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Analytical and experimental studies in the development of reduced-order computational models for nonlinear systems

Posted on:2007-02-22Degree:Ph.DType:Dissertation
University:University of Southern CaliforniaCandidate:Tasbihgoo, FarzadFull Text:PDF
GTID:1440390005968103Subject:Engineering
Abstract/Summary:
This research work reports on an integrated approach, involving carefully conducted experimental tests on reconfigurable apparatuses, that allows the study of a broad class of generic nonlinear phenomena (such as dead-space, dry friction, hysteresis, limited-slip, etc.), that generates high-quality experimental measurements, for the subsequent development of high-fidelity, nonlinear, reduced-order, mathematical models of different formats, that are useful for the monitoring, control, and simulation of realistic nonlinear structural systems.; Progresses achieved in this study includes: (1) design, fabrication, assembly, instrumentation, calibration, and use of reconfigurable one- and two-dimensional test apparatuses, that allow the convenient testing of many important classes of nonlinear phenomena, both stationary as well as nonstationary in nature; (2) evaluation and extension of some useful system identification tools, involving parametric methods, such as the adaptive least-squares with the Bouc-Wen hysteresis model, as well as nonparametric methods, such as neural networks and polynomial-basis models, for dealing with realistic situations involving challenging hysteretic phenomena; (3) development of on-line monitoring schemes for large-scale viscous dampers, based on the adaptive least-squares with a forgetting-factor, in conjunction with two models: (a) the simplified design model, and (b) a polynomial-basis model; and (4) development and implementation of a methodology for utilizing data-based models of discrete nonlinear "joint" elements to create efficient system-level finite-element representations of multi-dimensional structures incorporating complex nonlinear elements.
Keywords/Search Tags:Nonlinear, Models, Experimental, Development
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