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Artificial Neural Networks Applied to Flexible Pipes Fatigue Calculations

[+] Author Affiliations
Victor Chaves

Technip, Rio de Janeiro, Brazil

Luis V. S. Sagrilo, Vinícius Ribeiro Machado da Silva

Federal University of Rio de Janeiro/COPPE, Rio de Janeiro, Brazil

Mario Alfredo Vignoles

Consultant - UFRJ, Rio de Janeiro, Brazil

Paper No. OMAE2015-41650, pp. V05BT04A022; 9 pages
doi:10.1115/OMAE2015-41650
From:
  • ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 5B: Pipeline and Riser Technology
  • St. John’s, Newfoundland, Canada, May 31–June 5, 2015
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5652-9
  • Copyright © 2015 by ASME

abstract

Flexible pipes play an important role in offshore oil exploitation activities nowadays. However, time-domain flexible pipe irregular wave dynamic analyses are extremely computational expensive. One of the various existing methods to reduce computational costs in dynamic analyses is the hybrid methodology that combines dynamic Finite Element Analyses (FEA) and Artificial Neural Networks (ANN). This paper presents a novel application of this methodology for flexible pipes fatigue calculations. In order to decrease computational cost involved in these analyses the proposed hybrid methodology aims to predict tension and curvatures in the bend stiffener region. Firstly using short FEA simulations to train the ANN, and then using only the ANN and the prescribed floater motions to get the rest of the response histories. With the predicted tension and curvatures, a local analysis is applied to calculate stresses in tensile armour wires and the corresponding fatigue lives. To evaluate the optimal ANN a sensibility study is developed for some key parameters as: training time length, neurons on hidden layer and delay length. A full FEA is also performed in order to evaluate the accuracy of the proposed hybrid methodology, comparing both full FEA flexible pipe fatigue results and those obtained using the hybrid methodology.

Copyright © 2015 by ASME

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