Application of Optimization Algorithm on Parameters of an Empirical VIV Prediction Tool

Abstract

Abstract Conventional vortex-induced vibration (VIV) prediction tools are semi-empirical, in other words, based on several empirical parameters extracted from model tests in laboratory. Generally, the lab tests are costly, include small scale test conditions and with a limited test matrix. The extracted empirical databases are not directly applicable to full-scale VIV predictions of various slender marine structures. Therefore, large safety factors have been used by industry for VIV prediction in the past decades. To reduce the uncertainty (e.g. over-conservatism) related to semi-empirical VIV prediction tools, the NLPQL algorithm for parameter optimization of a semi-empirical time-domain prediction tool has been investigated. This methodology was demonstrated on pure cross-flow VIV prediction in an earlier study. It was shown that by setting appropriate constraints and cost functions of the optimization algorithm, this method is feasible to improve the VIV prediction accuracy. In this study, the NLPQL optimization algorithm was applied for combined cross-flow and in-line VIV predictions using time domain numerical model. Selected cases from field measurements representing multi-fidelity data were used to validate and verify the method.