A simplified method to predict fatigue damage of TTR subjected to short-term VIV using artificial neural network. (December 2018)

• Record Type: Journal Article
• Authors: Wong, Eileen Wee Chin; Kim, Do Kyun
• Is Part Of: Advances in engineering software. Volume 126(2018)
• Page Start: 100
• Page End: 109
• Publication Date: 2018-12
• Subjects: Top-tensioned riser -- Vortex-induced vibration -- Fatigue damage -- Riser -- Artificial neural network -- Current; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2018.09.011
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: An ANN model for predicting short-term VIV fatigue damage of TTR was developed and trained successfully using generated 21, 532 datasets. The proposed ANN technique was verified by test cases of current. It can serve as an alternative approach for VIV fatigue damage assessment at early design stage of TTR. Abstract: 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. Graphical abstract: