• Journal of the Society of Naval Architects of Korea
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• v.47 no.5
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• pp.647-655
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• 2010

In this paper, a numerical study is carried out for super-pipe, flat plate and axisymmetric body flows to investigate a validity of using wall function and high $y_1^+$ in calculation of high Reynolds number flow. The velocity profiles in boundary layer agree well with the law of the wall. And it is found that the range of $y^+$��which validated the logarithmic law of the wall grows with increasing Reynolds number. From the result, an equation is suggested that can be used to estimate a maximum $y^+$ value of validity of the log law. And the slope(1/$\kappa$) of the log region of the numerical result is larger than that of experimental data. On the other hand, as $y_1^+$ is increasing, both the friction and the pressure resistances tend to increase finely. When using $y_1^+$ value beyond the range of log law, the surface shear stress shows a significant error and the pressure resistance increases rapidly. However, when using $y_1^+$ value in the range, the computational result is reasonable. From this study, the use of the wall function with high value of $y_1^+$ can be justified for a full scale Reynolds number ship flow.

• The Korean journal of helicobacter and upper gastrointestinal research
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• v.18 no.3
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• pp.150-156
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• 2018

Precision medicine stands for 4Ps - precise, preventive, participatory, and personal; in which "precision" is important because the current modern medicine starts from "trial and error," and "one does not fit all". Current targeted therapies for cancer have changed treatment approaches and led the precision medicine; however, clinical use of liquid biopsy, using blood or other liquid specimens to characterize circulating tumor cells (CTC) or tumor genes instead of biopsies of tumor tissues, still awaits availability of more information regarding non-invasive cancer detection and characterization, prediction of treatment response, monitoring the disease course and relapse possibilities, identification of mechanisms of drug resistance, and newer pathogenesis. In this review, we will introduce the basic concept of CTC, circulating cell free DNA, and exosomes and their possible application for gastric cancer relevant with Helicobacter pylori infection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1131-1138
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• 2019

The importance of coatings has been increasing for different purposes such as prevention of static electricity of auto parts or products, improvement of abrasion and corrosion resistance, and enhancement of esthetics. As a method for measuring the thickness of a coating film, a contact method with probe is commonly used. However, it is problematic that accuracy of the sensor is degraded due to sensor output distortion or load phenomenon, which is caused by a change in magnetic permeability of the core. In this study, we propose a method to reduce the measurement error of the coating film by applying the optimized circuit design and the thickness measurement algorithm to the problems caused by the nonlinear characteristics. The tests result which have been taken with different thickness coating samples show that the measurement accuracy is within ${\pm}2%$.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.287-296
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• 2020

The present study aims to improve the accuracy of the maneuvering simulations based on captive model test results. To derive the hydrodynamic coefficients in a self-propelled condition, a mathematical maneuvering model using a whole vehicle model was established. Captive model tests were carried out using the Vertical Planar Motion Mechanism (VPMM) equipment. A motor controller was used to control the constant propeller revolution rate during pure motion tests. The resistance tests, self-propulsion tests, static drift tests, and VPMM tests were performed in the towing tank of Seoul National University. When the vertical drift angle changes, the gravity load on the sensors were changed. The hydrodynamic forces were deduced by subtracting the gravity load from the measured forces. The hydrodynamic coefficients were calculated using the least-square method. The simulation of the turning circle test was compared with the free-running model test result, and the error of the turning radius was 8.3 % compared to the free-running model test.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.435-448
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• 2021

This study describes the depth and straight motion control performance depending on control surface combinations of a supercavitating underwater vehicle. When an underwater vehicle experiences supercavitation, friction resistance can be minimized, thus achieving the effect of super-high-speed driving. Six degrees of freedom modeling of the underwater vehicle are performed and the guidance and control loops are designed with not only a cavitator and an elevator, but also a rudder and a differential elevator to improve the stability of the roll and yaw axis. The control performance based on the combination of control surfaces is analyzed by the root-mean-square error for keeping depth and straight motion.

• Geomechanics and Engineering
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• v.28 no.1
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• pp.11-23
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• 2022

The response of pile foundations under lateral loads are usually analyzed using beam-on-nonlinear-Winkler-foundation (BNWF) model framework employing various forms of empirically derived p-y curves and p-multipliers. In practice, the p-y curve presented by the American Petroleum Institute (API) is most often utilized for piles in granular soils, although its shortcomings are recognized. The objective of this study is to evaluate the performance of the BNWF model and to quantify the error in the estimated pile response compared to a rigorous numerical model. BNWF analyses are performed using three sets of p-y curves to evaluate reliability of the procedure. The BNWF model outputs are compared with results of 3D nonlinear finite element (FE) analysis, which are validated via field load test measurements. The BNWF model using API p-y curve produces higher load-displacement curve and peak bending moment compared with the results of the FE model, because empirical p-y curve overestimates the stiffness and underestimates ultimate resistance up to a depth equivalent to four times the pile diameter. The BNWF model overestimates the peak bending moment by approximately 20-30% using both the API and Reese curves. The p-multipliers are revealed to be sensitive on the p-y curve used as input. These results highlight a need to develop updated p-y curves and p-multipliers for improved prediction of the pile response under lateral loading.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.639-645
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• 2006

FRP (Fiber Reinforced Polymer) is an excellent new constructional material in resistibility to corrosion, high intensity, resistibility to fatigue, and plasticity. FBG (Fiber Bragg Grating) sensor is widely used at present as a smart sensor due to lots of advantages such as electric resistance, small-sized material, and high durability. However, with insufficiency of measuring displacement, FBG sensor is used only as a sensor measuring physical properties like strain or temperature. In this study, FRP and FBG sensors are to be hybridized, which could lead to the development of a smart FRP rod. Moreover, developing the estimated model for deflection with neural network method, with the data measured through FBG sensor, could make conquest of a disadvantage of FBG sensor - uniquely used for sensing strain. Artificial neural network is MLP (Multi-layer perceptron), trained within error rate of 0.001. Nonlinear object function and back-propagation algorithm is applied to training and this model is verified with the measured axial displacement through UTM and the estimated numerical values.

• Steel and Composite Structures
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• v.49 no.3
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• pp.337-348
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• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.