• Nuclear Engineering and Technology
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• v.30 no.4
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• pp.364-376
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• 1998

The statistical analysis method was applied to the evaluation of fracture toughness in the ductile-brittle transition temperature region. Because cleavage fracture in steel is of a statistical nature, fracture toughness data or values show a similar statistical trend. Using the three-parameter Weibull distribution, a fracture toughness vs. temperature curve (K-curve) was directly generated from a set of fracture toughness data at a selected temperature. Charpy V-notch impact energy was also used to obtain the K-curve by a $K_{IC}$ -CVN (Charpy V-notch energy) correlation. Furthermore, this method was applied to evaluate the neutron irradiation embrittlement of reactor pressure vessel (RPV) steel. Most of the fracture toughness data were within the 95% confidence limits. The prediction of a transition temperature shift by statistical analysis was compared with that from the experimental data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.501-507
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• 1997

Composite material has very excellent mechanical properties including tensile stress and specific strength. Especially impact loads may be expected in many of the engineering applications of it. The suitability of composite material for such applications is determined not only by the usual paramenters, but its impactor energy-absorbing properties. Composite material under impact load has poor mechanical behavior and so needs tailoring its structure. Genetic algorithms(GA) is probabilistic optimization technique by principle of natural genetics and natural selection and neural networks(NN) is useful for prediction operation on the basis of learned data. Therefore, This study presents optimization techniques on the basis of genetic algorithms and neural networks to minimum stiffness design of laminated composite material.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2005.11b
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• pp.245-252
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• 2005

An underground research facility (KURF) is under construction at KAERI for the in situ studies related to the validation of a HLW disposal system. For the safe construction and long-term researches at KURF, mechanical stability of the facility should be evaluated. In this study, 3D mechanical stability analysis using the rock mass properties determined from various in situ as well as laboratory tests was carried out. From the analysis, it was possible to predict the rock deformation, stress concentration, and plastic zone developed before and after the excavation. A test blasting was performed to characterize the site dependent dynamic response, which can be used for the prediction of the blasting impact on the facilities in KAERI.

• Nuclear Engineering and Technology
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• v.35 no.2
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• pp.154-164
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• 2003

The explicit consideration of different pressurization rates in estimating the probabilities of containment failure modes has a profound effect on the confidence of containment performance evaluation that is so critical for risk assessment of nuclear power plants. Except for the sophisticated NUREG-1150 study, many of the recent containment performance analyses (through Level 2 PSAs or IPE back-end analyses) did not take into account an explicit distinction between slow and fast pressurization in their analyses. A careful investigation of both approaches shows that many of the approaches adopted in the recent containment performance analyses exactly correspond to the NUREG-1150 approach for the prediction of containment failure mode probabilities in the presence of fast pressurization. As a result, it was expected that the existing containment performance analysis results would be subjected to greater or less conservatism in light of the ultimate failure mode of the containment. The main purpose of this paper is to assess potential conservatism of a plant-specific containment performance analysis result in light of containment failure mode probabilities.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.3940-3955
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• 2023

A new method of numerical simulating prediction and decontamination effect evaluation for abrasive jet decontamination to radioactively contaminated metal is proposed. Based on the Computational Fluid Dynamics and Discrete Element Model (CFD-DEM) coupled simulation model, the motion patterns and distribution of abrasives can be predicted, and the decontamination effect can be evaluated by image processing and recognition technology. The impact of three key parameters (impact distance, inlet pressure, abrasive mass flow rate) on the decontamination effect is revealed. Moreover, here are experiments of reliability verification to decontamination effect and numerical simulation methods that has been conducted. The results show that: ⁶⁰Co and other homogeneous solid solution radioactive pollutants can be removed by abrasive jet, and the average removal rate of Co exceeds 80%. It is reliable for the proposed numerical simulation and evaluation method because of the well goodness of fit between predicted value and actual values: The predicted values and actual values of the abrasive distribution diameter are Ф57 and Ф55; the total coverage rate is 26.42% and 23.50%; the average impact velocity is 81.73 m/s and 78.00 m/s. Further analysis shows that the impact distance has a significant impact on the distribution of abrasive particles on the target surface, the coverage rate of the core area increases at first, and then decreases with the increase of the impact distance of the nozzle, which reach a maximum of 14.44% at 300 mm. It is recommended to set the impact distance around 300 mm, because at this time the core area coverage of the abrasive is the largest and the impact velocity is stable at the highest speed of 81.94 m/s. The impact of the nozzle inlet pressure on the decontamination effect mainly affects the impact kinetic energy of the abrasive and has little impact on the distribution. The greater the inlet pressure, the greater the impact kinetic energy, and the stronger the decontamination ability of the abrasive. But in return, the energy consumption is higher, too. For the decontamination of radioactively contaminated metals, it is recommended to set the inlet pressure of the nozzle at around 0.6 MPa. Because most of the Co elements can be removed under this pressure. Increasing the mass and flow of abrasives appropriately can enhance the decontamination effectiveness. The total mass of abrasives per unit decontamination area is suggested to be 50 g because the core area coverage rate of the abrasive is relatively large under this condition; and the nozzle wear extent is acceptable.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.241-257
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• 2020

