• Composites Research
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• v.29 no.1
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• pp.16-23
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• 2016

In this paper, numerical results of sectional analysis, stress recovery and energy release rate were compared with the results of VABS, 3-D FEM through the blade analysis library. The result of stress recovery analysis for one-dimensional model including the stiffness matrix is compared with stress results of three-dimensional FEM. We discuss the configuration of the blade analysis library and compare verifications of numerical analysis results of VABS. Blade analysis library through dimensional reduction and stress recovery is intended to be utilized in conjunction with pre- and post-processing of the analysis program of the composite blade, high-altitude uav's wing, wind blades and tilt rotor blade.

• Structural Engineering and Mechanics
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• v.40 no.1
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• pp.85-103
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• 2011

Integration of finite element analysis (FEA) software into various software platforms is commonly used in coupling systems such as systems involving structural control, fluid-structure, wind-structure, soil-structure interactions and substructure method in which FEA is used for simulating the structural responses. Integrating an FEA program into various other software platforms in an efficient and simple way is crucial for the development and performance of the entire coupling system. The lack of simplicity of the existing integration methods makes this integration difficult and therefore entails the motivation of this study. In this paper, a novel practical technique, namely CS technique, is presented for integrating a general FEA software framework OpenSees into other software platforms, e.g., Matlab-$Simulink^{(R)}$ and a soil-structure interaction (SSI) system. The advantage of this integration technique is that it is efficient and relatively easy to implement. Instead of OpenSees, a cheap client handling TCL is integrated into the other software. The integration is achieved by extending the concept of internet based client-server concept, taking advantage of the parameterization framework of OpenSees, and using a command-driven scripting language called tool command language (TCL) on which the OpenSees' interface is based. There is no need for any programming inside OpenSees. The presented CS technique proves as an excellent solution for the coupling problems mentioned above (for both linear and nonlinear problems). Application examples are provided to validate the integration method and illustrate the various uses of the method in the civil engineering.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.1
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• pp.13-21
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• 2020

Surrogate models have been used for the rapid estimation of six-DOF aerodynamic coefficients in the context of the design and control of a missile. For this end, we may generate highly accurate surrogate models with a multitude of aerodynamic data obtained from wind tunnel tests (WTTs); however, this approach is time-consuming and expensive. Thus, we aim to swiftly predict aerodynamic coefficients via co-Kriging using a few WTT data along with plenty of computational fluid dynamics (CFD) data. To demonstrate the excellence of co-Kriging models based on both WTT and CFD data, we first generated two surrogate models: co-Kriging models with CFD data and Kriging models without the CFD data. Afterwards, we carried out numerical validation and examined predictive trends to compare the two different surrogate models. As a result, we found that the co-Kriging models produced more accurate aerodynamic coefficients than the Kriging models thanks to the assistance of CFD data.

• Smart Media Journal
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• v.8 no.4
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• pp.38-45
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• 2019

When there is a spray flow such as from a pesticide nozzle, winds affect the droplet flow of a rotary-wing drone accompanied by a strong wake, with a severe oscillation. Especially, during forwarding flights or when winds come from the side, compare to a simple hovering flight as the droplet is in the effect of aerodynamic drag force, the effect of spraying region becomes even larger. For this reason, the spraying of pesticides using drones may cause a greater risk of scattering or a difference in droplet dispersion between locations, resulting in a decrease in efficiency. Therefore, through proper numerical modeling and its applied simulation, an indication tool is required applicable for the various flight and atmospheric conditions. In this research, we completed both experiment and numerical analysis for the strong downwash from the rotor and flight velocity of the drone by comparing the probability density function of droplet distribution to build a spraying system that can improve the efficiency when spraying droplets in the pesticide spray drone.

