FIRSTLING-DIGIMAR, a Pilot Scale Digital Twin of a Marine Riser for Field Use-Reference-Cited by-同舟云学术

FIRSTLING-DIGIMAR, a Pilot Scale Digital Twin of a Marine Riser for Field Use

Published:2024-04-29 Issue: Volume: Page:
ISSN:
Container-title:Day 4 Thu, May 09, 2024
language:
Short-container-title:

Author:

Kharami E.¹,Mentzelopoulos A.²,Prele E.²,Shukla K.¹,Wang Z.²,Bray B.²,Cao Q.¹,Singh A.¹,Fan D.³,Sapsis T.²,Triantafyllou M. S.²,Karniadakis G. E.¹

Affiliation:

1. Division of Applied Math, Brown University, Providence, RI, USA

2. MIT Sea Grant, MIT, Boston, MA, USA

3. Westlake University, Westlake, China

Abstract

Abstract We developed and tested in real time FIRSTLING-DIGIMAR, a pilot scale digital twin of a marine riser driven by sparse field measurements. The FIRSTLING consists of hardware and software that includes a reduced order model developed through multi-fidelity methods combining massive field and experimental data, with low- and high-fidelity models, it is driven by real time measurements of a few sensors along the riser, and provides real-time fatigue life predictions with uncertainty quantification.

Publisher

OTC

Link

https://onepetro.org/OTCONF/proceedings-pdf/doi/10.4043/35371-MS/3402407/otc-35371-ms.pdf

Reference34 articles.

1. On fatigue damage of long flexible cylinders due to the higher harmonic force components and chaotic vortex-induced vibrations;Zheng;Ocean Engineering,2014

2. Vortex-Induced Vibrations of Slender Structures in Shear Flow;Triantafyllou,2016

3. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;Journal of Computational Physics,2019

4. E. Kharazmi , Z.Wang, D.Fan, S.Rudy, T.Sapsis, M. S.Triantafyllou, and G. E.Karniadakis. "From data to assessment models, demonstrated through a digital twin of marine risers." In Offshore Technology Conference, p. D031S035R003. OTC, 2021.

5. Inferring Vortex Induced Vibrations of Flexible Cylinders Using Physics-Informed Neural Networks;Kharazmi;Journal of Fluids and Structures,2020