Welding is one of representative manufacturing processes in the industrial field. Cryogenic storage containers are also manufactured through welding, and conversion to laser welding is issue in the field due to many advantages. Since welding causes thermal-elastic deformation, design considering distortion is required. Prediction of distortion through FEM is essential, but laser welding has difficulties in the field because there is no representative heat source model. The author presented the model that can cover various models using a multi-layer heat source model in previous studies. However the previous study has a limitation which is a welding heat source model must be derived after performing bead on plate welding. Thus this study was attempted to estimate the welding heat source parameters by comparing the shape of bead under various conditions. First, the difference between penetration shape and welding heat source parameters according to welding power was analyzed. The radius of the welding heat source increased according to the welding power, and the depth of the welding heat source also increased. The correlation between the penetration shape and the welding heat source parameter appears at a similar rate, however the follow-up research is necessary with more model data.

In this study, the positions of Cs-137 gamma ray source are estimated from the plastic scintillating fiber bundle sensor with length of 5 m, using machine learning data analysis. Seven strands of plastic scintillating fibers are bundled by black shrink tube and two photomultiplier tubes are used as a gamma ray sensing and light measuring devices, respectively. The dose rate of Cs-137 used in this study is 6 μSv·h−1. For the machine learning modeling, Keras framework in a Python environment is used. The algorithm chosen to construct machine learning model is regression with 15,000 number of nodes in each hidden layer. The pulse-shaped signals measured by photomultiplier tubes are saved as discrete digits and each pulse data consists of 1,024 number of them. Measurements are conducted separately to create machine learning data used in training and test processes. Measurement times were different for obtaining training and test data which were 1 minute and 5 seconds, respectively. It is because sufficient number of data are needed in case of training data, while the measurement time of test data implies the actual measuring time. The machine learning model is designated to estimate the source positions using the information about time difference of the pulses which are created simultaneously by the interaction of gamma ray and plastic scintillating fiber sensor. To evaluate whether the double-trained machine learning model shows enhancement in accuracy of source position estimation, the reference model is constructed using training data with one-time learning process. The double-trained machine learning model is designed to construct first model and create a second training data using the training error and predetermined coefficient. The second training data are used to construct a final model. Both reference model and double-trained models constructed with different coefficients are evaluated with test data. The evaluation result shows that the average values calculated for all measured position in each model are different from 7.21 to 1.44 cm. As a result, by constructing the double-trained machine learning model, the final accuracy shows 80% of improvement ratio. Further study will be conducted to evaluate whether the double-trained machine learning model is applicable to other data obtained from measurement of gamma ray sources with different energy and set a methodology to find optimal coefficient.

PURPOSES : Steel deck bridges are the preferred structural type for reducing dead load, and the use of thin-layer asphalt concrete with excellent adhesion to the steel deck and excellent deformation followability is increasing for bridge pavements. However, because these materials are constructed at a high temperature of 240 °C or higher to maintain high fluidity during construction, excessive thermal deformation and stress may be temporarily induced in the steel deck. Therefore, the stability of the structure must be assessed by considering the environmental conditions of the site during pavement construction. Herein, a method is presented for estimating the heat source equation, in which conduction and convection effects are removed using temperature measurement data, for modeling U-rib using plate elements. The validity of the study is assessed by deriving the equivalent heat source equation using the temperature data measured from the underside of the steel deck while constructing a 40-mm-thick goose asphalt concrete pavement layer on a 12-mm-thick steel deck. In addition, the practicality is verified by performing heat transfer and thermal stress analyses. METHODS : By comparing the temperature data measured during the construction of high-fluidity asphalt concrete with the results of repeated heat transfer numerical analysis, heat source data without field conduction and convection conditions are obtained. Subsequently, a heat source equation suitable for the heat source data is derived using the least-squares method. RESULTS : The results of the heat transfer analysis using the equivalent heat source equation calculated using the presented method are almost consistent with the measured temperature data. In addition, the behavioral characteristics of the structure that matches the behavior of the actual structure can be derived through thermal stress analysis, which considers heat conduction and convection to adjacent members. CONCLUSIONS : Even when the steel deck and U-rib member are modeled as plate elements, thermal effect analysis can be performed reasonably while considering field conditions.

The estimation of heat source model is very important for heat transfer analysis with finite element method. Part I of this study used adaptive simulated annealing which is one of the global optimization algorithm for anticipating the parameters of the Goldak model. Although the analysis with 3D model which depicted the real situation produced the correct answer, that took too much time with moving heat source model based on Fortran and Abaqus. This research suggests the procedure which can reduce time with maintaining quality of analysis. The lead time with 2D model is reduced by 90% comparing that of 3D model, the temperature distribution is similar to each other. That is based on the saturation of heat transfer among the direction of heat source movement. Adaptive simulated annealing with 2D model can be used to estimate more proper heat source model and which could enhance to reduce the resources and time for experiments.

