DeepOcean (Norway)

Abstract Lifting large objects through the splash zone may appear simple, but it involves complex physics. A simplified method is available but with inherent limitations. In this study, we compared the simplified method in DNVGL RP-N103 with a comprehensive OrcaFlex analysis for lifting a large suction anchor through the splash zone. The results indicate that the simplified method provides overly conservative weather limitations compared to the OrcaFlex analysis. Additionally, it is evident that the assumptions made in the simplified method are not appropriate for this lift due to significant pitching of the suction anchor. We compared a new automated web-based analysis system for offshore lifts with the traditional method. The automated system allows for easy, cost-effective, and fast analysis of lifting operations. It significantly improves the ability of analysis engineers to understand and tackle challenging lifting operations. By varying parameters, we optimized the crane-tip position and greatly expanded the weather limitations. Without a fast and reliable system, this optimization would have been difficult and time-consuming. The outcome is enhanced weather limitations and a more efficient operation, thanks to a better understanding of the factors influencing weather limitations.

Sizing of a new build FPSO hull is an iterative optimization process, where the main dimensions of the FPSO are varied until the optimal size is found, within a defined input domain. A wide range of criteria should be evaluated per size iteration, and thus the sizing process may be a very time consuming task. In order to speed up this process, it may be beneficial to adopt an automated iterative algorithm for performing the sizing of new build FPSOs for the Concept and Front-End Engineering Design (FEED) phase. The hull steel weight should be calculated for each size iteration, thus the hydrodynamic bending moment and shear force are needed. Obtaining the hydrodynamic sectional loads requires relatively time consuming calculations (compared to e.g. the required hydrostatic calculation), such as linear diffraction/radiation analysis and stochastic postprocessing. The required computational time of the automated sizing algorithm can be significantly reduced by calculating the hydrodynamic sectional loads by a simplified estimator, eliminating the need for e.g. diffraction/radiation analysis. Such an estimator may be obtained by the use of Artificial Neural Networks (ANNs). This paper presents how to estimate the hydrodynamic vertical bending moment and shear force for FPSOs, within acceptable accuracy to be used for initial sizing, using ANNs. The estimators, i.e. the ANNs, take known inputs such as the main dimensions, draft and pitch radius of gyration of the FPSO. The ANNs are trained on a database containing linear diffraction/radiation analyses for a variety of FPSO main dimensions, hull shapes and loading conditions. The database has been established by batch processing of the DNV-GL software HydroD [1], Wadam [2] and Postresp [3] through MATLAB [4]. This paper presents the methods used to obtain the results contained in the database, as well as training and performance of the ANNs.