• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.5 s.248
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• pp.533-542
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• 2006

In order to assess the integrity of pipes with local thinning area, the plastic strain as well as the elastic strain at the root of thinned region are required particularly when fluctuating load is applied to the pipe. For estimating elastic-plastic strain at local wall thinning area in a straight pipe under tensile load, an estimation model with idealized fully circumferential constant depth wall thinning area is proposed. Based on the compatibility and equilibrium equations a nonlinear estimation equation, from which local elastic-plastic strain can be determined as a function of pipe/defect geometry, material and the applied strain was derived. Estimation results are compared with those from detailed elastic-plastic finite element analysis, which shows good agreements. Noting that practical wall thinning in nuclear piping has not only a circular shape but also a finite circumferential length, the proposed solution for the ideal geometry is extended based on two-dimensional and three-dimensional numerical analysis of pipes with circular wall thinning.

• Transactions of Materials Processing
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• v.32 no.2
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.207-224
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• 2022

The mechanical behavior of prestressed concrete haunched beams (PSHBs) was investigated in depth using a finite element modeling technique in this study. The efficiency of finite element modeling was investigated in the first stage by taking into account a previous study from the literature. The first stage's findings suggested that finite element modeling might be preferable for modeling PSHBs. In the second stage of the research, a comprehensive parametric study was carried out to determine the effect of each parameter on PSHB load capacity, including haunch angle, prestress level, compressive strength, tensile reinforcement ratio, and shear span to depth ratio. PSHBs and prestressed concrete rectangular beams (PSRBs) were also compared in terms of capacity. Stochastic analysis was used in the third stage to define the uncertainty in PSHB capacity by taking into account uncertainty in geometric and material parameters. Standard deviation, coefficient of variation, and the most appropriate probability density function (PDF) were proposed as a result of the analysis to define the randomness of capacity of PSHBs. In the study's final section, a new equation was proposed for using symbolic regression to predict the load capacity of PSHBs and PSRBs. The equation's statistical results show that it can be used to calculate the capacity of PSHBs and PSRBs.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.12
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• pp.961-968
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• 2012

In this study, we have investigated the effects of Mn and Co co-doping on defects, J-E curves and grain boundary characteristics of ZnO-$Bi_2O_3$ (ZB) varistor. Admittance spectra and dielectric functions show two bulk defects of $Zn_i^{{\cdot}{\cdot}}$ (0.17~0.18 eV) and $V_o^{\cdot}$ (0.30~0.33 eV). From J-E characteristics the nonlinear coefficient (${\alpha}$) and resistivity (${\rho}_{gb}$) of pre-breakdown region decreased as 30 to 24 and 5.1 to 0.08 $G{\Omega}cm$ with sintering temperature, respectively. The double Schottky barrier of grain boundaries in ZB(MCo) ($ZnO-Bi_2O_3-Mn_3O_4-Co_3O_4$) could be electrochemically single type. However, its thermal stability was slightly disturbed by ambient oxygen because the apparent activation energy of grain boundaries was changed from 0.64 eV at lower temperature to 1.06 eV at higher temperature. It was revealed that a co-doping of Mn and Co in ZB reduced the heterogeneity of the barrier in grain boundaries and stabilized the barrier against an ambient temperature (${\alpha}$-factor= 0.136).

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.11
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• pp.882-889
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• 2011

In this study, we have investigated the effects of Co doping on I-V curves, bulk trap levels and grain boundary characteristics of ZnO-$Bi_2O_3$ (ZB) varistor. From I-V characteristics the nonlinear coefficient (a) and the grain boundary resistivity (${\rho}_{gb}$) decreased as 32${\rightarrow}$22 and 18.4${\rightarrow}0.6{\times}10^9{\Omega}cm$ with sintering temperature (900~1,300$^{\circ}C$), respectively. Admittance spectra and dielectric functions show two bulk traps of zinc interstitial, $Zn_i^{{\cdot}{\cdot}}$(0.16~0.18 eV) and oxygen vacancy, $V_o^{{\cdot}}$ (0.28~0.33 eV). The barrier of grain boundaries in ZBCo (ZnO-$Bi_2O_3-Co_3O_4$) could be electrochemically single type. However, its thermal stability was slightly disturbed by ambient oxygen because the apparent activation energy of grain boundaries was changed from 0.93 eV at the 460~580 K to 1.13 eV at the 620~700 K. It is revealed that Co dopant in ZB reduced the heterogeneity of the barrier in grain boundaries and stabilized the barrier against the ambient temperature.

