• Journal of Environmental Science International
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• v.29 no.12
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• pp.1223-1237
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• 2020

This study focused on comparing the meteorological conditions in the Atmospheric Boundary Layer (ABL) on high-event days and non-event days in the Seoul Metropolitan Area (SMA). We utilized observed PM10 and meteorological variables at the surface as well as at the upper heights. The results showed that high-event days were consistently associated with lower wind speed, whereas wind direction showed no particular difference between high-event and non-event days with frequent westerlies and northwesterlies for both cases. During high-event days, the temperature was much warmer than the monthly normal values with a sharp increasing trend, and Relative Humidity (RH) was higher than the monthly normal, especially on high-event days in February. During high-event days in spring, a double inversion layer was present at surface and upper heights. This indicates that stability in the multi-layer is an important indicator of higher PM10 concentrations. Net radiation in spring and winter is also closely associated with higher PM10 concentrations. Strong net radiation resulted in large sensible heat, which in turn facilitated a deeper mixing height with diluted PM10 concentrations; in contrast, PM10 concentrations were higher when sensible heat in spring and winter was very low. We also confirmed that convective and friction velocity was higher on non-event days than on high-event days, and this was especially obvious in spring and winter. This indicated that thermal turbulence was dominant in spring, whereas in winter, mechanical turbulence was dominant over the SMA.

• Safety and Health at Work
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• v.12 no.1
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• pp.119-126
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• 2021

Background: During the period 2001 to 2016, the maximum temperatures in Thailand rose from 38-41℃ to 42-44℃. The current occupational heat exposure standard of Thailand issued in 2006 is based on wet bulb globe temperature (WBGT) defined for three workload levels without a work-rest regimen. This study examined whether the present standard still protects most workers. Methods: The sample comprised 168 heat acclimatized workers (90 in construction sites, 78 in foundries). Heart rate and auditory canal temperature were recorded continuously for 2 hours. Workplace WBGT, relative humidity, and wind velocity were monitored, and the participants' workloads were estimated. Heat-related symptoms and signs were collected by a questionnaire. Results: Only 55% of the participants worked in workplaces complying with the heat standard. Of them, 79% had auditory canal temperature ≤ 38.5℃, compared with only 58% in noncompliant workplaces. 18% and 43% of the workers in compliant and noncompliant workplaces, respectively, had symptoms from heat stress, the trend being similar across all workload levels. An increase of one degree (C) in WBGT was associated with a 1.85-fold increase (95% confidence interval: 1.44-2.48) in odds for having symptoms. Conclusion: Compliance with the current occupational heat standard protects 4/5 of the workers, whereas noncompliance reduces this proportion to one half. The reasons for noncompliance include the gaps and ambiguities in the law. The law should specify work/rest schedules; outdoor work should be identified as an occupational heat hazard; and the staff should include occupational personnel to manage heat stress in establishments involving heat exposure.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.111-120
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• 2021

A tractor is an agricultural machine that performs farm work, such as cultivation, soil preparation, loading, bailing, and transporting, through attached working implements. Farm work must be carried out on time per the growing season of crops. As a result, the reliability of a tractor's transmission is vital. Ideally, the transmission's design should reflect the actual load during agricultural work; however, configuring such a measurement system is time- and cost-intensive. The design and analysis of a transmission are, therefore, mainly performed by empirical methods. In this study, a tractor with a measurement system was used to measure the actual working load in the field. Its hydro-mechanical transmission was then analyzed using the measured load. It was found that the velocity factor, load distribution factor, lubrication factor, roughness factor, relative notch sensitivity factor, and life factor affect the gear strength of the transmission. Also, loading conditions have a significant influence on the reliability of the transmission. It is believed that transmission reliability can be enhanced by analyzing the actual load on the transmission, as performed in this study.

• Structural Engineering and Mechanics
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• v.81 no.4
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• pp.411-428
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• 2022

This paper presents the Campbell diagram analysis of the rotordynamic system using the full order model (FOM) and the reduced order model (ROM) techniques to determine the critical speeds, identify the stability and reduce the computational time. Due to the spin-speed-dependent matrices (e.g., centrifugal stiffening matrix), several model order reduction (MOR) techniques may be considered, such as the modal superposition (MS) method and the Krylov subspace-based MOR techniques (e.g., Ritz vector (RV), quasi-static Ritz vector (QSRV), multifrequency quasi-static Ritz vector (MQSRV), multifrequency/ multi-spin-speed quasi-static Ritz vector (MMQSRV) and the combined Ritz vector & modal superposition (RV+MS) methods). The proposed MMQSRV method in this study is extended from the MQSRV method by incorporating the rotational-speed-dependent stiffness matrices into the Krylov subspace during the MOR process. Thus, the objective of this note is to respond to the question of whether to use the MS method or the Krylov subspace-based MOR technique in establishing the Campbell diagram of the shaft-disc-blade assembly systems in three-dimensional (3D) finite element analysis (FEA). The Campbell diagrams produced by the FOM and various MOR methods are presented and discussed thoroughly by computing the norm of relative errors (ER). It is found that the RV and the MS methods are dominant at low and high rotating speeds, respectively. More precisely, as the spinning velocity becomes large, the calculated ER produced by the RV method is significantly increased; in contrast, the ER produced by the MS method is smaller and more consistent. From a computational point of view, the MORs have substantially reduced the time computing considerably compared to the FOM. Additionally, the verification of the 3D FE rotordynamic model is also provided and found to be in close agreement with the existing solutions.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.1011-1019
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• 2021

