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Slender marine structures such as deep-water riser systems are continuouslyexposed to currents leading to vortex-induced vibrations (VIV) of thestructure. This may result in amplified drag loads and fast accumulation offatigue damage. Consequently, accurate prediction of VIV responses is of greatimportance for the safe design and operation of marine risers. Model tests withelastic pipes have shown that VIV responses are influenced by many structuraland hydrodynamic parameters, which have not been fully modelled in presentfrequency domain VIV prediction tools. Traditionally, predictions have beencomputed using a single set of hydrodynamic parameters, often leading toinconsistent prediction accuracy when compared with observed field measurementsand experimental data. Hence, it is necessary to implement a high safety factorof 10 - 20 in the riser design, which increases development cost and adds extraconstraints in the field operation. One way to compensate for thesimplifications in the mathematical prediction model is to apply adaptiveparameters to describe different riser responses. The objective of this work isto demonstrate a new method to improve the prediction consistency and accuracyby applying adaptive hydrodynamic parameters. In the present work, a four-stepapproach has been proposed: First, the measured VIV response will be analysedto identify key parameters to represent the response characteristics. Theseparameters will be grouped using data clustering algorithms. Secondly, optimalhydrodynamic parameters will be identified for each data group by optimisationagainst measured data. Thirdly, the VIV response using the obtained parameterswill be calculated and the prediction accuracy evaluated. The correcthydrodynamic parameters to be used for new cases can be obtained from theclustering. This concept has been demonstrated with examples from experimentaldata.

Improved VIV response prediction using adaptive parameters and data clustering