From Data to Assessment Models, Demonstrated through a Digital Twin of Marine Risers
Name
2021-DigiMaR-OTC.pdf
Description
Accepted version
Size
1.14 MB
Format
Adobe PDF
Checksum (MD5)
165d52bc72b5240cae259d7b62725e31
Author(s)
Kharazmi, Ehsan
Wang, Zhicheng
Fan, Dixia
Rudy, Samuel
Sapsis, Themis
Triantafyllou, Michael S
Karniadakis, George E
Date Issued
2021
Journal
Day 3 Wed, August 18, 2021
Publisher
Society of Petroleum Engineers (SPE)
Citation
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.
Version
Author's final manuscript
Abstract
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.4043/30985-MS
