From Data to Assessment Models, Demonstrated through a Digital Twin of Marine Risers
Author(s)Kharazmi, Ehsan; Wang, Zhicheng; Fan, Dixia; Rudy, Samuel; Sapsis, Themis; Triantafyllou, Michael S; Karniadakis, George E; ... Show more Show less
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<jats:title>Abstract</jats:title> <jats:p>Assessing the fatigue damage in marine risers due to 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 response of such complex systems is usually unavailable despite massive experimental data and computation results. These algorithms can use multi-fidelity data sets from multiple sources, including real-time sensor data from the field, systematic experimental data, and simulation data. Here we develop a three-pronged approach to demonstrate how tools in machine learning are employed to develop data-driven models that can be used for accurate and efficient fatigue damage predictions for marine risers subject to VIV.</jats:p>
Day 3 Wed, August 18, 2021
Society of Petroleum Engineers (SPE)
Kharazmi, Ehsan, Wang, Zhicheng, Fan, Dixia, Rudy, Samuel, Sapsis, Themis et al. 2021. "From Data to Assessment Models, Demonstrated through a Digital Twin of Marine Risers." Day 3 Wed, August 18, 2021.
Author's final manuscript