Several research efforts have been directed toward the development of models for response prediction of flexible risers. The main difficulties arise from the fact that the dynamic response of flexible risers involves a highly nonlinear behavior and a self-regulated process. This paper presents a quasi-steady approach for response prediction of oscillating flexible risers. Amplitude-dependent lift coefficients and an increased mean drag coefficient model during synchronization events are considered. Experimental validation of the proposed model is carried out using a 20-meter riser model excited by forced harmonic vibration at its top end. Large variations in the hydrodynamic force coefficients, a low mass-ratio value and synchronization events are the main features of the model presented in this paper. Experimental validation is provided for the asymmetric, transverse, diagonal and third vortex regimes.
A numerical scheme for dynamic response of deep-water risers is presented in this paper; its formulation is based on the Finite Element Method (FEM) and the quasi-steady model for prediction of the transverse forces. The increased mean drag coefficient during lock-in is also considered in the numerical scheme. The simulation results are compared to experimental data obtained from a 35-meter long flexible riser model. Good agreement is observed in these comparisons. The in-line response of the riser model is well represented by the numerical scheme and the transverse response is under-predicted as the oscillating frequency increases.
In this paper, a statistical pattern recognition method based on time series analysis is implemented in flexible risers. This method uses a combination of Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) prediction models. The flexible riser model used in this paper is experimentally validated employing a proposed numerical scheme for dynamic response of flexible risers. A modal-based damage detection approach is also implemented in the flexible riser model and its results are compared with the ones obtained from time series analysis. The numerical results show that the time series analysis presented in this paper is able to detect and locate structural deterioration related to fatigue damage in flexible risers. Finally, considering the case study results presented in this paper, the presented AR-ARX prediction model works better than the modal-based damage detection method.
In this paper, a statistical pattern recognition method based on time series analysis is implemented in flexible risers. This method uses a combination of Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) prediction models. The flexible riser model used in this paper is experimentally validated employing a proposed numerical scheme for dynamic response of flexible risers. A modal-based damage detection approach is also implemented in the flexible riser model and its results are compared with the ones obtained from time series analysis. The numerical results show that the time series analysis presented in this paper is able to detect and locate structural deterioration related to fatigue damage in flexible risers. Finally, considering the case study results presented in this paper, the presented AR-ARX prediction model works better than the modal-based damage detection method.
(2-15, Natushima-cho, Yokosuka 237-0061, Japan) Flexible risers are becoming increasingly important for deep-sea oil production. In addition, current attempts directed towards global warming mitigation target the use of flexible risers for carbon dioxide injection in deep waters. The main difficulties arise from the highly nonlinear behavior and self-regulated nature of flexible risers in marine environments. This paper presents the experimental validation of a response prediction model in the quasi-steady regime. A 20-meter riser model, pinned at its both ends with a constant tension force at its top end, is sinusoidally excited at values of Keulegan-Carpenter Number located in the quasi-steady regime. Good agreement in amplitude response is obtained between experimental data and simulation results.
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