Does More Tension Reduce VIV?

Vandiver, J. Kim; Ma, Leixin

doi:10.1115/omae2017-61435

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…In this situation, the radiation damping, determined by the impedance of the cylinder Z R , is the dominant factor for energy dissipation. Such a condition was found to happen for very long risers in shear flow 74,130,131 or risers partially covered with strakes or fairings. 20 In 1983, a 290 m long and 4.06 mm diameter braided Kevlar cable was deployed in a shear current from a barge, moored near St. Croix in the US Virgin Islands.…”

Section: The Effect Of Key Structural Parameters On Flexible Cylinder...mentioning

confidence: 97%

Flexible cylinder flow-induced vibration

Lin

Fan

et al. 2022

Physics of Fluids

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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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Section: The Effect Of Key Structural Parameters On Flexible Cylinder...mentioning

confidence: 97%

Flexible cylinder flow-induced vibration

Lin

Fan

et al. 2022

Physics of Fluids

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Enhancing Machine Learning Models With Prior Physical Knowledge to Aid in VIV Response Prediction

Resvanis

Vandiver

2021

Volume 8: CFD and FSI

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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.

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Does More Tension Reduce VIV?

Cited by 2 publications

References 5 publications

Flexible cylinder flow-induced vibration

Flexible cylinder flow-induced vibration

Enhancing Machine Learning Models With Prior Physical Knowledge to Aid in VIV Response Prediction

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