Analyzing damping in large models of complex dynamic systems

From the nano scale to the macro scale, large models are used to simulate and predict the responses of dynamic systems. The construction and evaluation of such models, often in the form of finite element models, require tremendous computational resources and time. Due to this large computational end...

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Main Author: Liem, Alyssa Tomoko
Other Authors: McDaniel, James G.
Language:en_US
Published: 2021
Subjects:
Online Access:https://hdl.handle.net/2144/42595
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spelling ndltd-bu.edu-oai-open.bu.edu-2144-425952021-05-28T17:01:44Z Analyzing damping in large models of complex dynamic systems Liem, Alyssa Tomoko McDaniel, James G. Acoustics Finite element analysis Fluid structure interaction Nano electro mechanical systems Neumann series Vibrations From the nano scale to the macro scale, large models are used to simulate and predict the responses of dynamic systems. The construction and evaluation of such models, often in the form of finite element models, require tremendous computational resources and time. Due to this large computational endeavor, it is paramount to learn as much as possible from the models and their solutions. In this work, analyses and methods for efficiently deriving significant knowledge of damped systems from models and their solutions are presented. Of primary interest to this work is the analysis of damped structures. Damping, the means by which energy is dissipated, often adds an additional layer of complexity to finite element models and any subsequent analyses. This added complexity is due to the relative complexity of many damping models and their accompanying computational burden. Furthermore, on the micro and nano scale, a variety of damping mechanisms, each with their own unique set of physics, may be present. The research presented in this work is organized in two parts. The first part presents methods for deriving knowledge from models and their solutions. Here, the developed methods perform approximate yet highly efficient analysis on the matrices and solution vectors of finite element models. In this work, methods utilizing the Neumann series approximation are presented. These methods efficiently predict how the response of a structure depends on its damping or any other input model parameter. Additionally, a method for analyzing the spatial dependence of damping with the use of loss factor images is presented. Research presented in the second part derives knowledge solely from solutions of models. In this part, it is assumed that the matrices of the models are not available, and therefore analysis is restricted to the solution itself. Here, research is focused on the analyses of structures on the micro and nano scale. Specifically, micro and nano beams surrounded by a viscous compressible fluid are analyzed. The dynamic responses of the structure and the surrounding fluid are analyzed to determine the prominent damping mechanisms. Here, results from 2--Dimensional analytical models and 3--Dimensional finite element models are complemented by experimental measurements to analyze damping due to viscous dissipation and acoustic radiation. 2021-05-25T15:33:10Z 2021-05-25T15:33:10Z 2021 2021-05-15T07:03:11Z Thesis/Dissertation https://hdl.handle.net/2144/42595 0000-0002-8789-1058 en_US
collection NDLTD
language en_US
sources NDLTD
topic Acoustics
Finite element analysis
Fluid structure interaction
Nano electro mechanical systems
Neumann series
Vibrations
spellingShingle Acoustics
Finite element analysis
Fluid structure interaction
Nano electro mechanical systems
Neumann series
Vibrations
Liem, Alyssa Tomoko
Analyzing damping in large models of complex dynamic systems
description From the nano scale to the macro scale, large models are used to simulate and predict the responses of dynamic systems. The construction and evaluation of such models, often in the form of finite element models, require tremendous computational resources and time. Due to this large computational endeavor, it is paramount to learn as much as possible from the models and their solutions. In this work, analyses and methods for efficiently deriving significant knowledge of damped systems from models and their solutions are presented. Of primary interest to this work is the analysis of damped structures. Damping, the means by which energy is dissipated, often adds an additional layer of complexity to finite element models and any subsequent analyses. This added complexity is due to the relative complexity of many damping models and their accompanying computational burden. Furthermore, on the micro and nano scale, a variety of damping mechanisms, each with their own unique set of physics, may be present. The research presented in this work is organized in two parts. The first part presents methods for deriving knowledge from models and their solutions. Here, the developed methods perform approximate yet highly efficient analysis on the matrices and solution vectors of finite element models. In this work, methods utilizing the Neumann series approximation are presented. These methods efficiently predict how the response of a structure depends on its damping or any other input model parameter. Additionally, a method for analyzing the spatial dependence of damping with the use of loss factor images is presented. Research presented in the second part derives knowledge solely from solutions of models. In this part, it is assumed that the matrices of the models are not available, and therefore analysis is restricted to the solution itself. Here, research is focused on the analyses of structures on the micro and nano scale. Specifically, micro and nano beams surrounded by a viscous compressible fluid are analyzed. The dynamic responses of the structure and the surrounding fluid are analyzed to determine the prominent damping mechanisms. Here, results from 2--Dimensional analytical models and 3--Dimensional finite element models are complemented by experimental measurements to analyze damping due to viscous dissipation and acoustic radiation.
author2 McDaniel, James G.
author_facet McDaniel, James G.
Liem, Alyssa Tomoko
author Liem, Alyssa Tomoko
author_sort Liem, Alyssa Tomoko
title Analyzing damping in large models of complex dynamic systems
title_short Analyzing damping in large models of complex dynamic systems
title_full Analyzing damping in large models of complex dynamic systems
title_fullStr Analyzing damping in large models of complex dynamic systems
title_full_unstemmed Analyzing damping in large models of complex dynamic systems
title_sort analyzing damping in large models of complex dynamic systems
publishDate 2021
url https://hdl.handle.net/2144/42595
work_keys_str_mv AT liemalyssatomoko analyzingdampinginlargemodelsofcomplexdynamicsystems
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