• Tunnel and Underground Space
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• v.19 no.6
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• pp.479-489
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• 2009

Appropriate investigation of ground condition near excavation face in tunnelling is an inevitable process for safe and economical construction. In this study mechanical parameters from drilling process for blasting were investigated for the purpose of predicting the ground condition, especially rock mass strength, ahead of tunnel face. Rock mass strength is one of the most important factors for classification of rock mass and making a decision of support type in underground construction. Several rock specimens which are considered homogeneous and having different strength values respectively were tested by hydraulic drill machines generally used. As a result, penetration rate is fairly related with rock mass strength among drilling parameters. It is also found that penetration rate increases along with the higher impact pressure even under same rock strength condition. It is finally suggested that new prediction method for rock mass strength using percussive pressure and penetration rate during drilling work can be utilized well in construction site.

• Computers and Concrete
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• v.22 no.3
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• pp.291-303
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• 2018

As the concept of "sponge city" is proposed, the pervious concrete for permeable pavement has been widely used in pavement construction. This paper aims at investigating the dynamic behavior and energy evolution of pervious concrete under impact loading. The dynamic compression and split tests are performed on pervious concrete by using split Hopkinson pressure bar equipment. The failure criterion on the basis of incubation time concept is used to analyze the dynamic failure. It is demonstrated that the pervious concrete is of a strain rate sensitive material. Under high strain rate loading, the dynamic strength increases while the time to failure approximately decreases linearly as the strain rate increases. The predicted dynamic compressive and split tensile strengths based on the failure criterion are in accordance with the experimental results. The total damage energy is found to increase with the increasing of strain rate, which means that more energy is needed to produce irreversible damage as loading rate increases. The fractal dimensions are observed increases with the increasing of impact loading rate.

• Nuclear Engineering and Technology
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• v.36 no.6
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• pp.497-511
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• 2004

A PSV (pressurizer safety valve) popping test carried out in the early phases of a refueling outage may trigger a test-induced LOCA(loss of coolant accident) if a PSV fails to fully close and is stuck in a partially open position. According to a KSNP (Korea standard nuclear power plant) low power and shutdown PSA (probabilistic safety assessment), the failure of a high pressure safety injection (HPSI) accompanied by the failure of a PSV to fully close was identified as a dominant accident sequence with a significant impact on low power and shutdown risks (LPSR). In this study, we aim to investigate and verify a new means for mitigating this type of accident using a thermal-hydraulic analysis. In particular, we explore the applicability of an aggressive cool-down combined with operator actions. The results of the various sensitivity studies performed there will help reduce LPSR and improve Refueling outage safety.

• Journal of Korea Water Resources Association
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• v.45 no.5
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• pp.455-464
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• 2012

In this study, a combined unsteady friction model has been developed to simulate the waterhammer phenomenon for the pipeline system. The method of characteristics has been employed as the modeling platform for the integration of the acceleration based model and the frequency dependant model for unsteady friction. Both Zielke's model and Ramos model were also compared with pressure measurements of a pilot plant pipeline system. In order to validate the modeling approach, a pipeline system equipped with the high frequency pressure data acquisition system was fabricated. The time series of pressure, introduced by a sudden valve closure, were obtained for two Reynolds numbers. A trial and error method was used to calibrate parameters for unsteady friction model. The comparison between different unsteady friction contributions in pressure variation provided the comprehensive understanding in the pressure damping mechanism of waterhammer. The proper evaluation of unsteady friction impact is a critical factor for accurate simulation of hydraulic transient.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.860-866
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• 2022

An accurate prediction of the pressure drop along the flow paths is crucial in the design of advanced passive systems cooled by heavy liquid metal coolants. To date, a generic pressure drop correlation over spacer grids by Rehme has been applied extensively, which was obtained from substantial experimental data with multiple types of components. However, a few experimental studies have reported that the correlation may give large discrepancies. To provide a more reliable correlation for ring-type spacer grids, the current numerical study aims at figuring out the most critical factor among four hypothetical parameters, namely the flow area blockage ratio, number of fuel rods, type of fluid, and thickness of the spacer grid in the flow direction. Through a set of computational fluid dynamics simulations, we observed that the flow area blockage ratio dominantly influences the pressure loss characteristics, and thus its dependence should be more emphasized, whereas the other parameters have little impact. Hence, we suggest a new correlation for the drag coefficient as C_B = C_ν,m/ε^2.7, where C_ν,m is formulated by a nonlinear fit of simulation data such that C_ν,m = -11.33 ln(0.02 ln(Re_b)).

• Geomechanics and Engineering
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• v.19 no.2
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• pp.193-199
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• 2019

A frozen fringe plays a key role in frost heave development in soils. Previous studies have focused on the physical and mechanical properties of the frozen fringe, such as overall hydraulic conductivity, water content and pore pressure. It has been proposed that the thickness of the frozen fringe controls frost heave behavior, but this effect has not been thoroughly evaluated. This study used a temperature-controllable cell to investigate the impact of frozen fringe thickness on the characteristics of frost heave. A series of laboratory tests was performed with various temperature boundary conditions and specimen heights, revealing that: (1) the amount and rate of development of frost heave are dependent on the frozen fringe thickness; (2) the thicker the frozen fringe, the thinner the resulting ice lens; and (3) care must be taken when using the frost heave ratio to characterize frost heave and evaluate frost susceptibility because the frost heave ratio is not a normalized factor but a specimen height-dependent factor.

• Journal of the Korean Society of Visualization
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• v.19 no.3
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• pp.115-122
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• 2021

Oral cancer surgery typically consists of resection of lesion, neck dissection and reconstruction, and it has an impact on the position of hyoid bone. Therefore, morphological change of airway can occur since the geometric parameter of airway is correlated with the hyoid bone. Airflow is affected by geometry of the airway. In this study, flow characteristics were compared between pre- and post-surgery models by both particle image velocimetry (PIV) and numerical simulation. 3D model of upper airway was reconstructed based on CT data. Velocity is accelerated by the reduced channel area, and vortex and recirculation region are observed in pre- and post-surgery models. For the post-surgery model, high pressure distribution is developed by significantly decreased hydraulic diameter, and the longitudinal flow stream is also interrupted.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Land and Housing Review
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• v.13 no.1
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• pp.141-150
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• 2022

Responding to the increasing demand for research on seismic resistance of structures triggered by a large-scale earthquake in Korea, the Ministry of the Interior and Safety revised the typical application of the existing seismic design standards with the national seismic performance target enhanced. Therefore, in this paper, the dam body of the aged Test-Bed and the penstock with fluid were modeled by the three-dimensional finite element method by introducing several variables. The current seismic design standard law confirmed the safety of the dam structure and penstock against seismic waves. As a result of the 3D finite element analysis, the stress change due to the water impact of the penstock was minimal, and it was confirmed that the effect of the hydraulic pressure was more significant than the water impact in the earthquake situation. When the hydrostatic pressure is in the form of SPH, it was analyzed that the motion of the fluid and the location of stress caused by the earthquake can be effectively represented, and it will be easier to analyze the weak part. As a result of the analysis, which considers penstock's corrosion, the degree of stress dispersion gets smaller because the penstock is embedded in the body. The stress result is minimal, less than 1% of the yield stress of the steel. In addition, although there is a possibility of micro-tensile cracks occurring in the inlet of the dam, it has not been shown to have a significant effect on the stress increa.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.