Transactions of the Korean Society of Mechanical Engineers B
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v.40
no.3
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pp.181-189
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2016
An analytical model was developed by Sim to estimate the two-phase damping ratio for upward two-phase flow perpendicular to horizontal tube bundles. The parameters of two-phase flow, such as void fraction and pressure loss evaluated in the model, were calculated based on existing experimental formulations. However, it is necessary to implement a few improvements in the formulations for the case of tube bundles. For the purpose of the improved formulation, we need more information about the two-phase parameters, which can be found through experimental test. An experiment is performed with a typical normal square array of cylinders subjected to the two-phase flow of air-water in the tube bundles, to calculate the two-phase Euler number and the two-phase friction multiplier. The pitch-to-diameter ratio is 1.35 and the diameter of cylinder is 18mm. Pressure loss along the flow direction in the tube bundles is measured with a pressure transducer and data acquisition system to calculate the two-phase Euler number and the two-phase friction multiplier. The void fraction model by Feenstra et al. is used to estimate the void fraction of the two-phase flow in tube bundles. The experimental results of the two phase friction multiplier and two-phase Euler number for homogeneous and non-homogeneous two-phase flows are compared and evaluated against the analytical results given by Sim's model.
This study derived determinants of 'Decent Work' in the logistics industry and aims to use the analysis results as basic data for policymaking related to labor in the logistics industry and to prepare policies suitable for the characteristics of the logistics industry. As the dependent variable of the model, the Decent Job derived from the first study was used, and the target model was derived from panel data of whole industries to understand the unique characteristics of logistics industry jobs and applied to the logistics industry model. This study found that in the logistics industry, developing the expertise of the logistics industry through "vocational training" compared to whole industries is an important factor rather than raising the "academic level" through the regular curriculum. This seems to reflect the characteristics of the logistics industry as specialized vocational training is required in the case of "railway transportation", "inland water and port transportation", and "air cargo transportation", which have a high proportion of decent job workers among the detailed logistics industries analyzed in this study. Therefore, developing job expertise through additional manpower training programs such as vocational training as well as academic fields learned through regular curriculum is a very important factor in engaging in "Decent Work" not only in the logistics industry but also in other industries.
Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.
Journal of the Korean Institute of Landscape Architecture
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v.36
no.3
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pp.29-38
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2008
This study intends to analyze the value of school forests, one type of urban forest. For this purpose, four attributes of school forests were investigated, considering ecological, educational, social and economic values using a conjoint model as the stated preference. Based on literature reviews, the levels of the four attributes were selected, and a questionnaire survey was given to 279 urban residents divided into 2 groups: those impacted by school forests and those not. The study results suggest that the most important attribute of school forests is economic value, and next is ecological, social and educational value according to the part-worth model. The fitness level of the model is 0.900(total group) which is very significant. As for the economic value, free and 1,000 won are more critical factors than the other 2 levels, 5,000 won and 10,000 won and air pollution purification and making the school landscape are more critical factors than small habitats and microclimate factors. In addition, regarding the social value related to residents' leisure activities,the utility of nature observation is higher than walking and exercising. Finally, for educational value, understanding nature's importance is more critical than the emotions and learning of students. The estimated WTP per household/month is 3,580 won, the group related to school forestsis 3,650 won and the non-related group is 3,540 won. Based on these results, the estimated total economic value of all households per year is 6,820 hundred million won. The group related to school forests is 6,970 hundred million won and the non-related group is 6,750 hundred million won.
Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.
Journal of the Korean Data and Information Science Society
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v.26
no.6
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pp.1409-1416
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2015
The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.
Journal of the Korea Academia-Industrial cooperation Society
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v.16
no.12
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pp.8187-8194
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2015
To accelerate a heavy roller coaster train with over 1G force, a lot of thrust is required and linear synchronous motor(LSM) as propulsion method is suitable for this kind of system. To increase the propulsion efficiency of LSM, precise and real-time position information of vehicle is required for accurate phase control. However, the discontinuous position information with relatively long time interval is usually transmitted from the hall-sensors on the track every magnet length. In this paper, the basic motor model based on traditional dq-axis equations is described and the motor dynamic model is produced by considering the cogging force and friction loss. To improve the position accuracy, the position estimator is also proposed for LSM control system. Simulations were performed to check the characteristics of the torque control system which includes the position estimator based on the motor model. Simulation results based on the linearized model show that this control system has an enough bandwidth and phase margin and the executed algorithm achieves an ideal effect to follow the real-time position signal. Therefore, the feasibility of position estimator is also confirmed.
