• Journal of Ocean Engineering and Technology
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• v.22 no.1
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• pp.22-29
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• 2008

A numerical investigation is made on the effects of the location and shape of the front wall of an OWC(Oscillating Water Column) chamber on the hydroelastic response of a VLFS. Most of the studies on the effects of an OWC chamber on the response of a VLFS have assumed the location of the OWC chamber to be at the front of the VLFS. In the present study, an OWC-chamber is introduced at an arbitrary position in relation to a VLFS to determine the influence of the location and shape of the OWC chamber on the hydroelastic response of the VLFS. A finite element method is adopted as a numerical scheme for the fluid domain. or the finite element method, combined with a mode superposition method, is applied in order to consider the change of mass and stiffness The OWC chamber in a piecewise constant manner. or the facilitated anefficient analysis of The hydroelastic response of the VLFS, as well as the easy modeling of different shape and material properties for the structure. Reduction of hydroelastic response of the VLFS is investigated for various locations and front wall shapes of the owe chamber.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.29-36
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• 2006

This paper proposes a new algorithm of MS-EIDV (modal sensitivity-effective independence distribution vector) for determining optimal accelerometer locations (OAL) by using the Fisher Information Matrix (FIM) derived from mode-shape sensitivities. Also, the paper provides a reasonable guideline for selecting OAL which can reflect dynamic responses of a structure effectively. Since OAL should be determined with known values of structural parameters but since the parameters can be estimated by applying an inverse method such as SI (system identification) using measured response, the paper proposes a statistical method to overcome the paradox by considering the error bound of the structural parameters. To examine the proposed methods, a frequency-domain SI method has been applied. By using the identified results, the minimum necessary number of accelerometers could be selected depending on the number of target measurable modes. Through simulation studies, the results by applying EIDV method directly using the information of mode shapes were compared with those by applying the proposed MS-EIDV.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.125-138
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• 2006

The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in data acquisition system andinformation processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, damage detection of bridge structures using neural networks technique based on the modal properties is presented, which can effectively consider the modeling uncertainty in the analysis model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness and applicability of the proposed method.

• Progress in Superconductivity and Cryogenics
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• v.21 no.4
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• pp.9-12
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• 2019

We performed Raman spectroscopy on two different over-doped Bi₂Sr₂CaCu₂O_8+x (BSCCO), of which superconducting transition temperatures are 89 K and 77 K. Line-shape analysis of the Raman-spectrum was done, focused on B_1g bond buckling mode which have drawn a lot of attention, since photoemission studies showed an evidence for strong coupling between the mode and electron. The line-shapes show asymmetry and are well fitted by the Fano line-shape formula. Remarkably, we found that the peak line-widths from B_1g bond buckling mode in BSCCO show much broader than those in YBa₂Cu₃O_7-x. The broad line width can be attributed to the superstructure modulation of BSCCO. Our results imply that B_1g bond buckling mode may have close relation to the origin of superconductivity or to boosting the superconducting transition temperature in BSCCO.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.1
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• pp.47-52
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• 2013

In order to operate a search and rescue robot in hazardous area, the robot requires high mobility and adaptable locomotion for moving in unpredictable environments. In this paper, we propose the deformable soft wheel robot that can produce three kinds of driving modes; caterpillar driving mode, normal wheel driving mode, legged-wheel driving mode. The robot changes its driving mode as it faces the various obstacles such as a small gap, stairs etc. Soft film and composite materials are used for fabrication of deformable wheel structure and Shape Memory Alloy (SMA) coil spring actuators are attached on the structure as an artificial muscle. Film lamination and an composite manufacturing process is introduced and the robot design is required to be modified and compromised to applying the manufacturing process. The prototype is developed and tested for verifying feasibility of the deformable wheel locomotion.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of Wind Energy
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• v.13 no.3
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• pp.13-21
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• 2022

When a wind turbine is designed, the dynamic stability of the system as well as the dynamic characteristics of the main components such as blades, hub, main shaft and tower must be evaluated. In particular, the natural frequencies of a blade, as a main load-generating component, need to be measured and assessed by component level testing. In conventional practice, the natural frequencies of a blade are determined as the measured frequencies near the reference frequencies provided by FE analysis results. But the reference frequencies are also uncertain since designers have difficulty distinguishing the torsional mode shape among the analysis results due to the complexity of its mode shape. So, in conventional practice, the determination of a measured torsional frequency inevitably contains uncertainty. Therefore, a novel method to definitely determine the torsional frequencies from the experimental data itself is necessary. In this paper, a new methodology to measure the torsional frequency of a blade was studied from the perspective of a modal test procedure, data processing method and mode determination logic. Finally, the validity of the method that can measure torsional frequency without reference FE analysis results was verified by applying it to an actual large wind turbine blade

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.5
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• pp.489-494
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• 2013

This study aims to understand the mode characteristics of a droplet under a periodic forced vibration. To predict the resonance frequency of a droplet, theoretical and experimental approaches were employed. A high-speed camera was used to capture the various deformation characteristics of a droplet-mode shape, detachment, separated secondary droplet, and skewed deformation. The comparison between the theoretical and the experimental approaches shows a ~10% discrepancy in the prediction of the resonance frequency, which appears to be caused by the effect of contact line friction, nonlinear wall adhesion, and experimental uncertainty. Owing to contact-line pinning and smaller amplitude, the droplet shape becomes symmetric and the size of each lobe at the resonance frequency exceeds that at the neighbor, which is out of resonance.

• Journal of Ocean Engineering and Technology
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• v.31 no.5
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• pp.364-370
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• 2017

A numerical model based on the mode superposition method is used to study the vortex-induced vibration response characteristics of a deep-sea riser such as steel catenary riser (SCR). A steel catenary riser can be modeled using a flexible cable with simple supports at both ends. The natural frequency, mode shape and mode curvature of the riser are calculated and the vortex-induced vibration response of the riser is obtained using the equilibrium of the input and output power. The mode superposition method is applied to the vibrational stresses for each mode to calculate the overall riser fatigue life.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.916-925
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• 2000

Perhaps, this is the first attempt which applies the wavelet transform to the fundamental vibration mode for damage detection in a beam. Contrary to most existing detection methods on mode shapes, the present method directly works only with the fundamental mode of a damaged beam: no vibration mode shape of a undamaged beam is necessary. Applying the concept of vanishing moments of wavelet functions, we show that wavelet functions are effective damage detectors. Both numerical and experimental results confirm the effectiveness of the present method.