• Earthquakes and Structures
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• v.11 no.6
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• pp.943-966
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• 2016

Recently, magnetorheological elastomer (MRE) material and its devices have been developed and attracted a good deal of attention for their potentials in vibration control. Among them, a highly adaptive base isolator based on MRE was designed, fabricated and tested for real-time adaptive control of base isolated structures against a suite of earthquakes. To perfectly take advantage of this new device, an accurate and robust model should be built to characterize its nonlinearity and hysteresis for its application in structural control. This paper first proposes a novel hysteresis model, in which a nonlinear hyperbolic sine function spring is used to portray the strain stiffening phenomenon and a Voigt component is incorporated in parallel to describe the solid-material behaviours. Then the fruit fly optimization algorithm (FFOA) is employed for model parameter identification using testing data of shear force, displacement and velocity obtained from different loading conditions. The relationships between model parameters and applied current are also explored to obtain a current-dependent generalized model for the control application. Based on the proposed model of MRE base isolator, a second-order sliding mode controller is designed and applied to the device to provide a real-time feedback control of smart structures. The performance of the proposed technique is evaluated in simulation through utilizing a three-storey benchmark building model under four benchmark earthquake excitations. The results verify the effectiveness of the proposed current-dependent model and corresponding controller for semi-active control of MRE base isolator incorporated smart structures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.193-200
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• 2011

A BOR(Boil-Off Rate) prediction model for the NO96 membrane-type LNG insulation containment filled with superlite powders during laden voyage is presented in this paper. Finite element model for the unsteady-state heat transfer analysis is constructed by considering the air and water conditions and by employing the homogenization method to simplify the complex insulation material composition. BOR is evaluated in terms of the total amount of heat invaded into LNGCC and its variation to the major variables is investigated by the parametric heat transfer analysis. Based upon the parametric results, a BOR prediction model which is in function of the LNG tank size, the insulation layer thickness and the powder thermal conductivity is derived. Through the verification experiment, the accuracy of the derived prediction model is justified such that the maximum relative difference is less than 1% when compared with the direct numerical estimation using the FEM analysis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.336-337
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• 2005

ZnO varistor ceramics were fabricated as a function of the amount of $Y_2O_3$ addition and sintered at $1250^{\circ}C$ for 2 hour. The average grain size was decreased from 14.2 ${\mu}m$ to 8.3 ${\mu}m$ with the amount of $Y_2O_3$ addition, and varistor voltage was increased from 433 V to 563 V with $Y_2O_3$ addition. Nonlinear coefficient a of all specimens were increased with the amount of $Y_2O_3$ more than 67, in case of $Y_2O_3$ 0.01wt% addition showed the excellent results of 87. And leakage current was less than $1{\mu}A$ at 82% of varistor voltage. The clamping voltage ratio of the specimens added 0.01wt% $Y_2O_3$ was 1.41 at 25A [8/20${\mu}s$]. At the specimen 0.01wt% $Y_2O_3$ addition. endurance of surge current and deviation of varistor voltage were 5700A/$cm^2$ and $\Delta$-2.86%, respectively.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.1
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• pp.1-9
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• 2019

In electric agricultural machine the gearbox is used to increase torque and lower the output speed of the motor shaft. The gearbox consists of several shafts, helical gears and spur gears works in series. Optimization plays an important role in gear design as reducing the weight or volume of a gear set will increase its service life and improve the bearing capacity. In this paper the basic design parameters for gear like shaft diameter and face width are considered as the input variables. The bending stress and material volume is considered as the objective function. ANSYS was used to investigate the bending stress when the variable was changed. Artificial Neural Network (ANN) was used to obtain the mathematical model of the system based on the bending stress behaviour. The ANN was used since the output system is nonlinear. The Genetic Algorithm (GA)　technique of optimization is used to obtain the optimized values of shaft diameter and face width on the pinion based on the ANN mathematical model and the results are compared as that obtained using the traditional method. The ANN and GA were performed using MATLAB. The simulation results were shown that the proposed algorithm was successfully calculated the value of shaft diameter and face width to obtain the minimal bending stress and material volume of the gearbox.

• 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.