• Wind and Structures
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• 제27권1호
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• pp.41-57
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• 2018

Concentrated Solar Photovoltaic (CPV) is a promising alternative to conventional solar structures. These solar tracking structures need to be optimized to be competitive against other types of energy production. In particular, the selection of the structural parameters needs to be optimized with regards to the dynamic wind response. This study aims to evaluate the effect of the main structural parameters, as selected in the preliminary design phase, on the wind response and then on the weight of the steel support structure. A parametric study has been performed where parameters influencing dynamic wind response are varied. The study is performed using a semi-deterministic time-domain wind analysis method. Unsteady aerodynamic model is applied for the shape of the CPV structure collector at different configurations in conjunction with a consistent mass-spring-damper model with the corresponding degrees of freedom to describe the dynamic response of the system. It is shown that, unlike the static response analysis, the variation of the peak wind response with many structural parameters is highly nonlinear because of the dynamic wind action. A steel structural optimization process reveals that close attention to structural and site wind parameters could lead to optimal design of CPV steel support structure.

• Journal of Astronomy and Space Sciences
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• 제34권2호
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• pp.105-110
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• 2017

Solar wind density depletions are phenomena that solar wind density is rapidly decreased and keep the state. They are generally believed to be caused by the interplanetary (IP) shocks. However, there are other cases that are hardly associated with IP shocks. We set up a hypothesis for this phenomenon and analyze this study. We have collected the solar wind parameters such as density, speed and interplanetary magnetic field (IMF) data related to the solar wind density depletion events during the period from 1996 to 2013 that are obtained with the advanced composition explorer (ACE) and the Wind satellite. We also calculate two pressures (magnetic, dynamic) and analyze the relation with density depletion. As a result, we found total 53 events and the most these phenomena's sources caused by IP shock are interplanetary coronal mass ejection (ICME). We also found that solar wind density depletions are scarcely related with IP shock's parameters. The solar wind density is correlated with solar wind dynamic pressure within density depletion. However, the solar wind density has an little anti-correlation with IMF strength during all events of solar wind density depletion, regardless of the presence of IP shocks. Additionally, In 47 events of IP shocks, we find 6 events that show a feature of blast wave. The quantities of IP shocks are weaker than blast wave from the Sun, they are declined in a short time after increasing rapidly. We thus argue that IMF strength or dynamic pressure are an important factor in understanding the nature of solar wind density depletion. Since IMF strength and solar wind speed varies with solar cycle, we will also investigate the characteristics of solar wind density depletion events in different phases of solar cycle as an additional clue to their physical nature.

• 천문학논총
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• 제30권2호
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• pp.47-51
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• 2015

Ground Level Enhancements (GLEs) in cosmic ray intensity observed during the period of 1997-2012 have been studied with energetic solar features and disturbances in solar wind plasma parameters and it is seen that all the GLEs have been found to be associated with coronal mass ejections, hard X-ray solar flares and solar radio bursts. All the GLEs have also been found to be associated with sudden jumps in solar proton flux of energy of ${\geq}60Mev$. A positive correlation with correlation coefficient of 0.48 has been found between the maximum percentage intensity (Imax%) of Ground Level Enhancements and the peak value of solar proton flux of energy (${\geq}60Mev$). All the Ground Level Enhancements have been found to be associated with jumps in solar wind plasma velocity (JSWV) events. A positive correlation with correlation coefficient of 0.43 has been found between the maximum percentage intensity (Imax %) of Ground Level Enhancements and the peak value of solar wind plasma velocity of associated (JSWV) events. All the Ground Level Enhancements have been found to be associated with jumps in solar wind plasma pressure (JSWP) events. A positive correlation with correlation coefficient of 0.67 has been found between the maximum percentage intensity (Imax %) of Ground Level Enhancements and the peak value of solar wind plasma pressure of associated (JSWP) events and of 0.68 between the maximum percentage intensity (Imax %) of Ground Level Enhancements and the magnitude of the jump in solar wind plasma pressure of associated (JSWP) events.

