Optimized Mooring Line Simulation Using a Hybrid Method Time Domain Scheme
Dynamic analyses of slender marine structures are computationally expensive. Recently it has been shown how a hybrid method which combines FEM models and artificial neural networks (ANN) can be used to reduce the computation time spend on the time domain simulations associated with fatigue analysis...
| Published in: | Volume 1B: Offshore Technology |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
The American Society of Mechanical Engineers (ASME)
2014
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| Subjects: | |
| Online Access: | https://orbit.dtu.dk/en/publications/1f6611ec-93e5-4f94-b429-ae6d6c6b02ff https://doi.org/10.1115/omae2014-23939 |
| Summary: | Dynamic analyses of slender marine structures are computationally expensive. Recently it has been shown how a hybrid method which combines FEM models and artificial neural networks (ANN) can be used to reduce the computation time spend on the time domain simulations associated with fatigue analysis of mooring lines by two orders of magnitude. The present study shows how an ANN trained to perform nonlinear dynamic response simulation can be optimized using a method known as optimal brain damage (OBD) and thereby be used to rank the importance of all analysis input. Both the training and the optimization of the ANN are based on one short time domain simulation sequence generated by a FEM model of the structure. This means that it is possible to evaluate the importance of input parameters based on this single simulation only. The method is tested on a numerical model of mooring lines on a floating offshore installation. It is shown that it is possible to estimate the cost of ignoring one or more input variables in an analysis. |
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