• Geomechanics and Engineering
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• 제29권6호
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• pp.627-643
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• 2022

Water-rock interactions have a significant influence on the mechanical behavior of rocks. In this study, uniaxial compression and tension tests on different water-treated sandstone samples were conducted. Acoustic emission (AE) monitoring and micro-pore structure detection were carried out. Water-rock interactions and their effects on rock mechanical behavior were discussed. The results indicate that water content significantly weakens rock mechanical strength. The sensitivity of the mechanical parameters to water treatment, from high to low, are Poisson ratio (𝜇), uniaxial tensile strength (UTS), uniaxial compressive strength (UCS), elastic modulus (E), and peak strain (𝜀). After water treatment, AE activities and the shear crack percentage are reduced, the angles between macro fractures and loading direction are minimized, the dynamic phenomenon during loading is weakened, and the failure mode changes from a mixed tensile-shear type to a tensile one. Due to the softening, lubrication, and water wedge effects in water-rock interactions, water content increases pore size, promotes crack development, and weakens micro-pore structures. Further damage of rocks in fractured and caved zones due to the water-rock interactions leads to an extra load on the adjoining coal and rock masses, which will increase the risk of dynamic disasters.

• 상하수도학회지
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• 제35권1호
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• Journal of Advanced Research in Ocean Engineering
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• 제2권1호
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• pp.1-11
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• 2016

The objective of this paper is to present an analytical solution in deep water waves and verify the validity of the theory (Shin, 2015). Hence this is a follow-up to Shin (2015). Instead of a variational approach, another approach was considered for a more accurate assessment in this study. The products of two coefficients were not neglected in this study. The two wave profiles from the KFSBC and DFSBC were evaluated at N discrete points on the free-surface, and the combination coefficients were determined for when the two curves pass the discrete points. Thus, the solution satisfies the differential equation (DE), bottom boundary condition (BBC), and the kinematic free surface boundary condition (KFSBC) exactly. The error in the dynamic free surface boundary condition (DFSBC) is less than 0.003%. The wave theory was simplified based on the assumption tanh $D{\approx}1$ in this paper. Unlike the perturbation method, the results are possible for steep waves and can be calculated without iteration. The result is very simple compared to the 5th Stokes' theory. Stokes' breaking-wave criterion has been checked in this study.

• Structural Engineering and Mechanics
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• 제60권1호
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• pp.131-148
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• 2016

Tuned Liquid Dampers (TLDs) provide low damping when it comes to deep water condition, and that not all water depth is mobilized in energy dissipation. This research focussed on a method to improve the efficiency of TLDs with deep water condition. Several bottom-mounted baffles were installed inside a TLD and the dynamic characteristics of modified TLDs together with their effect on the vibration control of a SDOF structure were studied experimentally. A series of free vibration and harmonic forced vibration tests were carried out. The controlling parameter in the conducted tests was the Vertical Blocking Ratio (VBR) of baffles. Results indicated that increase in VBR decreases the natural frequency of TLD and increases its damping ratio. It was found that the VBR range of 10% to 30% reduced response of the structure significantly. The modified TLD with the VBR of 30% showed the best performance when reduction in structural responses under harmonic excitations were compared.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1999년도 학술발표대회 논문집 제18권 1호
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• pp.218-223
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• 1999

A piezoelectric flextentional deep-water sonar transducer has been simulated using a coupled FE-BEM. The dynamics of the sonar transducer is modelled in three dimensions and is analyzed with extern리 electrical excitation conditions as well as external acoustic pressure loading conditions. Different results are available such as steady-state frequency response for RX and TX, displacement modes, directivity patterns, back-scattering patterns, resonant frequencies, bandwidths, quality factors, transmitting voltage (TV) responses, input receiving sensitivity (RS) responses. White the present barrel-stave typed sonar transducer of the piezoelectric material is being simulated, the external surface of the transducer is modified in order to allow the same water pressure to be applied to the inner and the outer surfaces of the transducer. With this modification for deep-water application, the resonance frequency of the modified flextentional sonar transducer becomes much lower than that of the unmodified flextentional sonar transducer. The results of the present sonar transducer modelling are also compared with those of a commercial package such as ATILA.

