• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.315-321
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• 2023

Long-span bridges are flexible structures with low natural frequencies and damping ratios, making them susceptible to vibrational serviceability problems. However, the current design guideline of South Korea assumes a uniform threshold of wind speed or vibrational amplitude to assess the occurrence of harmful vibrations, potentially overlooking the complex vibrational patterns observed in long-span bridges. In this study, we propose a pointwise vortex-induced vibration (VIV) detection method using a deep-learning-based signalsegmentation model. Departing from conventional supervised methods of data acquisition and manual labeling, we synthesize training data by generating sinusoidal waves with an envelope to accurately represent VIV. A Fourier synchrosqueezed transform is leveraged to extract time-frequency features, which serve as input data for training a bidirectional long short-term memory model. The effectiveness of the model trained on synthetic VIV data is demonstrated through a comparison with its counterpart trained on manually labeled real datasets from an actual cable-supported bridge.

• Journal of the Korean Society of Visualization
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• v.15 no.1
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• pp.19-24
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• 2017

In this study, we conduct a numerical experiment of the single 5MW NREL wind turbine and compare the performance of various vortex core identification for the wake behind the wind turbine. In the kinetic analysis of wind turbine, 20% velocity deficit at 200 s is observed, showing wake which contains tip vortex near blade tip and rotor vortex at the center of the wind turbine. Time series of velocity and turbulent intensity show numerical simulation converge to a quasi-steady state near 200 s. In the comparison between methods for vortex identification, ${\lambda}_2$-method has good performance in terms of tip vortex, rotor vortex, vortex during its cascade process compared to vorticity magnitude criteria, ${\Delta}$-method. We conclude that ${\lambda}_2$-method is suitable for vortex identification method for wake visualization.

• Journal of the Korean Society of Visualization
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• v.17 no.2
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• pp.48-57
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• 2019

A straightforward strategy for particle image velocimetry (PIV) interrogation and post-processing has been proposed, aiming at reducing errors and clarifying vortex structures. The interrogation window size should be kept small to reduce bias error and improve spatial resolution. A spatial filter is then applied to the velocity field to reduce random error and clarify flow structure. The performance of three popular spatial filters were assessed: box filter, median filter, and local quadratic polynomial regression filter. In order to quantify random uncertainty, the image matching (IM) method is applied to an experimental dataset of homogeneous and isotropic turbulence (HIT) obtained by 2D-PIV. We statistically analyze the uncertainty propagation through the spatial filters, and verify the reduction in random uncertainty. Moreover, we illustrate that the spatial filters help clarify vortex structures using vortex identification criteria. As a result, PIV random uncertainty was reduced and the vortex structures became clearer by spatial filtering.