Anticipation of welding deformation with finite element method is a very interested topic in the industries, adequate heat source model is essential for concluding reasonable results. This study is related to estimate the parameters of Goldak heat source model, and global optimization algorithm is applied to this research. The heat affected zone (HAZ) boundary line of bead on plate (BOP) welding is used as the target, parameters of heat sources are used as the variables. Adaptive simulated annealing is applied and the optimal result is obtained out of 1,000 candidates. The convergence of finite element method and the global optimization is meaningful for estimation of welding deformation, which could enhance to reduce the resources and time for experiments.

The stochastic point-source model has been widely used in generating artificial ground motions, which can be used to develop a ground motion prediction equation and to evaluate the seismic risk of structures. This model mainly consists of three different functions representing source, path, and site effects. The path effect is used to emulate decay in ground motion in accordance with distance from the source. In the stochastic point-source model, the path attenuation effect is taken into account by using the geometrical attenuation effect and the inelastic attenuation effect. The aim of this study is to develop accurate equations of ground motion attenuation in the Korean peninsula. In this study, attenuation was estimated and validated by using a stochastic point source model and observed ground motion recordings for the Korean peninsula.

In the study, public facilities in Korea covered by the law, including PC-rooms, child care facilities, bus terminalwaiting rooms, elderly nursing facilities, movie theaters, underground subway stations, super super markets andindoor parking lots (8 types of facility, for a total of 32 locations) were investigated for indoor gas phase PAHsand particulate phase PAHs. PAHs source profiles were investigated as well. Finally, public facilities PAHs wereestimated the main influencing factors and sources of indoor by factor analysis. Underground subway stations andPC-rooms tended to be higher the concentration than other facilities. It judged each the effects of car exhaust,smoking, and elderly nursing facilities, child care facilities, movie theaters, where the influence of the outdoor airis less relatively direct effect that car exhaust and incoming of ambient air, were showed low concentration. Supersuper markets displayed a large amount of different products and bus terminal waiting rooms influenced car exhaustis higher than those that. Sources of indoor PAHs in public facilities make out profiling(cooking process: broilingmeat and fish, incense, shampoo, decorative candles, tobacco) and on the effects of ambient on reported existingliterature(of diesel and gasoline engines, heating fuel, coke oven, a wood combustion) was referred for factor analysisto estimate emission sources. As a result of particulate PAHs phase, three major factors were showed that factor1: cooking, use of gas fuel and combustion devices, factor 2: smoking. Factor 3: car exhaust. Factor analysis resultsof gas PAHs phase are similar to particulate PAHs phase. Additionally, factors such as air fresheners was estimated.

2009년말 기준으로 11,811 다발의 경수로 사용후핵연료가 방출되었으며, 지금까지 각 사용후핵연료에 대해 방사선원항을 가중하여 설계에 반영하기는 사실상 불가능하여, 원자력 관련시설 설계시 보수성을 갖는 기준 사용후핵연료를 선정하고 이를 바탕으로 시스템 설계를 수행하여 왔다. 방사선원항에 대한 단순모델을 적용하면 각 사용후핵연료에 대한 방사선원항을 가중함으로써 이와 같은 보수성을 배제할 수 있으므로 본 연구에서 웨스팅하우스형 원전에 사용된 사용후핵연료를 대상으로 방사선원항, 즉, 붕괴열, 방사능세기, 섭취위해도 등을 예측하기 위한 회귀모형을 개발하였다. 개발된 회귀식을 통해 예측된 방사선원항값은 ORIGEN-ARP 코드로 계산된 값과 약 5% 이내에서 잘 일치함을 확인하였으며, 이의 유용성을 검토한 결과 각각의 사용후핵연료에 대한 방사선원항을 가중하여 설계에 반영하면 보수성을 줄일 수 있음을 확인하였다. 따라서 본 연구에서 개발된 회귀식은 사용후핵연료의 저장 및 처분과 관련한 원자력시설 설계시 개념설계 단계에서 유용하게 사용될 수 있을 것으로 판단된다.

