Stress Wave Propagation Analysis on Vortex-Induced Vibration of Marine Risers

To analyze the stress wave propagation associated with the vortex-induced vibration（VIV） of a marine riser, this paper employed a multi-signal complex exponential method. This method is an extension of the classical Prony＇s method which decomposes a complicated signal into a number of complex exponential components. Because the proposed method processes multiple signals simultaneously, it can estimate the “global” dominating frequencies（poles） shared by those signals.The complex amplitude（residues） corresponding to the estimated frequencies for those signals is also obtained in the process. As the signals were collected at different locations along the axial direction of a marine riser, the phenomena of the stress wave propagation could be analyzed through the obtained residues of those signals. The Norwegian Deepwater Program（NDP） high mode test data were utilized in the numerical studies, including data sets in both the in-line（IL） and cross-flow（CF） directions. It was found that the most dominant component in the IL direction has its stress wave propagation along the riser being dominated by a standing wave, while that in the CF direction dominated by a traveling wave.

Modal Parameters Identification of A Real Offshore Platform From the Response Excited by Natural Ice Loading

This paper investigates the possibility of utilizing response from natural ice loading for modal parameter identification of real offshore platforms.The test platform is the JZ20-2 MUQ jacket platform located in the Liaodong Bay,China.A field experiment is carried out in winter season,as the platform is excited by floating ices.The feasibility is demonstrated by the acceleration response of two different segments.By the SSI-data method,the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments.The estimated information from both segments is almost identical,which demonstrates that the modal identification is trustworthy.Furthermore,by taking the Jacket platform as a benchmark,the numerical performance of five popular time-domain EMA methods is systematically compared from different viewpoints.The comparisons are categorized as:(1)stochastic methods versus deterministic methods;(2)high-order methods versus low-order methods;(3)data-driven versus covariance-driven stochastic subspace identification methods.

Model updating based on incomplete modal data

The objective of model updating is to improve the accuracy of a dynamic model based on the correlation between the measured data and the analytical (finite element) model. In this paper, we intend to update the mass and stiffness matrices of an analytical model when only modal frequencies or spatially incomplete modal data are available. While the proposed method is systematic in nature, it also preserves the initial configuration of the analytical model, and physical equality and/or inequality constraints can be easily incorporated into the solution procedure. Numerical examples associated with a simple 5-DoF (degree of freedom) mass-spring system are chosen to illustrate the detailed procedure and the effectiveness of the proposed method. Numerical scenarios ranging from the updating for stiffness terms only to that for all mass and stiffness terms based on various kinds of incomplete modal data are studied. The obtained model updating results are excellent when the measured modal data are noise-free. Uncertainty studies are also conducted based on simulations of corrupted modal data, but a thorough theoretical analysis of the noise effect on the proposed method is still needed.

Improved polyreference time domain method for modal identification using local or global noise removal techniques

Modal identification involves estimating the modal parameters, such as modal frequencies, damping ratios, and mode shapes, of a structural system from measured data. Under the condition that noisy impulse response signals associated with multiple input and output locations have been measured, the primary objective of this study is to apply the local or global noise removal technique for improving the modal identification based on the polyreference time domain (PTD) method. While the traditional PTD method improves modal parameter estimation by over-specifying the computational model order to absorb noise, this paper proposes an approach using the actual system order as the computational model order and rejecting much noise prior to performing modal parameter estimation algorithms. Two noise removal approaches are investigated: a "local" approach which removes noise from one signal at a time, and a "global" approach which removes the noise of multiple measured signals simultaneously. The numerical investigation in this article is based on experimental measurements from two test setups: a cantilever beam with 3 inputs and 10 outputs, and a hanged plate with 4 inputs and 32 outputs. This paper demonstrates that the proposed noise-rejection method outperforms the traditional noise-absorption PTD method in several crucial aspects.