Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson’s ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

For the OPR1000, a standard power plant in Korea, an analytical model of the containment building considering voids and deterioration was built with multilayer shell elements. Voids were placed in the vulnerable parts of the analysis model, and the deterioration effects of concrete and rebar were reflected in the material model. To check the impact of voids and deterioration on the seismic performance of the containment building, iterative push-over analysis was performed on four cases of the analytical model with and without voids and deterioration. It was found that the effect of voids with a volume ratio of 0.6% on the seismic performance of the containment building was insignificant. The effect of strength reduction and cross-sectional area loss of reinforcement due to deterioration and the impact of strength increase of concrete due to long-term hardening offset each other, resulting in a slight increase in the lateral resistance of the containment building. To determine the limit state that adequately represents the seismic performance of the containment building considering voids and deterioration, the Ogaki shear strength equation, ASCE 43-05 low shear wall allowable lateral displacement ratio, and JEAC 4601 shear strain limit were compared and examined with the analytically derived failure point (ultimate point) in this study.

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

물질주의는 다양한 사회적 사건에 의해서 촉발될 수 있다. 본 연구에서는 물질주의를 정체성과 관련된 목표 달성 이라고 개념화하는 이론을 기반으로 사회적 배제가 자기 정체성과 관련된 욕구를 불러일으키며 이는 물질주의 수준 의 상승으로 이어질 것이라고 가정하였다. 구체적으로, 사회적 배제가 소속의 욕구와 통제의 욕구를 상승시켜 물질 주의를 상승시킬 것이라고 예상하였다. 또, 권력감의 접근 경향성을 기반으로 권력감이 높은 사람은 낮은 사람에 비해서 사회적 배제를 경험할 때 소속의 욕구와 통제의 욕구가 더 커질 것으로 예상하였다. 20 ̴30대 한국 여성 202 명을 대상으로 실험 연구를 진행한 결과, 사회적 배제는 통제 욕구의 상승을 통해서 물질주의를 상승시켰다. 또, 권 력감이 높은 사람의 경우, 권력감이 낮은 사람들보다 사회적 배제가 통제 욕구에 미치는 영향이 더 강해졌으며, 사회 적 배제가 통제 욕구를 통해 물질주의로 이어지는 경로는 권력이 높은 사람들에게만 유의했다. 사회적 배제는 소속 의 욕구를 상승시켰으나 소속의 욕구와 물질주의의 관계는 유의하지 않았고, 소속의 욕구의 매개 효과도 지지되지 않았다. 해당 결과를 기반으로 사회적 배제와 물질주의의 관계 및 연구의 한계에 대해 논의하였다.

In order to monitor the contamination of groundwater due to unplanned release of radioactive materials and the spread to off-site environments, the nuclear power plants (NPPs) conduct groundwater monitoring program (GWMP) in Korea. The GWMP should be established based on the groundwater flow model reflecting the conceptual site model (CSM) of the NPP’s site. In this study, in order to optimize the GWMP, the existing CSM and the groundwater flow model of the domestic NPPs site was updated by reflecting the latest groundwater level. As part of the CSM improvement, the hydrogeological units were subdivided more detailed from three to six through the review of hydrogeological characteristics of the NPPs site. In addition, major variables that affect groundwater flow, such as water conductivity, have been updated. The groundwater flow model was revised overall as the CSM was improved. In particular, the excavation depth of the structure and backfill area generated during the construction stage of the NPP structures was accurately reflected, and the drainage boundary conditions were realistically reflected. To verify the revised groundwater flow model, steady-state correction was performed using the groundwater level measured in April, 2021. As a results of the steady-state correction, the standard error of estimate, root mean square (RMS), normalized RMS, and the correlation coefficient were 0.32 m, 1.692 m, 5.608%, and 0.964, respectively. This means that the groundwater flow model is reasonably constructed. The CSM and groundwater flow model improved in this study will be used to optimize the monitoring location of groundwater in NPPs.

The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. Based on the methodology performed in previous studies, the welding heat source was found through experiments and FEM under the welding power conditions of three cases and the parameters of the welding heat source were analyzed according to the welding power. In this study, parameters of fiber laser welding heat source according to welding power were searched through optimization algorithm and finite element analysis, and the correlation was analyzed. It was confirmed that the concentration of the welding heat source in the 1st layer was high regardless of the welding power, and it was confirmed that the concentration of the welding heat source in the 5th layer (last layer) increased as the welding power increased. This reflects the shape of the weld bead that appears during actual fiber laser welding, and it was confirmed that this study represents the actual phenomenon.

In this paper, we studied the applicability of data driven model, AAKR(Auto-Associative Kernel Regression) for generator power loss estimation. Correlation analysis performed on 39 turbine system variables for dataset construction and then 13 variables were selected as highly correlated with generator power output. For a memory vector, 95~100% thermal power section data were used and data at normal power condition for 3 month were extracted for query vector. Analysis result shows that 9 variables show good prediction between measured and estimated data, 2 variables show good correlation but with small bias and 2 variables show increasing difference and low correlation with the passage of time, which assumed to be cause of electric output loss.

본 연구의 목적은 전후진 자동변속기가 장착된 트랙터의 변속 충격을 모사하기 위한 시뮬레이션 모델에 적용될 비례 제어 밸브의 전류 제어 모델을 개발하는 것이다. 전류 제어 모델을 개발하기 위하여 시험 장치를 구성하여 밸브의 정특성 시험과 계단 응답 시험을 수행하였으며, 시험 결과를 토대로 전류 제어 모델을 검증하였다. 비례 제어 밸브의 전류 제어 모델은 밸브의 전류-압력 선도, PID 제어기, 펄스폭변조 신호 발생기와 밸브 모델로 구성하였다. 전류 제어 모델에 사용된 데이터는 시험을 통하여 도출된 값과 대상 트랙터에 적용된 값을 사용하였다. 전류 제어 모델 검증을 위해 계단 입력 신호에 대하여 전류 특성 시험을 수행하였고, 실제 전류 시험 결과와 시뮬레이션 결과를 비교하였다. 시험 결과와 시뮬레이션 결과에 대한 전류 특성을 비교해 보면, 상승 시간 오차는 0.0074초, 첨두치 시간의 오차는 0.0065초, 오버슈트 오차는 0.06%로 나타났다. 따라서 개발된 비례 제어 밸브의 전류 제어 모델은 실제 제어기의 전류 제어 특성을 제대로 반영할 수 있으며, 추후 변속 충격 모사를 위한 트랙터 시뮬레이션 모델에서 비례 제어 밸브의 전류 제어 모델로 사용될 수 있을 것으로 판단된다.

In this paper, an experimental study was carried out for vibration control of cable bridges with structurally flexible characteristics. For the experiment on vibration control, a model bridge was constructed by reducing the Seohae Grand Bridge and the shear type MR damper was designed using the wind load response measured at Seohae Grand Bridge. The shear type MR damper was installed in the vertical direction at the middle span of the model bridge, and dynamic modeling was performed using the power model. The tests of the vibration control were carried out by non-control, passive on/off control and Lyapunov control method on model bridge with scaled wind load response. The performance of the vibration control was evaluated by calculating absolute maximum displacement, RMS displacement, absolute maximum acceleration, RMS acceleration, and size of applied power using the response (displacement, acceleration, etc.) from the model bridge. As a result, the power model was effective in simulating the nonlinear behavior of the MR damper, and the Lyapunov control method using the MR damper was able to control the vibration of the structure and reduce the size of the power supply.