Pipelines/flowlines/risers

Digital-Twin Approach Predicts Fatigue Damage of Marine Risers

The authors describe how tools in machine learning are used to develop data-driven models that can be used for accurate and efficient fatigue-damage prediction for marine risers.

 LSTM-ModNet structure.
Fig. 1—LSTM-ModNet structure. The first part learns the modal coordinates, while the second part reconstructs motion.

Assessing fatigue damage in marine risers caused by vortex-induced vibrations (VIV) serves as a comprehensive example of using machine-learning methods to derive assessment models of complex systems. A complete characterization of the response of such complex systems usually is unavailable despite massive experimental data and computation results. These algorithms can use multifidelity data sets from multiple sources. In the complete paper, the authors develop a three-pronged approach to demonstrate how tools in machine learning are used to develop data-driven models that can be used for accurate and efficient fatigue-damage predictions for marine risers subject to VIV.

Introduction

In this study, machine-learning tools are developed to construct a digital twin of a marine riser.

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