Structural system identification is traditionally related to the estimation of modal parameters (natural frequencies, modal damping ratios, and mode shapes). Various well known modal methods often fail to extract these parameters for heavily damped structures with high modal densities due to the high coupling between densely packed adjacent modes. The recent development of the scanning laser Doppler vibrometer (SLDV) technology that provides efficient and massive dynamic data acquisition with high spatial density makes the new non-modal system identification techniques feasible. The proposed non-modal system identification method is based on the singular value decomposition (SVD) of the spatial mobility matrices that are acquired by the SLDV technique. Data reduction, filtering, periodization, and remapping techniques are applied to the measured data in the spatial domain. Linear and polynomial singular vector interpolation and subspace rotation techniques are applied in the frequency domain for the prediction of the spatial mobility over the frequency range of interest. This non-modal method uses measured frequency response data directly and involves neither curve fitting nor modal parameter extraction. The proposed non-modal technique was applied to a commercial business jet airplane fuselage. The measured mobility data of the fuselage were reduced to a much smaller and very efficient data set that could be easily managed, stored, and retrieved for the reconstruction and/or prediction the dynamic responses of the fuselage in both frequency and spatial domains
Ph. D.