VIV Fracture Investigation into 3D Marine Riser with a Circumferential Outside Surface Crack

Vortex-induced vibration (VIV) is one of the most common dynamic mechanisms that cause damage to marine risers. Hamilton’s variational principle is used to establish a vortex-induced vibration (VIV) model of a flexible riser in which the wake oscillator model is used to simulate cross-flow (CF) and inline flow (IL) vortex-induced forces and their coupling, taking into account the effect of the top tension and internal flow in the riser. The VIV model is solved by combining the Newmark-β and Runge–Kutta methods and verified with experimental data from the literature. Combining Option 1 and Option 2 failure assessment diagrams (FADs) in the BS7910 standard, a fracture failure assessment model for a marine riser with circumferential semielliptical outside surface cracks is established. Using the VIV model and FAD failure assessment chart, the effects of riser length, inside/outside flows, and top tension on the VIV response and safety assessment of marine risers with outside surface cracks are investigated. It is shown that increasing the top tension can inhibit the lateral displacement amplitude and bending stress in a riser, but excessive top tension can increase the axial stress in the riser, which counteracts the decrease in the bending stress, so that the effect of top tension on crack safety is not significant. The increasing outside flow velocity significantly increases the lateral vibration amplitude and bending stress in the riser and reduces the crack safety. When other parameters remain unchanged, increasing riser length has no significant effect on the vibration amplitude of the lower part of the riser.

Digital-Twin Approach Predicts Fatigue Damage of Marine Risers

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30985, “From Data to Assessment Models, Demonstrated Through a Digital Twin of Marine Risers,” by Ehsan Kharazmi and Zhicheng Wang, Brown University, and Dixia Fan, SPE, Massachusetts Institute of Technology, et al., prepared for the 2021 Offshore Technology Conference, Houston, 16–19 August. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. 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. The digital twin uses various sources of training data, including field data, experimental data, computational-fluid-dynamics simulations, extracted databases, semiempirical codes, and existing knowledge of underlying physical models. The authors also show that a well-trained digital twin can use the streaming data from a few field sensors efficiently to provide an accurate reconstruction of motion and to provide fatigue-damage prediction. Several machine-learning algorithms have been developed in the literature to predict the life span of the structure through the changes in parameters. To the best of the authors’ knowledge, most existing methods are developed as black boxes that return parameters by only feeding experimental data and therefore are ignorant of the underlying physics. In the first of three approaches, the authors enhance the capabilities of semiempirical codes by developing efficient databases through active learning. In the second approach, the LSTM-ModNet framework is applied to reconstruct and analyze the entire motion of a riser in deep water from sensor measurements through modal decomposition in space and the sequence-learning capability of recurrent neural networks in time. The formulation described in the paper provides a tool that efficiently combines different types of sensor measurements, such as strain and acceleration. In the third approach, a higher level of abstraction is introduced and the nonlinear operator that maps the inflow current velocity to the root-mean-square function of the riser response is approximated. In particular, the newly developed neural network DeepONet is used as a black box to learn the mapping between the input parameters (the inflow velocity, riser bending stiffness, and tension as a function of water depth) to the output parameters (strain, amplitude, and exciting frequencies as a function of water depth). In these approaches, data from the high-mode VIV test is used to train the networks.

From Data to Assessment Models, Demonstrated through a Digital Twin of Marine Risers

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.

Mitigation of Vortex-Induced Vibration of Cylinders Using Cactus-Shaped Cross Sections in Subcritical Flow

Flexible cylinders, such as marine risers, often experience sustained vortex-induced vibrations (VIVs). Installing helical strakes on a riser is the most widely used technique to mitigate VIVs. This study was inspired by the giant Saguaro Cacti which can withstand strong wind with a shallow root system. In this study, numerical simulations of flow past a stationary cylinder of a cactus-shaped cross-section in a two-dimensional flow field at a subcritical Reynolds number of 3900 were performed. Results show that cylinders of a cactus-shaped cross-section have a lower lift coefficient without increasing drag compared to those of a circular cylinder. VIV experiments on a single flexible pipe as well as on a set of two tandem-arranged flexible pipes were conducted at different reduced velocities to investigate the effects of the streamwise spacing and wake of the cactus-like body shape on VIV mitigation. Experimental results show that the cactus-like body shape can mitigate VIV responses of the cylinder at upstream position with no cost of increased drag; however, similar to helical strakes, the efficiency of VIV mitigation for the cylinder at downstream position is reduced. Although the cactus-like body shapes tested in this study were not optimized for oscillation suppression, still this study suggests that modification of the cross-sectional shape to a well-designed cactus-like shape has potentials to be used as an alternative technology to mitigate VIV of marine risers.