Fatigue prediction and response monitoring on a FPSO

The fatigue life of structural members is an important aspect in the hull design of a Floating Production Storage and Offloading unit (FPSO). In contrast to the design for ultimate conditions, a fatigue assessment requires a thorough knowledge of the long-term environment to which the unit is exposed and how the unit is operated to estimate an accurate long-term load distribution. Furthermore, the fatigue life of a structural member is sensitive to the load to which it is exposed. This makes that an accurate long-term load distribution must be available to calculate an accurate fatigue life calculation. In this thesis, a model is investigated and developed to calculate the fatigue life of structural members in the side-shell, deck and bottom of a FPSO. The model is developed in the time-domain and validated against available model test data and full-scale measurement data recorded at FPSO Glas Dowr during operations at the Durward & Dauntless field (UKCS) and the Sable field (offshore South-Africa). It is demonstrated that the individual sea-states can be represented by short time-series, because many sea-states need to be taken into account in a fatigue assessment. This makes the fatigue calculations in time-domain feasible from a computational point of view. The sensitivity of the fatigue life in the model is investigated. It is found that the accuracy of the environmental data and loading condition data with which the model is fed, are equally important as the model itself. The vessel is designed with historical environmental data, but the vessel is likely to encounter different environmental conditions during its time at the field. Furthermore, the vessel will most likely be operated differently than assumed in the fatigue calculations. Both parameters have a significant impact on the fatigue life. Therefore, for offshore inspection, maintenance and repair (IMR) and for life-time extension (LTE) work at a yard for the next field, it is of interest to record the loads that the vessel encountered. In the second part of this thesis, data processing techniques are investigated to derive load accumulation data at specified locations from recorded sensor data. In particular, a processing technique is investigated that quantifies the long-term correlations between the statistics of different vessel responses. The basis of this technique is that all vessel responses are induced by the same wave conditions. In storm conditions all vessel responses will be large where in calm sea-states all vessel responses will be small. This suggests that a long-term relation between the statistics of different vessel responses exists. The developed method aims at describing this long-term relation. Potentially, this technique can lead to a practical and relatively inexpensive monitoring system, since this technique provides the possibility to derive load data in the structure from a limited number of conveniently placed sensors.