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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Christiansen, Niels Hørbye Voie, Per Erlend Torbergsen Høgsberg, Jan Sødahl, Nils |
| Copyright Year | 2013 |
| Abstract | Dynamic analyses of mooring line systems are computationally expensive. Over the last decades an extensive variety of methods to reduce this computational cost have been suggested. One method that has shown promising preliminary results is a hybrid method which combines finite element analysis and artificial neural networks (ANN). The present study presents a novel strategy for selecting, arranging and normalizing training data for an ANN. With this approach one ANN can be trained to perform high speed dynamic response prediction for all fatigue relevant sea states and cover both wave frequency motion and slow drift motion. The method is tested on a mooring line system of a floating offshore platform. After training a full fatigue analysis is carried out. The results show that the ANN with high precision provides top tension force histories two orders of magnitude faster than a full dynamic analysis. |
| Sponsorship | Ocean, Offshore and Arctic Engineering Division |
| File Format | |
| ISBN | 9780791855317 |
| DOI | 10.1115/OMAE2013-10682 |
| Volume Number | Volume 1: Offshore Technology |
| Conference Proceedings | ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering |
| Language | English |
| Publisher Date | 2013-06-09 |
| Publisher Place | Nantes, France |
| Access Restriction | Subscribed |
| Subject Keyword | Dynamic response Seas Simulation Mooring Dynamic analysis Wave frequency Artificial neural networks Fatigue Tension Finite element analysis Fatigue analysis Offshore platforms |
| Content Type | Text |
| Resource Type | Article |
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