Artificial Neural Networks Applied to Flexible Pipes Fatigue Calculations

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.