。该方法利用随机输入变量对系统有限元模型的矩阵进行多项式混沌展开,然后利用其变换矩阵对模型进行简化。该方法与MOR算法无关,因此与流行的有限元求解器中使用的MOR方法无缝兼容。仿真结果验证了该方法在大规模MEMS设计过程中的有效性。

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