THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS
Floating offshore structures, particularly floating oil production, storage and offloading systems (FPSOs) are still in great demand, both in small and large reservoirs, for deployment in deep water. The prediction of such vessels’ responses to her environmental loading over her lifetime is now often undertaken using response-based design methodology, although the approach is still in its early stages of development. Determining the vessel’s responses to hydrodynamic loads induced by long term sea environments is essential for implementing this approach effectively. However, it is often not practical to perform a complete simulation for every 3-hour period of environmental data being considered. Therefore, an Artificial Neural Networks (ANN) modelling technique has been developed for the prediction of FPSO’s responses to arbitrary wind, wave and current loads that alleviates this problem. Comparison of results obtained from a conventional mathematical model with those of the ANN-based technique for the case of a 200,000 tdw tanker demonstrates that the approach can successfully predict the vessel’s responses due to arbitrary loads.
Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data With Different Platform Loads
The current design process of mooring systems for Floating Production, Storage, and Offloading units (FPSOs) depends on the availability of the platform’s mathematical model and the accuracy of dynamic simulations. These simulations then provide the FPSO’s time series motion which is evaluated according to design constraints. This process can be time-consuming and present inaccurate results due to the mathematical model’s limitations and the overall complexity of the vessel’s dynamics. We propose a Neural Simulator, called NeuroSim, a set of data-based surrogate models with environmental data as input, each model specialized in predicting different motion statistics relevant to mooring system design: Maximum Roll, Platform Offset, and Fairlead Displacements. The surrogate models are trained by current, wind, and wave data given in 3 hours periods at a Brazilian Offshore Basin from 2003 to 2010, and the associated dynamic response of a spread-moored FPSO is obtained through time-domain simulations using the Dynasim software. Hyperparameter Optimization techniques are performed to obtain optimal Artificial Neural Network (ANN) models specialized in different platform drafts. Finally, the proposed models are shown to correctly capture platform dynamics, providing good results when compared to motion statistics obtained from Dynasim. We conclude that an ANN surrogate model can be trained directly on actual measured metocean conditions and corresponding FPSO motion statistics to provide increased accuracy and reduced computational time over traditional methods based on dynamic simulation. Moreover, the proposed architecture can be integrated into an automated learning framework: The data-based surrogate models can be continuously fine-tuned and updated with newly measured data, improving accuracy over time.
DYNAMIC RESPONSE OF MOORED SEMI-SUBMERSIBLE PLATFORMS TO NON- COLLINEAR WAVE, WIND AND CURRENT LOADING
The authors developed a non-linear time domain analysis procedure and calculation tools based on these procedures to predict the motion response of moored semisubmersibles under wave, wind and current loading. These tools have been validated with published results. In the first part of the paper a brief summary of the nonlinear analysis procedures is given, together with some results obtained from predictions and published experimental and theoretical results. In the second part of the paper the results of parametric studies investigating the effects of variations in external wave, wind and current force magnitudes and directions on the dynamic motion response of semisubmersible platforms are illustrated. These results show the sensitivity of certain types of response values as the external forces vary in magnitude and in direction. Such results provide essential information for probability based design procedures. In the last part of the paper, time domain models are used to simulate incidents which occurred in the North Sea in 1990, in which mooring lines of some semi-submersibles failed.