• 한국해양공학회지
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• 제26권6호
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• pp.86-92
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• 2012

An exploration well is drilled where oil or gas potential is shown by a seismic survey and interpretation. With the advance of drilling technology, most of the easily accessible oil had been developed by the end of the 20th century. To satisfy the ever increasing demand for oil, and bolstered by high oil prices, the major oil companies started to drill in deep water, which requires a deep offshore drilling unit. Offshore drilling units are generally classified by their maximum operating water depth. Many semi-submersible rigs have been purpose-designed for the drilling industry as the allowable drilling water depth has become deeper by the developed technics since the first semi-submersible was launched in 1963. Semi-submersible rigs are commonly used for shallow to deep water up to 3,000 m. Drilling equipment such as a top drive, blowout preventer, drawworks and power system, mud circulation system, and subsea wellhead system are explained to help with an understanding of offshore drilling procedures in the oil and gas fields. The objective of this paper is to introduce the main components of a semi-submersible rig and, by doing so, to raise the awareness of offshore drilling, which accounts for over 30% of the total oil production and will continue to increase.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권6호
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• pp.1056-1063
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• 2015

Waves can be expressed in terms of a spectrum; that is, the energy density distribution of a representative wave can be determined using statistical analysis. The JONSWAP, PM and BM spectra have been widely used for the specific target wave data set during storms. In this case, the extracted wave data are usually discontinuous and independent and cover a very short period of the total data-recording period. Previous studies on the continuous wave spectrum have focused on wave deformation in shallow water conditions and cannot be generalized for deep water conditions. In this study, the Generalized Extreme Value (GEV) function is proposed as a more-optimal function for the fitting of the continuous wave spectral shape based on long-term monitored point wave data in deep waters. The GEV function was found to be able to accurately reproduce the wave spectral shape, except for discontinuous waves of greater than 4 m in height.

• Journal of Electrochemical Science and Technology
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• 제12권4호
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• pp.387-397
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• 2021

Deep eutectic solvents have been established as feasible metal electroplating solvent alternatives over traditional toxic aqueous plating baths. However, water, either added intentionally or unintentionally, can significantly influence the solvent's physical properties and performance, thereby hindering its industry application. In this study, the hygroscopicity, or the ability to absorb moisture from the environment, of synthesized ethaline (1:2 choline chloride:ethylene glycol) was investigated. The kinematic viscosity, electrical conductivity, electrochemical window, and water content of ethaline were monitored over a 2-week period. Karl Fischer titration tests showed that ethaline exposed to the atmosphere displayed significant hygroscopicity compared to its unexposed counterpart. ¹H NMR spectroscopy revealed that water vapor was readily absorbed at the surface due to the hydrophilic groups present in the ethaline molecule. Water uptake resulted in the decrease in viscosity, increase in electrical conductivity and narrowing of the electrochemical window of ethaline. Solution heating at 100℃ removed the absorbed moisture and allowed the recovery of the solvent's initial properties.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.206-214
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• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.

• 한국수자원학회논문집
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• 제54권spc1호
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• pp.1083-1093
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• 2021

물 사용량 예측은 최적의 용수 공급 운영 방안을 수립하고 전력 소비량 절감을 위하여 꼭 필요한 과정이라고 할 수 있다. 그러나 수용가 단위의 물 사용량은 용도, 사용자의 패턴, 날씨 등의 다양한 요인으로 인해 변화하는 비선형적 특성을 지니고 있다. 따라서 본 연구에서는 비선형적인 수용가 단위의 물 사용량을 예측하기 위하여 다양한 기법들을 연계한 KWD 프레임워크를 제안하고자 하였다. 즉, 먼저 개별 수용가 마다 용도에 따른 유사한 패턴을 파악하기 위해 K-means (K) 군집분석을 수행하였고, 잡음성분을 제거함으로써 핵심적인 주기패턴을 파악하기 위해 Wavelet (W) 방법을 적용하였다. 또한 비선형적 특성을 학습시키기 위해 Deep learning (D) 알고리즘을 적용하였다. 그리고 기존의 선형 시계열 모형인 ARMA 모형과 비교하여 KWD 프레임워크의 성능을 분석하였다. 그 결과 제안된 모형의 상관성은 92%, ARMA 모형은 약 39%로 KWD 프레임워크가 2배 이상의 성능을 가지는 것으로 분석되었다. 따라서 본 연구에서 제안한 방법을 활용할 경우 정확한 물 사용량 예측이 가능해질 것이며, 상황에 따른 최적의 공급 방안을 수립할 수 있을 것이다.