Journal of Korean Society of Coastal and Ocean Engineers
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v.24
no.4
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pp.277-286
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2012
In the numerical simulation of wave fields using a multi-phase flow model that considers simultaneous flows of materials with different states such as gas, liquid and solid, there is need of an accurate representation of the interface separating the fluids. We adopted an algebraic interface capturing method called tangent of hyperbola for interface-capturing(THINC) method for the capture of the free-surface in computations of multi-phase flow simulations instead of geometrical-type methods such a volume of fluid(VOF) method. The THINC method uses a hyperbolic tangent functions to represent the surface, and compute the numerical flux for the fluid fraction functions. One of the remarkable advantages of THINC method is its easy applicability to incorporate various numerical codes based on Navier-Stokes solver because it does not require the extra geometric reconstruction needed in most of VOF-type methods. Several tests were carried out in order to investigate the advection of interfaces and to verify the applicability of the THINC method to wave fields based on the one-field model for immiscible two-phase flows (TWOPM). The numerical results revealed that the THINC method is able to track the interface between air and water separating the fluids although its algorithm is fairly simple.
In underground spaces including nuclear waste repository, prediction of air quantity, temperature/humidity and pollutant concentration is utmost important for space construction and management during the normal state as well as for determining the measures in emergency cases such as underground fires. This study aims at developing a model for underground space environment which has capabilities to take into account the effects of autocompression for the natural ventilation head calculation, to find the optimal location and size of fans and regulators, to predict the temperature and humidity by calculating the convective heat transfer coefficient and the sensible and latent heat transfer rates, and to estimate the pollutant levels throughout the network. The temperature/humidity prediction model was applied to a military storage underground space and the relative differences of dry and wet temperatures were 1.5 ~ 2.9% and 0.6 ~ 6.1%, respectively. The convection-based pollutant transport model was applied to two different vehicle tunnels. Coefficients of turbulent diffusion due to the atmospheric turbulence were found to be 9.78 and 17.35$m^2$/s, but measurements of smoke and CO concentrations in a tunnel with high traffic density and under operation of ventilation equipment showed relative differences of 5.88 and 6.62% compared with estimates from the convection-based model. These findings indicate convection is the governing mechanism for pollutant diffusion in most of the tunnel-type spaces.
Background: Due to the emergence of new diseases such as COVID-19, an increasing number of people are struggling with stress and depression. Interest is growing in forest-based recreation for physical and mental relief. Objectives: A prediction model equation using meteorological factors and data was developed to predict the quantities of medicinal substances generated in forests (monoterpenes) in real-time. Methods: The concentration of phytoncide and meteorological factors in the forests near Chuncheon in South Korea were measured for nearly two years. Meteorological factors affecting the observation data were acquired through a multiple regression analysis. A model equation was developed by applying a linear regression equation with the main factors. Results: The linear regression analysis revealed a high explanatory power for the coefficients of determination of temperature and humidity in the coniferous forest (R2=0.7028 and R2=0.5859). With a temperature increase of 1℃, the phytoncide concentration increased by 31.7 ng/Sm3. A humidity increase of 1% led to an increase in the coniferous forest by 21.9 ng/Sm3. In the deciduous forest, the coefficients of determination of temperature and humidity had approximately 60% explanatory power (R2=0.6611 and R2=0.5893). A temperature increase of 1℃ led to an increase of approximately 9.6 ng/Sm3, and 1% humidity resulted in a change of approximately 6.9 ng/Sm3. A prediction model equation was suggested based on such meteorological factors and related equations that showed a 30% error with statistical verification. Conclusions: Follow-up research is required to reduce the prediction error. In addition, phytoncide data for each region can be acquired by applying actual regional phytoncide data and the prediction technique proposed in this study.
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