• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.7
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• pp.616-621
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• 2014

Consumption of the fuel on the satellite operating in low earth orbit, is increased due to the air resistance and the amount of increase makes the satellite lifetime decrease or the satellite mass risen. Therefore the prediction of drag force of the satellite is important. In the paper, drag force and drag coefficient analysis of the parabolic antenna satellite in low earth orbit using direct simulation monte carlo method (DSMC) is conducted according to the mission altitude and angle of attack. To verify the DSMC simulated rarefied air movement, Starshine satellite drag coefficient according to the altitude and gas-surface interaction are compared with the flight data. Finally, from the analysis results, it leads to appropriate satellite drag coefficient for orbit lifetime calculation.

• Elastomers and Composites
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• v.47 no.3
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• pp.188-193
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• 2012

The fatigue analysis and lifetime evaluation are very important in design procedure to assure the safety and reliability of the rubber components. Recently, the design, analysis and evaluation technology was required to achieve the high quality, fidelity, reliability of rubber products. However, rubber manufacturing companies of our country have uesd the method of trial and error and experience in the process of a compound mixing, manufacturing and improvement of rubber properties. The objectives of this study are to establish the test methods of rubber material and to make the database of rubber material properties and to evaluate the performance of rubber components and to construct the prediction system of fatigue life. Fatigue lifetime prediction methodology of the rubber component was proposed by incorporating the finite element analysis and fatigue damage parameter from fatigue test.

• Macromolecular Research
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• v.15 no.4
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• pp.315-323
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• 2007

The present manuscript deals with the prediction of the lifetime of NBR compound based rubber gaskets for use as fuel cells. The material was investigated at 120, 140 and $160^{\circ}C$, with aging times from 3 to 600 h and increasing $H_2SO_4$ concentrations of 5, 6, 7 and 10 vol%. Both material and accelerated acid-heat aging tests were carried out to predict the useful life of the NBR rubber gasket for use as a fuel cell stack. To investigate the effects of acid-heat aging on the performance characteristics of the gaskets, the properties of the NBR rubber, such as crosslink density and elongation at break, were studied. The hardness of the NBR rubber was found to decrease with decreasing acid concentration at both $120\;and\;140^{\circ}C$, but at $160^{\circ}C$, the hardness of the NBR rubber increased abruptly in a very short time at different acid concentrations. The tensile strength and elongation at break were found to decrease with increases in both the $H_2SO_4$ concentration & temperature. The observed experimental results were evaluated using the Arrhenius equation.

• Journal of the Semiconductor & Display Technology
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• v.12 no.4
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• pp.45-48
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• 2013

The electronic device, such as flat panel display (FPD), is very important in our life as a means of communication between humans. Liquid crystal display (LCD), which is categorized as a flat panel display, has been used in many display products, especially in TV industry. An LED TV is composed of several electrical components, such as liquid critical module (LCM), analog to digital convertor (AD), power supplier, and inverter board. These modules are very vulnerable to particulate contamination, and causing malfunction or visibility degradation. In this study, we developed a test method for prediction of LCM's lifetime. The test system consists of carbon particle generation flame, dilution system, test chamber, and particle concentration monitoring instrument. Since the carbon particles are the most abundant in the atmosphere and easily absorb light, soot particles are used as a challenging material for this test. The concentration of generated soot particles is set around 4,000,000 #/cc, which is 400 times higher than that of usual atmospheric particles. Through this experiment, we deduced the relationship between the dust concentration and life time of the test specimen.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.188-193
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• 2000

A more accurate life prediction for gas turbine blade takes into account the material behavior under the complex thermo-mechanical fatigue(TMF) cycles normally encountered in turbine operation. An experimental program has been carried out to address the thermo-mechanical fatigue life of the IN738LC nickel-base superalloy. In the first phase of the study, out-of-phase and in-phase TMF experiments have been performed on uncoated and coated materials. In the temperature range investigated. the deposition of NiCrAlY air plasma sprayed coating did not affect the fatigue resistance. In the second phase of the study, a physically-base life prediction model that takes into account of the contribution of different damage mechanisms has been applied. This model was able to reflect the temperature and strain rate dependences of isothermal cycling fatigue lives, and the strain-temperature history effect on the thermo-mechanical fatigue lives.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• Tribology and Lubricants
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• v.28 no.1
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• pp.19-26
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• 2012

Rolling contact fatigue of an urban railway wheel was analysed during its rolling. A FEM analysis was performed using a 3D modelling of rail and wheel, considering the slope of the rail and nonlinear isotropic and kinematic hardening behavior of the rail and the wheel. The maximum von-Mises stress and contact pressure between the rail and wheel were 656.9 MPa and 1111.4 MPa, respectively, under axial load of 85 kN with friction coefficient of 0. The fatigue initiation life prediction relationships by strain-lifetime (${\varepsilon}$-N) and Smith-Watson-Topper method were drawn for the wheel steel as follows: $N_i=7.35{\times}10^6{\times}SWT^{-3.56}$ and $N_i=5.41{\times}10^{-9}{\times}(\frac{{\Delta}{\varepsilon}}{2})^{-5.77}$. The fatigue lifetimes of the wheel due to rolling contact were determined to be infinite by ${\varepsilon}$-N and SWT methods.

• Journal of the Korea Concrete Institute
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• v.18 no.2 s.92
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• pp.233-238
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• 2006

In lifetime estimation, the FEM analysis method is proposed for predicting corrosion failure time of concrete structures exposed to sea-water. This study shows that the corrosion rate of rebar in artificial pore solution can be transferred to the corrosion rate of rebar in concrete using the relationship between pore volume and concrete volume by Jennings' model. And this study considered the pitting corrosion effects of reinforcement bar on corrosion failure analysis, rebar size to cover depth and nonlinear crack analysis. These analysis results have good accordance with the experimental results of Williamson's work. This methodology can be applied to lifetime prediction procedure of reinforced concrete structures and also gives more reasonable results of concrete cover failure time estimation of reinforced concrete structures exposed to sea-water.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.6
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• pp.719-726
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• 2009

A delay system in fuze for high explosive shell is an important safety device, but failure in the delay system usually causes failure of the shell. Root-cause analysis of failure in the delay system is required since failure in over 10-years stored delay system recently occurs. In this paper, failure in the delay system was reproduced experimentally to examine aged characteristics of the delay system, and the failed delay system shows the same characteristics as ones of failed delay systems in field. Based on the reproduced experiments, accelerated life testings and the data analysis of failure times of delay systems were performed to predict the storage lifetime.