Time Series Based Model Updating in Nonlinear Systems using Singular Value Decomposition

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The problem considered is the use of time series data to do model updating in nonlinear structural systems for which the mathematical form of the system nonlinearities is known ahead of time. This work is a departure from most classical model updating work, which utilizes model data to update linear structural dynamics models. In the present application a singular value decomposition (SVD) of the measured data (e.g., m of the N coordinates are measured at n sampling times) is the basis of the updating. The SVD produces a representation of the data as a linear combination of the so-called principal ... continued below

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Hemez, F.M.; Beardsley, P.; Rhee, W. & Burton, T.D. June 27, 1999.

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The problem considered is the use of time series data to do model updating in nonlinear structural systems for which the mathematical form of the system nonlinearities is known ahead of time. This work is a departure from most classical model updating work, which utilizes model data to update linear structural dynamics models. In the present application a singular value decomposition (SVD) of the measured data (e.g., m of the N coordinates are measured at n sampling times) is the basis of the updating. The SVD produces a representation of the data as a linear combination of the so-called principal components, which are analogous to modal coordinate time histories in a linear system. The structural dynamics model parameters are updated by minimizing the differences in the SVD's of the experimental data and the model simulations. This method, proposed by Hasselman et al (IMAC 1998), has been applied to both simulated and actual experimental data for low degree of freedom spring-mass systems with cubic nonlinearity and light damping. The main results that will be presented are the following: (1) the SVD updating is robust in the presence of noise, (2) SVD based updating is effective for both linear and nonlinear systems, and (3) in some cases the nonlinear updating problem is actually easier to do than the linear problem because of the additional ''information'' contained in the harmonics produced by the nonlinearity. A possible limitation of the approach is the computing time needed to do the parameter optimization.

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OSTI as DE00757272

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  • ASME Mechanics and Materials Conference, Blacksburg, VA (US), 06/27/1999--06/30/1999

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  • Report No.: LA-UR-99-1284
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 757272
  • Archival Resource Key: ark:/67531/metadc703304

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  • June 27, 1999

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  • Sept. 12, 2015, 6:31 a.m.

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  • May 6, 2016, 3:04 p.m.

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Hemez, F.M.; Beardsley, P.; Rhee, W. & Burton, T.D. Time Series Based Model Updating in Nonlinear Systems using Singular Value Decomposition, article, June 27, 1999; New Mexico. (digital.library.unt.edu/ark:/67531/metadc703304/: accessed October 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.