{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/37278"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/37278","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Parametric spatial modal analysis of beams","abstract":"Modal analysis is the experimental characterization of the dynanlical behavior of a structure. Recent advances in laser velocimetery have made available to the experimentalist a rich, new source of vibration data. Data can now be obtained from many different spatial locations on a structure. A method is presented to use this new data for the analysis of beams. Two approaches are investigated: minimum residual methods and boundary condition methods. The minimum residual approaches include autoregressive methods and non-linear least squares techniques. Significant contributions to sample rate considerations for parametric sinusoidal estimation resulted from this research. The minimum residual methods provide a good connection between the measured data and the fitted model. However, they do not yield a true modal decomposition of the spatial data. The boundary condition approach provides a complete modal model that is based on the spatial data and is completely compatible with classical beam theory. All theoretical constraints are included in the procedure. Monte Carlo investigations describe the statistical characteristics of the methods. Experiments using beams validate the methods presented. Advantages and limitations of each approach are discussed.","abstract_html":"Modal analysis is the experimental characterization of the dynanlical behavior of a structure. Recent advances in laser velocimetery have made available to the experimentalist a rich, new source of vibration data. Data can now be obtained from many different spatial locations on a structure. A method is presented to use this new data for the analysis of beams. Two approaches are investigated: minimum residual methods and boundary condition methods. The minimum residual approaches include autoregressive methods and non-linear least squares techniques. Significant contributions to sample rate considerations for parametric sinusoidal estimation resulted from this research. The minimum residual methods provide a good connection between the measured data and the fitted model. However, they do not yield a true modal decomposition of the spatial data. The boundary condition approach provides a complete modal model that is based on the spatial data and is completely compatible with classical beam theory. All theoretical constraints are included in the procedure. Monte Carlo investigations describe the statistical characteristics of the methods. Experiments using beams validate the methods presented. 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