• Nuclear Engineering and Technology
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• 제51권1호
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• pp.310-316
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• 2019

Fluidelastic instability is a key issue in steam generator tube bundles subjected in cross-flow. With a low flow velocity, a large amplitude vibration of the tube observed by many researchers. However, the mechanism of this vibration is seldom analyzed. In this paper, the mechanism of cross-flow effects on fluidelastic instability of tube bundles was investigated. Analysis reveals that when the system reaches the critical state, there would be two forms, with two critical velocities, and thus two expressions for the critical velocities were obtained. Fluidelastic instability experiment and numerical analysis were conducted to obtain the critical velocity. And, if system damping is small, with increases of the flow velocity, the stability behavior of tube array changes. At a certain flow velocity, the stability of tube array reaches the first critical state, a dynamic bifurcation occurs. The tube array returns to a stable state with continues to increase the flow velocity. At another certain flow velocity, the stability of tube array reaches the second critical state, another dynamic bifurcation occurs. However, if system damping is big, there is only one critical state with increases the flow velocity. Compared the results of experiments to numerical analysis, it shows a good agreement.

• 한국수소및신에너지학회논문집
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• 제32권6호
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• pp.592-611
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• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.357-373
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• 2022

The high-temperature gas-cooled reactor pebble-bed module project is the first commercial Generation-IV NPP(Nuclear Power Plant) in China. A new joint is used for the vertical support of RPV(Reactor Pressure Vessel). The steel corbel is integrally embedded into the reactor-cabin wall through eight asymmetrically arranged pre-stressed high-strength bolts, achieving the different path transmission of shear force and moment. The vertical monotonic loading test of two specimens is conducted. The results show that the failure mode of the joint is bolt fracture. There is no prominent yield stage in the whole loading process. The stress of bolts is linearly distributed along the height of corbel at initial loading. As the load increases, the height of neutral axis of bolts gradually decreases. The upper and lower edges of the wall opening contact the corbel plate to restrict the rotation of the corbel. During the loading, the pre-stress of some bolts decreases. The increase of the pre-stress strength ratio of bolts has no noticeable effect on the structure stiffness, but it reduces the ultimate bearing capacity of the joint. A simplified calculation model for the elastic stage of the joint is established, and the estimation results are in good agreement with the experimental results.

• Nuclear Engineering and Technology
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• 제54권7호
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• pp.2408-2417
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• 2022

The nanoindentation behavior of P92 steel with thermomechanical treatment under 3.5 MeV Fe¹³⁺ ion irradiation at room temperature, 400 and 700 ℃ was investigated. Pop-in behavior is observed for all the samples with and without irradiation at room temperature, while the temperature dependence of pop-in behavior is only observed in irradiated samples. The average load and penetration depth at the onset of pop-in increase as the irradiation temperature increases, in line with the results of the maximum shear stress. Irradiation induced hardening is exhibited for all irradiated samples, but there is a significant reduction in the hardness of sample irradiated at 700 ℃ in comparison to the samples irradiated at room temperature and 400 ℃. The ratio of hardness to elastic modulus for all samples decreases with increasing penetration depth except for samples at 700 ℃. With the increasing of irradiation temperature, the ratio of the irreversible work to the total work gradually decreases. In contrast, it increases for samples without irradiation.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• Steel and Composite Structures
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• 제42권6호
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• pp.733-745
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• 2022

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

• 한국산업융합학회 논문집
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• 제26권4_2호
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• pp.657-668
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• 2023

It's important to note that air compressors in the industrial sector are major energy consumers, accounting for a significant portion of total energy costs in manufacturing plants, ranging from 12% to 40%. To address this issue, researchers have compared forecasting models that can predict the power consumption of air compressors. The forecasting models were designed to incorporate variables such as flow rate, pressure, temperature, humidity, and dew point, utilizing statistical methods, machine learning, and deep learning techniques. The model performance was compared using measures such as RMSE, MAE and SMAPE. Out of the 21 models tested, the Elastic Net, a statistical method, proved to be the most effective in power comsumption forecasting.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.47-47
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• 2023

Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R²=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.80-99
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• 2023

This paper presents the results of investigating the change in welding residual stresses of the core shroud, which is one of subcomponents in reactor vessel internals, performing finite element analysis. First, the welding residual stresses of the core shroud were calculated by applying the heat conduction based lumped pass technique and finite element elastic-plastic stress analysis. Second, the temperature distribution of the core shroud during the normal operation was calculated by performing finite element temperature analysis considering gamma heating. Third, through the finite element viscoelastic-plastic stress analysis using the calculated temperature distribution and setting the calculated residual stresses as the initial stress state, the variation of the welding residual stresses was derived according to repeating the normal operation. In the viscoelastic-plastic stress analysis, the effects of neutron irradiation on mechanical properties during the cyclic normal operations were considered by using the previously developed user subroutines for the irradiation agings such as irradiation hardening/embrittlement, irradiation-induced creep, and void swelling. Finally, the effect of neutron irradiation on the welding residual stresses was analysed for each irradiation aging. As a result, it is found that as the normal operation is repeated, the welding residual stresses decrease and show insignificant magnitudes after the 10^th refueling cycle. In addition, the irradiation-induced creep/void swelling has significant mitigation effect on the residual stresses whereas the irradiation hardening/embrittlement has no effect on those.

• Nuclear Engineering and Technology
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• 제55권12호
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• pp.4647-4658
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• 2023

In this paper, elastic-plastic fracture mechanics analysis is performed to determine the critical crack sizes of the multipurpose canister (MPC) manufactured using austenitic stainless steel under dynamic loading conditions that simulate drop accidents. Firstly, dynamic finite element (FE) analysis is performed using Abaqus v.2018 with the KORAD (Korea Radioactive Waste Agency)-21 model under two drop accident conditions. Through the FE analysis, critical locations and through-thickness stress distributions in the MPC are identified, where the maximum plastic strain occurs during impact loadings. Then, the evaluation using the failure assessment diagram (FAD) is performed by postulating an external surface crack at the critical location to determine the critical crack depth. It is found that, for the drop cases considered in this paper, the principal failure mechanism for the circumferential surface crack is found to be the plastic collapse due to dominant high bending axial stress in the thickness. For axial cracks, the plastic collapse is also the dominant failure mechanism due to high membrane hoop stress, followed by the ductile tearing analysis. When incorporating the strain rate effect on yield strength and fracture toughness, the critical crack depth increases from 10 to 20%.