• 천문학회보
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• 제42권1호
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• pp.41.1-41.1
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• 2017

It is well known that there are good relations of coronal hole (CH) parameters such as the size, location, and magnetic field strength to the solar wind conditions and the geomagnetic storms. Especially in the minimum phase of solar cycle, CHs in mid- or low-latitude are one of major drivers for geomagnetic storms, since they form corotating interaction regions (CIRs). By adopting the method of Vrsnak et al. (2007), the Space Weather Research Center (SWRC) in Korea Astronomy and Space Science Institute (KASI) has done daily forecast of solar wind speed and Dst index from 2010. Through years of experience, we realize that the geomagnetic storms caused by CHs have different characteristics from those by CMEs. Thus, we statistically analyze the characteristics and causality of the geomagnetic storms by the CHs rather than the CMEs with dataset obtained during the solar activity was very low. For this, we examine the CH properties, solar wind parameters as well as geomagnetic storm indices. As the first result, we show the different trends of the solar wind parameters and geomagnetic indices depending on the degree of solar activity represented by CH (quiet) or sunspot number (SSN) in the active region (active) and then we evaluate our forecasts using CH information and suggest several ideas to improve forecasting capability.

• 한국생산제조학회지
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• 제21권5호
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• pp.747-754
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• 2012

A solar power generator is usually installed outdoors and it is exposed to extreme environments such as snow weight and wind loading. The solar tracker structure should be designed to have sufficient stiffness and strength against such loads. In this paper, simulations are performed by varying the parameters such as wind directions, wind speeds and the pose of the solar panel to evaluate the effects of extreme wind on solar tracker. As the effects of wind load, maximum displacement and maximum equivalent stress in the solar tracker are calculated. Finite element stress analysis is carried out by using the pressure distribution that is obtained by prior wind load analysis due to the flow around the solar tracker. The stress analysis of solar tracker to check and/or improve structural robustness provides some useful instructions for structural design or revision of solar tracker.

• 한국태양에너지학회 논문집
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• 제30권2호
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• pp.54-64
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• 2010

Interest in new and renewable energies like solar energy and wind energy is increasing throughout the world due to the rapidly expanding energy consumption and environmental reasons. An essential requirement for wind force power generation is estimating the size of wind energy accurately. Wind energy is estimated usually using meteorological data or field measurement. This study attempted to estimate wind energy density using meteorological data on daily mean wind speed and the Weibull parameters in Seoul, a representative inland city where over 60% of 15 story or higher apartments in Korea are situated, and Busan, Incheon, Ulsan and Jeju that are major coastal cities in Korea. According to the results of analysis, the monthly mean probability density distribution based on the daily mean wind speed agreed well with the monthly mean probability density distribution based on the Weibull parameters. This finding suggests that the Weibull parameters, which is highly applicable and convenient, can be utilized to estimate the wind energy density distribution of each area. Another finding was that wind energy density was higher in coastal cities Busan and Incheon than in inland city Seoul.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2004년도 한국우주과학회보 제13권1호
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• pp.41-41
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• 2004

We report the results on the investigation of the association of solar wind dynamics and the occurrence of geosynchronous relativistic electron events. This study analyzed E>2MeV electron fluxes measured by GOES 10 satellite and solar wind parameters by ACE satellite for April, 1999 to December, 2002. Most of the relativistic events during the time period are found to be accompanied by the prolonged period of quiet solar wind dynamics which is characterized as low solar wind pressure, weak interplanetary magnetic field, and fast fluctuations in IMF Bz. (omitted)

• Journal of Astronomy and Space Sciences
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• 제28권2호
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• pp.123-132
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• 2011

