A Bayesian wave inference method accounting for nonlinearity related inaccuracies in motion RAOs
Jordi Mas-Soler, Alexandre N. Simos
Abstract
Motion based wave inference allows the estimation of the directional sea spectrum from the measured motions of a vessel. Solving the resulting inverse problem is challenging as it is often ill-posed; moreover, inaccuracies related to the linearity hypothesis behind estimated platform response functions (RAOs) may result in misleading estimations of the sea states. This work discusses how these inaccuracies affect the estimations obtained by means of a Bayesian motion based wave inference method (VMB). For this purpose, an heuristic correction, accounting for the non-linearity related inaccuracies of the estimated RAOs, is included in an expanded Bayesian inference approach. Then, the resulting inference model is verified by means of a comparison between the outputs of this novel approach and those obtained without accounting for nonlinearity related inaccuracies in the RAOs. This assessment has been carried out using the data measured through dedicated model scale experiments of the Equinor’s Åsgard-B semisubmersible oil processing platform. The results attested that improvements are obtained if the linearly related RAOs inaccuracies are taken into account in the VMB, especially for the sea conditions that excite the non-linear responses of the semisubmersible platform adopted as motion-based wave sensor.