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.
Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artiﬁcial Neural Network
: The ﬁnite element method (FEM) is an essential method for predicting the response of the spar platform considering all nonlinear variables. Although FEM is an extremely laborious and time-consuming process for predicting platform responses using hydrodynamic loads, artiﬁcial neural networks (ANNs) can predict the response quickly, as required for platform management to either linger or stop the production of oil and gas. The application of ANN approaches to estimate the wave height and period from the expected wind forces is investigated in this paper. The ANN model can also predict the nonlinear responses of the spar platform subjected to the structural parameter as well as the wave height and wave period. The backpropagation technique depletes feed-forward neural networks, allowing the network to be trained. Following the formation of the neural network, rapid reactions from a freshly anticipated wind force are obtained. The results are validated via a comparison with results from a conventional ﬁnite element analysis. The ﬁndings demonstrate that the artiﬁcial neural network (ANN) technique is effective and is able to signiﬁcantly reduce the required time to make predictions when compared to the conventional FEM.
SIMULATIONS OF LOW FREQUENCY MOTIONS OF DYNAMICALLY POSITIONED OFFSHORE STRUCTURES
In 1981, in the Netherlands, the 'Stichting Coordinatie Maritiem Onderzoek' ordered the first part of a research program concerning thrusters and station keeping. This work has been continued in subsequent years covering such topics as thruster/thruster interaction, thruster/hull interaction, environmental forces and low frequency motions. This resulted in a time-domain simulation program, DPSIM. To validate this program, dynamic positioning model tests were performed for a number of conditions. The measured and calculated results show adequate agreement.