TY  - JOUR
Y1  - 2018///
VL  - 126
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055903788&doi=10.1016%2fj.advengsoft.2018.09.011&partnerID=40&md5=1de8f0f2ff93c6a920c1c0bdf36e3c9e
A1  - Wong, E.W.C.
A1  - Kim, D.K.
JF  - Advances in Engineering Software
KW  - Buoyancy; Electric currents; Fluid structure interaction; Forecasting; Marine risers; Modal analysis; Neural networks; Vibrations (mechanical); Vortex flow
KW  -  Critical component; Damage prediction models; Early design stages; Riser; Semi-empirical methods; Simplified method; Top tensioned risers; Vortex induced vibration
KW  -  Fatigue damage
ID  - scholars9535
N2  - Marine riser is the critical component transporting hydrocarbon and fluid from well to the platform and vice versa. Riser experiences vortex-induced vibration (VIV) caused by current, leading to fatigue damage. Estimation of VIV fatigue damage is essential in designing feasible and operable riser. A simplified approach for predicting fatigue damage is required to reduce the computation time to analyse the fatigue damage. This study aims to propose a simplified approach to predict VIV fatigue damage of top tensioned riser (TTR) using artificial neural network (ANN). A total of 21,532 riser model was generated with different combination of six main input parameters: riser outer diameter, wall thickness, top tension, water depth, surface and bottom current velocity. The modal analysis was performed using OrcaFlex and VIV fatigue damage of the riser was computed using SHEAR7. The six input parameters and corresponding fatigue damage results made up the database for training a 2-layer neural network. Weight and bias values acquired from the training of ANN were used to develop the VIV fatigue damage prediction model of the riser. The hyperparameters of the ANN model were tuned to optimize performance of the model. The results showed the final ANN model predict fatigue damage well with shorter time compared to conventional semi-empirical method. Hence, the proposed approach is suitable to be used for prediction of VIV fatigue damage of TTR at early design stage of TTR. © 2018 Elsevier Ltd
SN  - 09659978
PB  - Elsevier Ltd
EP  - 109
AV  - none
SP  - 100
TI  - A simplified method to predict fatigue damage of TTR subjected to short-term VIV using artificial neural network
N1  - cited By 63
ER  -