• 한국해양학회지
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• v.29 no.2
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• pp.83-94
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• 1994

Two-dimensional tidal and tidal residual currents in Chinhae Bay are investigated by field observations, hydraulic and numerical experiments. The results of hydraulic and numerical model experiments roughly coincide with the field measurements. Maximum tidal currents during the spring and neap tides in Kaduk and Kyunnaeryang channel and the central channel of Chinhae Bay are strong as 90 to 110 and 30 to 40 cm/s respectively, and strong tidal residual currents having numerous eddies take place. Maximum tidal currents during the spring and neap tides in the western and northern parts of the bay are weak as below 30 and 10 cm/s respectively, and also tidal residual currents are relatively weak. Tidal residual currents in the northern part of Kajo-do go toward the north, whereas the currents in the southern part move down the bay, and the currents rotating clockwise occur around Bu-do. The surface currents in the bay depend strongly on the wind and river inflow, and such phenomena are more remarkable during he neap tide than the spring tide.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1461-1473
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• 2021

The purpose of this study is to ascertain how the level of resolution of land cover data affects on the local distribution and diffusion of fine dust. the CALPUFF model, which considers the spatio-temporal terrain conditions and changes in weather conditions, was used to estimate PM10 concentration in the Pyeongchon, Anyang-si, Gyeonggi-do. Three different resolutions of land cover data including 20 m, 50 m, and 100 m were compared as the input of the modeling. Using higher resolution land cover data (20 m), the wind speed of the simulated region was the largest and the PM10 concentration was the lowest. Through this study, we confirm that the resolution level of land-use/cover data can affect the local distribution and diffusion of fine dust, which can be detected by CALPUFF. Therefore, when using CALPUFF to simulate fine dust in the future, it can be suggested that checking the impact on spatial resolution according to the form of land cover in advance and proceeding with the simulation can achieve mote accurate results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Earthquakes and Structures
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• v.16 no.3
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• pp.349-358
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• 2019

Long span cable-stayed bridges are extremely vulnerable to dynamic excitations such as which caused by traffic load, wind and earthquake. Studies on cable-stayed bridge vibration control have been keenly interested by researchers and engineers in design new bridges and assessing in-service bridges. In this paper, a novel Hybrid-Tuned Mass Damper (H-TMD) is proposed and a hybrid control model named Mixed Logic Dynamic (MLD) is employed to build the bridge-H-TMD system to mitigate the vibrations. Firstly, the fundamental theory and modeling process of MLD model is introduced. After that, a new state switching design of the H-TMD and state space equations for different states are proposed to control the bridge vibrations. As the state switching designation presented, the H-TMDs can applied active force to bridge only if the structural responses are beyond the limited thresholds, otherwise, the vibrations can be reduced by passive components of dampers without active control forces provided. A new MLD model including both passive and active control states is built based on the MLD model theory and the state switching design of H-TMD. Then, the case study is presented to demonstrate the proposed methodology. In the case study, the control scheme with H-TMDs is applied for a long span cable-stayed bridge, and the MLD model is established and simulated with earthquake excitation. The simulation results reveal that the suggested method has a well damping effect and the established system can be switched between different control states as design excellently. Finally, the energy consumptions of H-TMD schemes are compared with that of Active Tuned Mass Damper (ATMD) schemes under variable seismic wave excitations. The compared results show that the proposed H-TMD can save energy than ATMD.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.243-248
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• 2015

This study was conducted to predict the changes of potential distribution for invasive alien plant, Conyza bonariensis in Korea. C. bonariensis was found in southern Korea (Jeju, south coast, southwest coast). The habitats of C. bonariensis were roadside, bare ground, farm area, and pasture, where the interference by human was severe. Due to the seed characteristics of Compositae, C. bonariensis take long scattering distance and it will easily spread by movement of wind, vehicles and people. C. canadensis in same Conyza genus has already spread on a national scale and it is difficult to manage. We used maximum entropy modeling (MaxEnt) for analyzing the environmental influences on C. bonariensis distribution and projecting on two different RCP scenarios, RCP 4.5 and RCP 8.5. The results of our study indicated annual mean temperature, elevation and temperature seasonality had higher contribution for C. bonariensis potential distribution. Area under curve (AUC) values of the model was 0.9. Under future climate scenario, the constructed model predicted that potential distribution of C. bonariensis will be increased by 338% on RCP 4.5 and 769% on RCP 8.5 in 2100s.