Thesis
Model order reduction for multiphysics MEMS simulation
Washington State University
Master of Science (MS), Washington State University
2010
Handle:
https://hdl.handle.net/2376/101688
Abstract
The ability to fabricate complex microelectromechanical systems (MEMS) has constantly expanded over the past few decades leading to a significant amount of interest in the development of robust simulation techniques to aid in device modeling and design. Traditional finite-element method (FEM) has commonly been used in MEMS simulation, but this can place excessive demands on CPU-time and memory resources. This has led to an increased interest in Model Order Reduction (MOR). The goal of MOR is to convert governing Partial Differential Equations {PDE's} into low dimensional Ordinary Differential Equation (ODE) models using robust, mathematical methods which maintain the original model response characteristics while alleviating the computational demands associated with traditional FEM simulation of MEMS devices. In this research, a number of current MOR methods were implemented and investigated with regard to limitations and strengths. A new method that is based on decomposition of an ensemble of Krylov Subspaces is proposed. The method combines the desirable qualities of pre-existing MOR methods and is entitled Krylov Subspace Decomposition (KSD). Numerical simulation examples using the discussed MOR methods are presented. To address multi-physics MEMS behavior, the example problems range from direct coupling of electro-thermal domains, sequential coupling between thermal-mechanical domain interactions, electrostatic pull-in phenomenon and pre-stressed electrostatic microresonator dynamics. Our results demonstrate the efficiency, accuracy and applicability of the investigated MOR methods in MEMS simulation with the KSD method demonstrating the ability to efficiently and accurately capture nonlinear Input/Output MEMS behavior.
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Details
- Title
- Model order reduction for multiphysics MEMS simulation
- Creators
- David Wayne Binion
- Contributors
- Xiaolin Chen (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525133101842
- Language
- English
- Resource Type
- Thesis