강바닥판 포장에 사용될 수 있는 특수아스팔트 중의 하나인 구스아스팔트는 의 고온 상태에서 시공되기 때문에 강바닥판에 예상하지 못한 열응력 및 열변형을 발생시킬 수 있다. 따라서 구스아스팔트의 타설 중에 강바닥판에 미치는 열영향을 시공조건을 고려하여 사전에 평가하고 그 영향의 최소화를 위해서는 열전달 및 열응력 수치해석을 실시하여야 하지만 구조해석에서 주로 사용되는 평판/보요소의 특성상 3차원 구조해석 모델에서 구현하기가 매우 어렵다. 본 연구에서는 강바닥판 교량의 열영향해석을 위하여 일반적인 구조해석모델에 직접 적용할 수 있는 등가열원(EHS) 산정방법을 제안하였다. 강바닥판 교량의 구스아스팔트에 의한 열영향을 정확히 평가하기 위하여 (1) 기존의 실험결과를 이용하여 열전달해석에 필요한 물리량을 검증하고, (2) 정밀해석을 통해 3차원 교량모델에 적합한 등가열원을 산정하였으며, (3) 이를 해석모델에 적용하여 산정한 등가열원에 의한 수치해석방법의 타당성을 검증하였다. 본 연구에서 제안된 등가열원은 실제 강교량의 3차원 열전달 및 열응력 해석에 즉각 활용될 수 있으며, 등가열원산정기법은 용접잔류응력해석, 교량의 화재 해석 등 열영향을 받는 다른 공학적 해석에 응용될 수 있을 것으로 기대된다.

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

In this study, we investigated the characteristics of Volatile Organic Compounds(VOCs) emission from painting and printing facilities in the Pyeongdong industrial complex in Gwangju. In addition, the objective was to understand the distribution characteristics of VOCs in the ambient air in industrial complexes affected by painting and printing facilities. The painting facility mainly emitted toluene, acetone, butyl acetate, 4-methyl-2-pentanone, ethyl acetate, 1-butanol, methyl ethyl ketone, m,p-xylene, o-xylene, 4-ethyltoluene, ethylbenzene, 3-ethyltoluene, and 1,2,4-trimethylbenzene. The main emission components in printing facilities were methyl ketone, ethyl acetate, acetone, 2-propanol, toluene, heptane, and butyl acetate. Ethyl acetate, toluene, 2-butanone, acetone, butyl acetate, 2-propanol, xylenes, and 4-methyl-2-pentanone were detected in the ambient air of the Pyeongdong industrial complex, consistent with the VOCs emitted by painting and printing facilities. The average concentration of seasonal TVOCs followed an order of winter > fall > spring > summer, whereas the concentrations of daytime and nighttime TVOCs were generally higher at night than those during the day, and the wind speed was greater during the day than it was at night. Based on a factor analysis of VOCs in the ambient air of Pyeongdong industrial complex, it is considered that organic solvents used in coating, printing, and electronics manufacturing facilities, as well as diesel vehicle emissions played a major role.

This study was conducted to investigate the distribution characteristics, source identification, and health risk of polycyclic aromatic hydrocarbons (PAHs) present in particulate matter 10 (PM-10), in Gwangju. PM-10 samples were collected from September 2021 to August 2022 from three sampling sites, one located in each of the following areas: green, residential, and industrial. The average concentrations of PAHs were found to be higher in the industrial area (9.75±6.51 ng/㎥) than in the green (6.90±2.41 ng/㎥) and residential (6.74±2.38 ng/㎥) areas. Throughout the year and across all sites, five-ring PAHs accounted for the largest proportion (29.8–34.5%) of all PAHs. The concentrations of PAHs showed distinct seasonal variations, with the highest concentration observed in winter, followed by autumn, spring, and summer. Source apportionment analyses were performed using diagnostic ratios and principal component analyses, which indicated that coal/biomass combustion and vehicle emissions were the primary sources of PAHs in PM-10. The incremental lifetime cancer risk was estimated for all age groups at all sampling sites, and the results revealed a much lower risk level than the standard acceptable risk level (1×10-6).

Industrial emissions, mainly from industrial complexes, are important sources of ambient Volatile Organic Compounds (VOCs). Identification of the significant VOC sources from industrial complexes has practical significance for emission reduction. VOC samples were collected from July 2019 to June 2020. A Positive Matrix Factorization (PMF) receptor model was used to evaluate the VOC sources in the area. Four sources were identified by PMF analysis, including coating-1, coating-2, printing, and vehicle exhaust. The coating-1 source was revealed to have the highest contribution (41.5%), followed by coating-2 (23.9%), printing (23.1%), and vehicle exhaust (11.6%). The source showing the highest contribution was coating emissions, originating from the northwest to southwest of the sample site. It also relates to facilities that produce auto parts. The major components of VOC emissions from the coating facilities were toluene, m,p-xylene, ethylbenzene, o-xylene, and butyl acetate. Industrial emissions should be the top priority to meet the relevant control criteria, followed by vehicular emissions. This study provides a strategy for VOC source apportionment from an industrial complex, which is helpful in the development of targeted control strategies.