Browse by author
Lookup NU author(s): Said Mazaheri, Professor Martin Downie, Professor Ehsan Mesbahi, Professor Atilla Incecik
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
Author(s): Mazaheri S; Mesbahi E; Incecik A; Downie MJ
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 22nd International Conference on Offshore Mechanics and Arctic Engineering
Year of Conference: 2003
Pages: 275-284
Publisher: ASME