The rapid proliferation of oil/gas drilling and wind turbine installations with jack-up rig-formed structures increases structural safety requirements, due to the greater risks of operational collisions during use of these structures. Therefore, current industrial practices and regulations have tended to increase the required accidental collision design loads (impact energies) for jack-up rigs. However, the existing simplified design approach tends to be limited to the design and prediction of local members due to the difficulty in applying the increased uniform impact energy to a brace member without regard for the member's position. It is therefore necessary to define accidental load estimation in terms of a reasonable collision scenario and its application to the structural response analysis. We found by a collision probabilistic approach that the kinetic energy ranged from a minimum of 9 MJ to a maximum 1049 MJ. Only 6% of these values are less than the 35 MJ recommendation of DNV-GL (2013). This study assumed and applied a representative design load of 196.2 MN for an impact load of 20,000 tons. Based on this design load, the detailed design of a leg structure was numerically verified via an FE analysis comprising three categories: linear analysis, buckling analysis and progressive collapse analysis. Based on the numerical results from this analysis, it was possible to predict the collapse mode and position of each member in relation to the collision load. This study provided a collision strength assessment between attendant vessels and a jack-up rig based on probabilistic collision scenarios and nonlinear structural analysis. The numerical results of this study also afforded reasonable evaluation criteria and specific evaluation procedures.

• Advances in concrete construction
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• v.13 no.2
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• pp.123-132
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• 2022

This paper aims to adapt Multilinear regression (MLR) to predict the strength and toughness of SIFCON containing various pozzolanic materials. Slurry Infiltrated Fibrous Concrete (SIFCON) is one of the most common terms used in concrete manufacturing, known for its benefits such as high ductility, toughness and high ultimate strength. Assessment of compressive strength (CS.), flexural strength (F.S.), splitting tensile strength (STS), dynamic elasticity modulus (DME) and impact energy (I.E.) using the experimental approach is too costly. It is time-consuming, and a slight error can lead to a repeat of the test and, to solve this, alternative methods are used to predict the strength and toughness properties of SIFCON. In the present study, the experimentally investigated SIFCON data about various mix proportions are used to predict the strength and toughness properties using regression analysis-multilinear regression (MLR) models. The input parameters used in regression models are cement, fibre, fly ash, Metakaolin, fine aggregate, blast furnace slag, bottom ash, water-cement ratio, and the strength and toughness properties of SIFCON at 28 days is the output parameter. The models are developed and validated using data obtained from the experimental investigation. The investigations were done on 36 SIFCON mixes, and specimens were cast and tested after 28 days of curing. The MLR model yields correlation between predicted and actual values of the compressive strength (C.S.), flexural strength, splitting tensile strength, dynamic modulus of elasticity and impact energy. R-squared values for the relationship between observed and predicted compressive strength are 0.9548, flexural strength 0.9058, split tensile strength 0.9047, dynamic modulus of elasticity 0.8611 for impact energy 0.8366. This examination shows that the MLR model can predict the strength and toughness properties of SIFCON.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Journal of the Korean institute of surface engineering
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• v.43 no.3
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• pp.159-164
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• 2010

Surface roughness of deposited or etched film strongly depends on ion bombardment. Relationships between ion bombardment variables and surface roughness are too complicated to model analytically. To overcome this, an empirical neural network model was constructed and applied to a deposition process of silicon nitride (SiN) films. The films were deposited by using a pulsed plasma enhanced chemical vapor deposition system in $SiH_4$-$NH_4$ plasma. Radio frequency source power and duty ratio were varied in the range of 200-800 W and 40-100%. A total of 20 experiments were conducted. A non-invasive ion energy analyzer was used to collect ion energy distribution. The diagnostic variables examined include high (or) low ion energy and high (or low) ion energy flux. Mean surface roughness was measured by using atomic force microscopy. A neural network model relating the diagnostic variables to the surface roughness was constructed and its prediction performance was optimized by using a genetic algorithm. The optimized model yielded an improved performance of about 58% over statistical regression model. The model revealed very interesting features useful for optimization of surface roughness. This includes a reduction in surface roughness either by an increase in ion energy flux at lower ion energy or by an increase in higher ion energy at lower ion energy flux.