• Proceedings of the Korean Nuclear Society Conference
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• 2002.05a
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• pp.126-126
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• 2002

• Proceedings of the SAREK Conference
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• 2004.06a
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• pp.4-4
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• 2004

• Bulletin of the Society of Naval Architects of Korea
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• v.26 no.2
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• pp.64-72
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• 1989

A response prediction model is introduced for the non-lockin vibration of a marine riser in sheared flow, where the riser is modelled as linearly varying tensioned-beam. This prediction model is based on the Green's function approach and random vibration theory. This model, of course, can treat general beams having slowly varying spatial system parameters. According to the predicted result of a marine riser by the prediction model proposed in this paper, the dynamic behavior of a marine riser has the mixed characteristics of finite and infinite boundary behavior. Furthermore the velocity response distribution along the riser length is much similar with the sheared flow profile. The predicted response result of a marine riser having linearly varying tension was also compared to that of constant mean tensioned-beam model. It was found that the constant mean tensioned-beam case gives over-estimated responses at the top side of the riser.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.79-86
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• 2019

This paper describes a time-series data prediction based on artificial neural networks (ANN). In this study, a batch based ANN model and a stochastic ANN model have been implemented using TensorFlow libraries. Each model are evaluated by comparing training and testing errors that are measured through experiment. To train and test each model, tax dataset was used that are collected from the government website of indiana state budget agency in USA from 2001 to 2018. The dataset includes tax incomes of individual, product sales, company, and total tax incomes. The experimental results show that batch model reveals better performance than stochastic model. Using the batch scheme, we have conducted a prediction experiment. In the experiment, total taxes are predicted during next seven months, and compared with actual collected total taxes. The results shows that predicted data are almost same with the actual data.

• Composites Research
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• v.35 no.5
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• pp.334-339
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• 2022

This study suggests the prediction algorithm for transverse permeability, represented the flow resistance during the manufacturing process of composite, of unidirectional continuous fiber reinforced plastics. The cross-sectional shape of representative volume element (RVE) is considered to reflect fiber arrangement. The equivalent length is used as a factor to express the change of resin flow according to fiber arrangement. The permeability prediction algorithm is created by grafting the Electro-Hydraulic analogy and validity is confirmed. The code for permeability prediction was composed by means of MATLAB and Python, flow analysis was performed by using FLUENT. The algorithm was verified as the permeability results obtained through Algorithm and numerical analysis were almost identical to each other, and the calculation time was reduced around 1/450 compared to the numerical analysis.

• Geomechanics and Engineering
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• v.38 no.3
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• pp.215-229
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• 2024

Because of the various patterns of deep-water inrush and complicated mechanisms, accurately predicting mine water inflows is always a difficult problem for coal mine geologists. In study presented in this paper, the water inrush channels were divided into four basic water diversion structures: aquifer, rock fracture zone, fracture zone and goaf. The fluid flow characteristics in each water-conducting structure were investigated by laboratory tests, and multistructure and multisystem coupling flow analysis models of different water-conducting structures were established to describe the entire water inrush process. Based on the research of the water inrush flow paths, the analysis model of different water inrush space structures was established and applied to the prediction of mine water inrush inflow. The results prove that the conduction sequence of different water-conducting structures and the changing rule of permeability caused by stress changes before and after the peak have important influences on the characteristics of mine water-gushing. Influenced by the differences in geological structure and combined with rock mass RQD and fault conductivity characteristics and other mine exploration data, the prediction of mine water inflow can be realized accurately. Taking the water transmitting path in the multistructure as the research object of water inrush, breaking through the limitation of traditional stratigraphic structure division, the prediction of water inflow and the estimation of potentially flooded area was realized, and water bursting intensity was predicted. It is of great significance in making reasonable emergency plans.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.12
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• pp.1175-1183
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• 2013

Even if some CFD software developers and its users think that a state-of-the-art CFD software can be used to reasonably solve at least single-phase nuclear reactor safety problems, there remain limitations and uncertainties in the calculation result. From a regulatory perspective, the Korea Institute of Nuclear Safety (KINS) is presently conducting the performance assessment of commercial CFD software for nuclear reactor safety problems. In this study, to examine the prediction performance of commercial CFD software with the porous model in the analysis of the scale-down APR (Advanced Power Reactor Plus) internal flow, a simulation was conducted with the on-board numerical models in ANSYS CFX R.14 and FLUENT R.14. It was concluded that depending on the CFD software, the internal flow distribution of the scale-down APR was locally somewhat different. Although there was a limitation in estimating the prediction performance of the commercial CFD software owing to the limited amount of measured data, CFX R.14 showed more reasonable prediction results in comparison with FLUENT R.14. Meanwhile, owing to the difference in discretization methodology, FLUENT R.14 required more computational memory than CFX R.14 for the same grid system. Therefore, the CFD software suitable to the available computational resource should be selected for massively parallel computations.

• Water for future
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• v.27 no.1
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• pp.123-131
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• 1994

A plunge pool is often employed as an energy-dissipating device at the end of a spillway or a pipe culvert. A jet from spillways or pipes frequently generates a scour hole which threatens the stability of the hydraulic structure. Existing scour prediction formulas of plunge pool of spillways or pipe culverts give a wide range of scour depths, and it is, therefore, difficult to accurately predict those scour depths. In this study, a new experimental method and new scour prediction formulas under submerged circular jet for large bed materials with shallow tailwater depths were developed. A major variale, which was not used in previous scour prediction equations, was the ratio of jet size to bed material size. In this study, jet momentum acting on a bed particle and jet diffustion theory were employed to derive scour prediction formulas. Four theoretical formulas were suggested for the two regions of jet diffusion, i.e., the region of flow establishment and the region of established flow. The semi-theoretically developed scour prediction formulas showed close agreement with laboratory experiments performed on a movable bed made of large spherical particles.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.717-727
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• 2006

The present study experimentally investigated the effect of flow direction and other flow parameters on two-phase flow distribution of refrigerants at a T-junction, and also suggested a prediction model for refrigerant in a T-junction by modifying previous model for air-water flow. R-22, R-134a, and R-410A were used as test refrigerants. As geometric parameters, the direction of the inlet or branch tube and the tube diameter ratio of branch to inlet tube were chosen. The measured data were compared with the values predicted by the models developed for air-water or steam-water mixture in the literature. We propose a modified model for application to the reduced T-junction and vertical tube orientation. Among the geometric parameters, the branch tube direction showed the biggest sensitivity to the mass flow rate ratio for the gas phase, while the inlet quality showed the biggest sensitivity to the mass flow rate ratio among the inlet flow parameters.