• Journal of Bio-Environment Control
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• v.22 no.2
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• pp.108-115
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• 2013

This study was conducted to suggest a model to calculate the overall heat transfer coefficient of single layer covering for various greenhouse conditions. There was a strong correlation between cover surface temperature and inside air temperature of greenhouse. The equations to calculate the convective and radiative heat transfer coefficients proposed by Kittas were best fitted for calculation of the overall heat transfer coefficient. Because the coefficient of linear regression between the calculated and measured cover surface temperature was founded to 0.98, the slope of the straight line is 1.009 and the intercept is 0.001, the calculation model of overall heat transfer coefficient proposed by this study is acceptable. The convective heat transfer between the inner cover surface and the inside air was greater than the radiative heat transfer, and the difference increased as the wind speed rose. The convective heat transfer between the outer cover surface and the outside air was less than the radiative heat transfer for the low wind speed, but greater than for the high wind speed. The outer cover convective heat flux increased proportion to the inner cover convective heat flux linearly. The overall heat transfer coefficient increased but the cover surface temperature decreased as the wind speed increased, and the regression function was founded to be logarithmic and power function, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.3
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• pp.325-347
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• 2012

Long-term air and weather data monitored during the period of 2000 to 2009 were analyzed to quantitatively estimate the precipitation scavenging and wind dispersion contributions of ambient $PM_{10}$ and $NO_2$ in Korea. Both air pollutants and meteorological data had been respectively collected from 120 stations by the Ministry of Environment and from 20 weather stations by the Korea Meteorological Administrations in all parts of Korea. To stochastically identify the relation between a meteorological factor and an air pollutant, we initially defined the SR (scavenging ratio) and the DR (dispersion ratio) to separately calculate the precipitation and wind speed effects on the removal of a specific air pollutant. We could then estimate the OSC (overall scavenging contribution) and the ODC (overall dispersion contribution) with considering sectoral precipitation and wind speed probability density distributions independently. In this study, the SRs for both $PM_{10}$ and $NO_2$ were generally increased with increasing the amounts of precipitation and then the OSCs for $PM_{10}$ and $NO_2$ were estimated by 22.3% and 15.7% on an average in Korea, respectively. However, the trend of the DR was quite different from that of SR. The DR for $PM_{10}$ was increased with increasing wind speed up to 2.5 m/s and further the DR for $NO_2$ showed a minimum in the range of $1<WS{\leq}1.5$. The ODCs for $PM_{10}$ and $NO_2$ were estimated by 14.9% and 1.0% in Korea, respectively. Finally, we have also provided an interesting case study observed in Seoul.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.283-292
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• 2009

Considering an integral equation governing the motion of unfolding fin, an algebraic equation was acquired to get estimated minimum deployment energy required for the successful fin unfolding under the given wind condition. To complete the integration of moment, some approximations had to be introduced particularly to frictional moment and aerodynamic damping for which deployment angular speed of the unfolding fin was modelled as a function of deployment angle only with assumed profile using expected maximum angular speed. Technique for the estimation of the minimum required deployment energy was finalized by introducing the ideal deployment angular speed representing work done by the fin unfolding device alone during fin unfolding and was confirmed by comparing results from simulation with various aerodynamic conditions and profiles of the hinge torque.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.689-700
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• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

• Journal of Environmental Science International
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• v.5 no.5
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• pp.657-662
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• 1996

Meteorological and flux data measured from semiarid watersheds (Lucky Hills and Kendall) during the summer rainy and winter periods were used to study the sensitivity of the those variables used in the estimation of evapotranspiration rates. Relative sensitivity was examined to compare the importance of four meteorological and flux variables (net radiation wind speed, air temperature, and relative humidity) on Penman potential evapotranspiration (PET) estimation. The study results show that variations in Penman PET rates during the summer rainy period at both watersheds appears to be controlled by air temperature adn net radiation. During the winter period at both watersheds, wariations in Penman PET rates appears to be controlled by relative humidity and air temperature.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.89-96
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• 2011

The damping ratio as an index of bridge vibration could be considered as one of the important dynamic characteristics of a suspension bridge. But estimating of damping ratio on an existing suspension bridge under ambient vibration condition could be a laborious task. Moreover, it is not simple to directly distinguish aerodynamic damping and friction damping from apparent damping. According to previous studies, the aerodynamic damping properties can be linearly affected by wind speed level, and apparent damping ratio can be affected by amplitude of vibration. Therefore, in this article, the relationships among damping ratio, wind speed level and amplitude of acceleration were studied for separating extract aerodynamic damping and friction damping from apparent damping. Damping ratios on Sorok Bridge, a suspension bridge which is a located in Go-Heung, Korea, were estimated by two different methods as using Hilbert transform and extended Kalman filter which were well known as effective estimation methods for non-linear state. It was possible to distinguish aerodynamic damping and friction damping from apparent damping using averaged normal components of wind speed, RMQ values of acceleration, and estimated damping ratios from wind-induced vibration responses and vehicle loading responses.

• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.17-25
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• 2013

In this paper we interpreted the changes in wind field over complex mountainous terrains. The results of our study can be applied for predicting the direction of fire spread and for establishing strategies for fire prevention. The study area is bounded by $12{\times}12$ km domains of the Samcheok's long-term ecological research (LTER) site located in the east coast, in which a large-fire had occurred from 7 to 13 April 2000. Because of the area's complex topography, we compared the result of the Weather Research and Forecasting (WRF) mesoscale model with those observed by four automated weather stations. The WRF simulation overestimated the wind speed by 5 to 8 m/s (~200%) in comparison with those from four automated weather stations. The wind directions observed by the AWSs were from various directions whereas those from WRF model were mostly west wind at all stations. Overall, the simulations by the WRF mesoscale models were not appropriate for the estimation of microscale wind fields over complex mountainous areas. To overcome such inadequacy of reproducing the wind fields, we employed the ENVI-met model over Samcheok's LTER site. In order to test the model's sensitivity with the terrain effects, experimental simulations were conducted with various initial conditions. The simulation results of the ENVI-met model showed a reasonable agreement in wind speeds (about 70% accuracy) with those of the four AWSs. Also, that the variations in wind directions agreed reasonably well with changes in terrain effect. We concluded that the ENVI-met model is more appropriate in representing the microscale wind field over complex mountain terrains, which is required to predict fire spread and to establish strategies for forest fire prevention.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1324-1335
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• 2016

This article explores the speed regulation problem of permanent magnet synchronous motor (PMSM) systems subjected to unknown time-varying disturbances. A continuous sliding mode control (CSMC) technique is introduced for the speed loop to enhance the robustness of PMSM systems and eliminate the chattering phenomenon caused by high-frequency switch function in the conventional control law. However, the high control gain of the CSMC law in the presence of strong disturbances leads to large steady-state speed fluctuations for PMSM systems. In many application fields, PMSM systems are affected by time-varying disturbances instead of constant disturbances. For example, electric bicycles are usually affected by changing environmental disturbances, including wind speeds, road conditions, etc. These disturbances may be in the form of constant, ramp, and parabolic disturbances. Hence, a generalized proportional integral (GPI) observer is employed to estimate these types of disturbances. Then, the disturbance estimation method and the aforementioned CSMC method are combined to establish a composite sliding mode control method called the CSMC+GPI method for the speed loop of PMSM systems. Contrary to the conventional sliding mode control technique, the proposed method completely eliminates the chattering phenomenon caused by the switching function in the conventional control law. Moreover, a small control gain for the CSMC+GPI method is chosen by feed-forwarding estimated values to the speed controller. Hence, the steady-state speed fluctuations are small. The effectiveness of the proposed control scheme is verified by simulation and experimental result.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.4
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• pp.299-309
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• 2022

Among the fishing vessels operating in the coastal waters, jigging fishing vessels were considered representative vessels engaged only by wind, sea, tide, and external force. Then, a fishing vessel with a length of shorter than 10 m from July 1, 2018 to August 5, 2019 was studied to obtain a drift prediction model by multiple regression analysis. In the correlation analysis between variables for leeway of speed and direction, the speed and direction of tidal seem to be the most affected in coastal waters. Therefore, it should be considered an explanatory variable when conducting drift tests. As a result of multiple regression analysis on the predicted equations of leeway speed and direction due to the external force on the drift of the fishing vessel, p < 0.000 was considered significant in the F-test, but the coefficient of determination was 55.2% and 37.8%. The effect on the predicted leeway speed was in the order of the tidal speed and current speed. In addition, the impact on the predicted leeway direction was in the order of the tidal speed and current speed. ŷ(m/s) = - 0.0011(x1) + 0.9206(x2) + 0.0001(x3) + 0.0002(x4) + 0.0050(x5) + 0.0529(x6) + 0.2457 ŷ(degree) = 0.6672(x1) + 93.1699(x2) + 0.0585(x3) - 0.0244(x4) - 1.2217(x5) + 4.6378(x6) - 0.0837