Response Prediction of Offshore Floating Structure Using Artificial Neural Network
Abstract:For deep-water oil and gas exploration, spar platform is considered to be the most economic and suitable floating offshore structure. Analysis of spar platform is complex due to various nonlinearities such as geometric, variable submergence, varying pretention, etc. The Finite Element Method (FEM) is an important technique to deal with this type of analysis. However, FEM is computationally very expensive and highly time-consuming process. Artificial Neural Network (ANNs) can provide meaningful solutions and can process information in extremely rapid mode ensuring high accuracy of prediction. This paper presents dynamic response prediction of spar mooring line using ANN. FEM-based time domain response of spar platform such as surge, heave and pitch is trained by ANN. Mooring line top tension is predicted after 7200 sec (2 hours) of wave loading. The response obtained using ANN is validated by conventional FEM analysis. Results show that ANN approach is found to be very efficient and it significantly reduces the time for predicting long response time histories. Thus ANN approach is recommended for efficient designing of floating structures.
Document Type: Research Article
Publication date: July 1, 2012
More about this publication?
- ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites