Fatigue damage prediction of top tensioned riser subjected to vortex-induced vibrations using artificial neural networks

• Published in: Ocean engineering Vol. 268; p. 113393
• Main Authors: Sivaprasad, Harishankar, Lekkala, MalaKonda Reddy, Latheef, Mohamed, Seo, Junhyeon, Yoo, Kwangkyu, Jin, Chungkuk, Kim, Do Kyun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2023
• Subjects: Artificial neural networks; Fatigue damage; Optimisation; Top-tensioned riser; Vortex induced vibration; Top-tensioned riser; Vortex induced vibration; Optimisation; Fatigue damage; Artificial neural networks
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2022.113393

Several marine structures are subjected to critical loads resulting in their fatigue failure. Among these structures, the riser is an essential component that transports the hydrocarbons and fluids from the platform to the subsea well and vice versa. Risers are subjected to vortex-induced vibrations (VIV) caused by current, leading to fatigue damage (Lekkala et al., 2022a; 2022b). Therefore, developing a simplified and valid approach to estimate fatigue behaviour and damage is necessary. Though semi-empirical methods are available for fatigue analysis, their deployments demand immense computational time and costs. In this study, we propose a novel framework based on data-driven analysis utilising artificial neural networks (ANN) to predict the VIV-induced fatigue damage of the top-tensioned riser (TTR). To build a reliable database, OrcaFlex, and SHEAR7, are used for the riser's modal and fatigue damage analysis due to VIV, respectively. A total of 24,686 riser models based on combinations of eight input parameters (outer diameter of riser, wall thickness of riser, water depth, surface, and bottom velocities, top tension, buoyancy modules thickness, and coverage ratio) and corresponding output as fatigue damage results are used for training a multi-layered ANN. The fatigue damage prediction surrogate model has been achieved by tuning the hyperparameters to improve the model's prediction accuracy. The findings of the ANN-based riser fatigue damage approach confirm that the ANN model exhibits a mean absolute percentage error (MAPE) between the predictions and truths from SHEAR7 is 9.29%. Also, the proposed framework can reduce computational time and cost compared to conventional semi-empirical methods. Finally, detailed parametric investigations are carried out using an optimised ANN model to evaluate the accuracy with the semi-empirical tool. These metrics suggest that the proposed ANN method could be used to determine the fatigue damage of risers in real-world field conditions. •A simplified model is developed to predict the fatigue damage of the riser subjected to VIV.•By using the automation function, a large database of 24,686 datasets of riser models are generated.•The training, cross-validation and testing of the ANN model are successful by using the generated database.•The optimised ANN model prediction is compared and verified with new datasets of input parameters.•Parametric studies are performed to verify the prediction accuracy of the optimised model with individual input parameters.