In this paper, we conducted a selective review on the recent progress in physics insight and modeling of flexible cylinder flow-induced vibrations (FIVs). FIVs of circular cylinders include vortex-induced vibrations (VIVs) and wake-induced vibrations (WIVs), and they have been the center of the fluid-structure interaction (FSI) research in the past several decades due to the rich physics and the engineering significance. First, we summarized the new understanding of the structural response, hydrodynamics, and the impact of key structural properties for both the isolated and multiple circular cylinders. The complex FSI phenomena observed in experiments and numerical simulations are explained carefully via the analysis of the vortical wake topology. Following up with several critical future questions to address, we discussed the advancement of the artificial intelligent and machine learning (AI/ML) techniques in improving both the understanding and modeling of flexible cylinder FIVs. Though in the early stages, several AL/ML techniques have shown success, including auto-identification of key VIV features, physics-informed neural network in solving inverse problems, Gaussian process regression for automatic and adaptive VIV experiments, and multi-fidelity modeling in improving the prediction accuracy and quantifying the prediction uncertainties. These preliminary yet promising results have demonstrated both the opportunities and challenges for understanding and modeling of flexible cylinder FIVs in today's big data era.

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Practical engineering prediction models for flow-induced vibration are needed in the design of structures in the ocean. Research has shown that structural vibration response may be influenced by a large number of physical input parameters, such as damping and Reynolds number. Practical response prediction tools used in design are inevitably a compromise between complexity and simplicity of use. Modern machine learning tools may be used to identify which input parameters are most important. Standard machine learning techniques enable the researcher to compile a list of the most important input parameters, ranked or ordered by the effect of each on the prediction error of the model. When all inputs are treated as equals, blind application of machine learning may lead to predictions that are inconsistent with prior physical knowledge. To address this problem, we conducted a parameter selection process using a prior knowledge-based, trend-informed neural network architecture. This approach was used to identify features important to the prediction of the cross-flow vibration response amplitude of long flexible cylinders, given the known prior effect of Reynolds number and damping. The model balances the usual goal of minimizing the model prediction error, but doing so in a manner that closely follows the extensive knowledge we have of the influence of Reynolds number and damping parameter on response. The resulting neural network model was able to reveal additional insights, including the role of mode number shifting, mode dominance and travelling waves in the regulation of VIV response amplitude.

In this paper it is shown that for very long risers in sheared flow there is a surprising outcome — the VIV response amplitude in the power-in region does not depend at all on the amount of damping in the power out region, as long as it is sufficient to prevent waves from reflecting at the boundary and returning to the power-in region. In these cases, the response in the power-in region depends on the wave radiation damping and not on the damping in the power-out regions. Tension plays a major role in the determining the radiation damping and in some cases, but not all, pulling harder will indeed reduce response in the power-in region. Numerical simulations are presented in which a finite element model of a long riser is used to compute the VIV response in a sheared flow for which the power-in region is at one end of the riser. The radiated waves are shown to diminish with distance traveled as expected. When ζoutnout, the product of the number of wavelengths to reach the far termination and the damping ratio in the power-out region is greater than 0.18, it is shown that no significant vibration energy returns to the power-in region and the response in the power-in region is independent of the damping in the power-out region. The numerical simulation is used to illustrate the effect of changing tension on the radiation damping and therefore on the VIV response. The VIV response prediction program SHEAR7 is used to evaluate the effect of increasing tension on a realistic deepwater drilling riser in 3000 m water depth. A 20% increase in tension leads to a 12% reduction in fatigue damage rate.

Because of scale effects and inappropriate hydrodynamic models, the nonlinear hydroelastic response of net cages used for fish-farming cannot be analyzed precisely with traditional model testing or combinations of finite element methods (FEMs) and load models. In this study, an innovative hybrid method is proposed to determine the hydroelastic response of full-scale floater-and-net systems more accurately. In this method, the net for the fish cage was vertically and peripherally divided into similar interconnected sections with different hydrodynamic parameters, which were assumed to be uniformly distributed over each section. A model of a typical section was subjected to various towing velocities, oscillation periods, and amplitudes in a towing tank to simulate the potential motions of all sections in the net under various currents, waves, and floater movements. By analyzing the measured hydrodynamic force from this test section, a hydrodynamic force database for a typical net section under various currents, waves, and floater motions was built. Finally, based on an FEM, the modified Morison equation and the hydrodynamic force database, the hydroelastic behavior of the full-scale fish cage was calculated with an iterative scheme. It is demonstrated that this hybrid method is able to produce correct hydroelastic response for both steady and oscillatory flows. The hydroelastic response of a two-dimensional example of a full-length net panel with steady currents and floater oscillations was studied in detail.