Nonlinear multi-mode interactions in subsea risers undergoing vortex-induced vibrations

This paper investigates nonlinear multi-mode interactions in subsea risers undergoing vortex-induced vibrations based on a computationally efficient reduced-order fluid-structure interaction model. Cross-flow responses as a result of a steady uniform current are considered. The geometrically nonlinear equations of riser motion are coupled with nonlinear wake oscillators which have been modified to capture the effect of initial curvatures of curved cylinder and to approximate the space-time varying hydrodynamic lift forces. The main objectives are to provide new insights into the vortex-induced vibration characteristics of risers under external and internal resonances and to distinguish nonlinear dynamic behaviors between curved catenary and straight toptensioned risers. The analyses of multi-mode contributions, lock-in regimes, response amplitudes, resonant nonlinear modes and curvatures are carried out and several interesting aspects are highlighted

Vortex-induced vibration of catenary riser: reduced-order modeling and lock-in analysis using wake oscillator

A new reduced-order model capable of analyzing the vortex-induced vibration of catenary riser in the ocean current has been developed. This semi analytical-numerical approach is versatile and allows for a significant reduction in computational effort for the analysis of fluid-riser interactions. The incoming current flow is assumed to be steady, uniform, unidirectional and perpendicular to the riser plane of initial equilibrium curvatures

Automatic detection of change in address blocks for reply forms processing

In this paper, an automatic method to detect the presence of on-line erasures/scribbles/corrections/over-writing in the address block of various types of subscription and utility payment forms is presented. The proposed approach employs bottom-up segmentation of the address block. Heuristic rules based on structural features are used to automate the detection process. The algorithm is applied on a large dataset of 5,780 real world document forms of 200 dots per inch resolution. The proposed algorithm performs well with an average processing time of 108 milliseconds per document with a detection accuracy of 98.96%