This study presents numerical simulations of submerged jump and washed-out jump resulted from the flow over the embankment type weir. Unsteady Reynolds Averaged Navier-Stokes (URANS) equations are solved with the k-𝜔 SST turbulence model. Validations are carried out using the experimental results in the literature, revealing that computed roller shape, free surface, and mean velocity are in good agreement with measured data. The volume fractions of water of the submerged jump and washed-out jump are compared, and the characteristics of the two flows from the double-averaged volume fractions of water are presented. The condition under which the transition occurs from the submerged jump to washed-out jump is presented by the relation between the relative embankment length and submergence factor via numerical simulations by changing the weir length, discharge, and tailwater depth.

• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.27-40
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• 2024

The prediction of VIV amplitude is essential for the design and fatigue life estimation of steel tubes in tubular transmission towers. Limited to costly and time-consuming traditional experimental and computational fluid dynamics (CFD) methods, a machine learning (ML)-based method is proposed to efficiently predict the VIV amplitude of steel tubes in transmission towers. Firstly, by introducing the first-order mode shape to the two-dimensional CFD method, a simplified response analysis method (SRAM) is presented to calculate the VIV amplitude of steel tubes in transmission towers, which enables to build a dataset for training ML models. Then, by taking mass ratio M^*, damping ratio ξ, and reduced velocity U^* as the input variables, a Kriging-based prediction method (KPM) is further proposed to estimate the VIV amplitude of steel tubes in transmission towers by combining the SRAM with the Kriging-based ML model. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by using three full-scale steel tubes with C-shaped, Cross-shaped, and Flange-plate joints, respectively. The results show that the SRAM can reasonably calculate the VIV amplitude, in which the relative errors of VIV maximum amplitude in three examples are less than 6%. Meanwhile, the KPM can well predict the VIV amplitude of steel tubes in transmission towers within the studied range of M^*, ξ and U^*. Particularly, the KPM presents an excellent capability in estimating the VIV maximum amplitude by using the reduced damping parameter S_G.

• Geophysics and Geophysical Exploration
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• v.13 no.3
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• pp.243-248
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• 2010

Two suspicious events, which were claimed as underground nuclear tests by North Korea, were detected in the northern Korean Peninsula on October 9, 2006 and May 25, 2009. The KIGAM and Korea-China Joint seismic stations are distributed uniformly along the boundaries between North Korea and adjacent countries. In this study, the data from broadband stations with the distance of 200 to 550 km from the test site are used to analyze and compare two nuclear tests of North Korea. By comparing the time differences of the Pn-wave arrival times of 1st and 2nd tests at multiple stations, the relative locations of two test sites could be calculated precisely. From the geometrical calculation with the velocity of Pn wave $V_{Pn}$ = 8 km/s, the 2nd test site is estimated to move in the WNW direction from 1st one with the distance of 2 km. Body wave magnitude, mb of the 2nd test, which was announced officially as the network average of 4.5, varies widely with the directional location of stations from 4.1 to 5.2. The magnitude obtained from Lg wave, $m_b$(Lg), shows less variation between 4.3 to 4.7 with the average of 4.6. The moving-window spectra of time traces of 1st and 2nd tests show very similar pattern with different scale level. In addition, the corner frequencies of P wave of 1st and 2nd tests at each station show no or negligible difference. This indicates the burial depths of two tests might be very similar. The relative yield amount of the 2nd test is estimated 8 times larger than that of the 1st from the weighted average of ground-velocity amplitude ratios.

• Journal of Bio-Environment Control
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• v.15 no.4
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• pp.296-305
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• 2006

The heterogeneity of crop transpiration is important to clearly understand the microclimate mechanisms and to efficiently handle the water resource in greenhouses. A computational fluid dynamic program (Fluent CFD version 6.2) was developed to study the internal climate and crop transpiration distributions of greenhouses. Additionally, the global solar radiation model and a crop heat exchange model were programmed together. Those models programmed using $C^{++}$ software were connected to the CFD main module using the user define function (UDF) technology. For the developed CFD validity, a field experiment was conducted at a $17{\times}6 m^2$ plastic-covered mechanically ventilated single-span greenhouse located at Pusan in Korea. The CFD internal distributions of air temperature, relative humidity, and air velocity at 1m height were validated against the experimental results. The CFD computed results were in close agreement with the measured distributions of the air temperature, relative humidity, and air velocity along the greenhouse. The averaged errors of their CFD computed results were 2.2%,2.1%, and 7.7%, respectively.