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Stochastic Response Determination an...

Brudastova, Olga.

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Stochastic Response Determination and Spectral Identification of Complex Dynamic Structural Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic Response Determination and Spectral Identification of Complex Dynamic Structural Systems./
作者:	Brudastova, Olga.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	113 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
Contained By:	Dissertation Abstracts International80-03B(E).
標題:	Civil engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10936478
ISBN:	9780438488632

Stochastic Response Determination and Spectral Identification of Complex Dynamic Structural Systems.
Brudastova, Olga.

Stochastic Response Determination and Spectral Identification of Complex Dynamic Structural Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 113 p.

Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2018.

Uncertainty propagation in engineering mechanics and dynamics is a highly challenging problem that requires development of analytical/numerical techniques for determining the stochastic response of complex engineering systems. In this regard, although Monte Carlo simulation (MCS) has been the most versatile technique for addressing the above problem, it can become computationally daunting when faced with high-dimensional systems or with computing very low probability events. Thus, there is a demand for pursuing more computationally efficient methodologies. Further, most structural systems are likely to exhibit nonlinear and time-varying behavior when subjected to extreme events such as severe earthquake, wind and sea wave excitations. In such cases, a reliable identification approach is behavior and for assessing its reliability.

ISBN: 9780438488632Subjects--Topical Terms:

860360
Civil engineering.

Stochastic Response Determination and Spectral Identification of Complex Dynamic Structural Systems.
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245 1 0 $a Stochastic Response Determination and Spectral Identification of Complex Dynamic Structural Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 113 p.
500 $a Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
500 $a Adviser: Ioannis A. Kougioumtzoglou.
502 $a Thesis (Ph.D.)--Columbia University, 2018.
520 $a Uncertainty propagation in engineering mechanics and dynamics is a highly challenging problem that requires development of analytical/numerical techniques for determining the stochastic response of complex engineering systems. In this regard, although Monte Carlo simulation (MCS) has been the most versatile technique for addressing the above problem, it can become computationally daunting when faced with high-dimensional systems or with computing very low probability events. Thus, there is a demand for pursuing more computationally efficient methodologies. Further, most structural systems are likely to exhibit nonlinear and time-varying behavior when subjected to extreme events such as severe earthquake, wind and sea wave excitations. In such cases, a reliable identification approach is behavior and for assessing its reliability.
520 $a Current work addresses two research themes in the field of stochastic engineering dynamics related to the above challenges.
520 $a In the first part of the dissertation, the recently developedWiener Path Integral (WPI) technique for determining the joint response probability density function (PDF) of nonlinear systems subject to Gaussian white noise excitation is generalized herein to account for non-white, non-Gaussian, and non-stationary excitation processes. Specifically, modeling the excitation process as the output of a filter equation with Gaussian white noise as its input, it is possible to define an augmented response vector process to be considered in the WPI solution technique. A significant advantage relates to the fact that the technique is still applicable even for arbitrary excitation power spectrum forms. In such cases, it is shown that the use of a filter approximation facilitates the implementation of the WPI technique in a straightforward manner, without compromising its accuracy necessarily. Further, in addition to dynamical systems subject to stochastic excitation, the technique can also account for a special class of engineering mechanics problems where the media properties are modeled as non-Gaussian and non-homogeneous stochastic fields. Several numerical examples pertaining to both single- and multi-degree-of freedom systems are considered, including a marine structural system exposed to flow-induced non-white excitation, as well as a beam with a non-Gaussian and non-homogeneous Young's modulus. Comparisons with MCS data demonstrate the accuracy of the technique.
520 $a In the second part of the dissertation, a novel multiple-input/single-output (MISO) system identification technique is developed for parameter identification of nonlinear time-variant multi-degree-of-freedom oscillators with fractional derivative terms subject to incomplete non-stationary data. The technique utilizes a representation of the nonlinear restoring forces as a set of parallel linear subsystems. In this regard, the oscillator is transformed into an equivalent MISO system in the wavelet domain. Next, a recently developed L1-norm minimization procedure based on compressive sampling theory is applied for determining the wavelet coefficients of the available incomplete non-stationary input-output (excitation-response) data. Finally, these wavelet coefficients are utilized to determine appropriately defined time- and frequency-dependent wavelet based frequency response functions and related oscillator parameters. A nonlinear time-variant system with fractional derivative elements is used as a numerical example to demonstrate the reliability of the technique even in cases of noise corrupted and incomplete data.
590 $a School code: 0054.
650 4 $a Civil engineering. $3 860360
650 4 $a Applied mathematics. $3 2122814
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