A novel design approach for estimation of extreme responses of a subsea shuttle tanker hovering in ocean current considering aft thruster failure

Abstract

The subsea shuttle tanker (SST) is an innovative 33 600-ton underwater cargo tanker designed to transport CO2 to marginal fields. During offloading, the SST will approach and hover in the vicinity of the subsea well. A remotely operated vehicle (ROV) will carry and connect a flexible flowline from the subsea well to the SST. CO2 is then offloaded via this flexible flowline. The offloading process takes four hours. During this time, the SST is subjected to time-varying current load effects, and it will dynamically keep its position using its ballast tanks, propeller, and thrusters. Knowing the extreme positional responses is essential. The extreme heave motion determines the maximum depth and corresponding the maximum hydrostatic loading; hydrostatic loading is a dominating load and drives the collapse design of the SST hull. Further, the extreme surge motion determines the flowline length required to avoid snap loads. In this paper, the extreme positional responses of the SST when the aft thruster fails during offloading is investigated for mean current velocities of 0.5 and 1.0 m/s using the averaged conditional exceedance rate (ACER) method. The empirical data is generated using time-domain simulations with a 2D planar Simulink model. The proposed methodology provides an accurate bivariate extreme value prediction, utilizing all available data efficiently. In this study, the estimated vessel response 5 years return level values and contours, obtained by ACER 1D and 2D methods. It is shown that the extreme responses with return periods of 5 years are, in general, higher than the maxima of the 4 h response by a factor of two. Further, it is seen that the response at the aft where the thruster fails is 1.3–2.6 times larger than the response at the fore of the SST. Based on the overall performance of the proposed method, it was concluded that the ACER 1D and 2D methods could provide robust and accurate both univariate and bivariate predictions based on accurate dynamic vessel motion numerical simulations.