It is generally believed that the occurrence of a magnetic storm depends upon the solar wind conditions, particularly the southward interplanetary magnetic field (IMF) component. To understand the relationship between solar wind parameters and magnetic storms, variations in magnetic field polarity and solar wind parameters during magnetic storms are examined. A total of 156 storms during the period of 1997~2003 are used. According to the interplanetary driver, magnetic storms are divided into three types, which are coronal mass ejection (CME)-driven storms, co-rotating interaction region (CIR)-driven storms, and complicated type storms. Complicated types were not included in this study. For this purpose, the manner in which the direction change of IMF $B_y$ and $B_z$ components (in geocentric solar magnetospheric coordinate system coordinate) during the main phase is related with the development of the storm is examined. The time-integrated solar wind parameters are compared with the time-integrated disturbance storm time (Dst) index during the main phase of each magnetic storm. The time lag with the storm size is also investigated. Some results are worth noting: CME-driven storms, under steady conditions of $B_z$ < 0, represent more than half of the storms in number. That is, it is found that the average number of storms for negative sign of IMF $B_z$ (T1~T4) is high, at 56.4%, 53.0%, and 63.7% in each storm category, respectively. However, for the CIR-driven storms, the percentage of moderate storms is only 29.2%, while the number of intense storms is more than half (60.0%) under the $B_z$ < 0 condition. It is found that the correlation is highest between the time-integrated IMF $B_z$ and the time-integrated Dst index for the CME-driven storms. On the other hand, for the CIR-driven storms, a high correlation is found, with the correlation coefficient being 0.93, between time-integrated Dst index and time-integrated solar wind speed, while a low correlation, 0.51, is found between timeintegrated $B_z$ and time-integrated Dst index. The relationship between storm size and time lag in terms of hours from $B_z$ minimum to Dst minimum values is investigated. For the CME-driven storms, time lag of 26% of moderate storms is one hour, whereas time lag of 33% of moderate storms is two hours for the CIR-driven storms. The average values of solar wind parameters for the CME and CIR-driven storms are also examined. The average values of ${\mid}Dst_{min}{\mid}$ and ${\mid}B_{zmin}{\mid}$ for the CME-driven storms are higher than those of CIR-driven storms, while the average value of temperature is lower.

• Journal of Astronomy and Space Sciences
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• 제31권2호
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• pp.149-157
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• 2014

As the prediction of geomagnetic storms is becoming an important and practical problem, conditions in the Earth's magnetosphere have been studied rigorously in terms of those in the interplanetary space. Another approach to space weather forecast is to deal with it as a probabilistic geomagnetic storm forecasting problem. In this study, we carry out detailed statistical analysis of solar wind parameters and geomagnetic indices examining the dependence of the distribution on the solar cycle and annual variations. Our main findings are as follows: (1) The distribution of parameters obtained via the superimposed epoch method follows the Gaussian distribution. (2) When solar activity is at its maximum the mean value of the distribution is shifted to the direction indicating the intense environment. Furthermore, the width of the distribution becomes wider at its maximum than at its minimum so that more extreme case can be expected. (3) The distribution of some certain heliospheric parameters is less sensitive to the phase of the solar cycle and annual variations. (4) The distribution of the eastward component of the interplanetary electric field BV and the solar wind driving function BV2, however, appears to be all dependent on the solar maximum/minimum, the descending/ascending phases of the solar cycle and the equinoxes/solstices. (5) The distribution of the AE index and the Dst index shares statistical features closely with BV and $BV^2$ compared with other heliospheric parameters. In this sense, BV and $BV^2$ are more robust proxies of the geomagnetic storm. We conclude by pointing out that our results allow us to step forward in providing the occurrence probability of geomagnetic storms for space weather and physical modeling.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2009년도 한국우주과학회보 제18권2호
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• pp.36.3-37
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• 2009

This is an attempt to improve a formula to predict variations of geomagnetic storm indices (Dst) from solar wind parameters. A formula which is most widely accepted was given by Burton et al. (1975) over 30 years ago. Their formula is: dDst*/dt = Q(t) - Dst*(t)/$\tau$, where Q(t) is the Dst injection rate given by the convolution of dawn-to-dusk electric field generated by southward solar wind magnetic field and some response function. However, they did not clearly specify the response function. As a result, misunderstanding seems to be prevailing that the injection rate is proportional to the dawn-to-dusk electric field. In this study we tried to determine the response function by examining 12 intense geomagnetic storms with minimum Dst < -200 nT for which solar wind data are available. The method is as follows. First we assume the form of response function that is specified by several time constants, so that we can calculate the injection rate Q1(t) from the solar wind data. On the other hand, Burton et al. expression provide the observed injection rate Q2(t) = dDst*/dt + Dst*(t)/$\tau$. Thus, it is possible to determine the time constants of response function by a least-squares method to minimize the difference between Q1(t) and Q2(t). We have found this simple method successful enough to reproduce the observed Dst variations from the corresponding solar wind data. The present result provides a scheme to predict the development of Dst 30 minutes to 1 hour in advance by using the real time solar wind data from the ACE spacecraft.