• Journal of Energy Engineering
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• v.20 no.4
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• pp.338-345
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• 2011

The swirl flow applied for high efficiency and reduction of emission such as NOx, CO in a gas turbine engine makes recirculation zone by shear layer in the combustion chamber. This recirculation zone influences a decreasing flame temperature and flame length by burned gas recirculation. Also it is able to suppress from instability in lean-premixed flame. In this study, it was found that the swirl flow field was characterized as function of swirl number using PIV measurement in dump combustor. As increasing swirl number, a change of flow field was presented and recirculation zone was shifted in the nozzle exit direction. Also turbulent intensity and turbulent length scale in combustor were decreased in combustion. It has shown reduction of eddies scale with swirl number increasing.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1605-1609
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• 2010

CGCM3.1 SRES B1 시나리오의 2D 변수들을 입력값으로 인공신경망 모형을 이용한 스케일 상세화기법으로 강부식(2009)은 소양강댐 유역의 월 누적강수 경향분석을 실시하였다. 원시 GCM 시나리오를 스케일 상세화 시키기 위한 기법의 하나로 인공신경망 모형을 사용할 수 있는데, 이 경우 GCM에서 모의되는 강수플럭스, 해면기압, 지표면 근처에서의 일 평균온도, 지표면 근처에서의 일평균온도, 지표면으로부터 발생하는 잠열플럭스 등과 같은 22개의 변수를 잠재적인 예측인자로 사용하여 신경망을 구성하게 된다. 입력변수세트의 구성은 인공신경망의 계산 효율을 좌우하는 중요한 요소라 할 수 있다. 본 연구에서는 변수의 물리적 특성을 고려하여 순차적인 변수선택을 통한 신경망 입력변수 세트를 구성하고 입력세트 간의 학습성과 비교를 통하여, 최적 입력변수 선정 및 신경망의 학습효과를 높일 수 있는 방법에 대해 연구하였다. 물리적 상관성이 높다고 판단되는 GCM_Prec, huss, ps를 입력변수로 하여 순차적인 케이스를 학습해본 결과 huss와 ps를 입력변수로 하는 케이스에 대해서 적은 오차와 높은 상관성을 보였다, 또한, 신경망의 학습 효과를 높이기 위해 홍수기와 비홍수기로 구분하여 학습한 결과 홍수기와 비홍수기로 구분하여 신경망을 구성하였을 경우가 향상된 모의값을 나타내었다. 기후변화모의자료는 CCCma(Canadian Center for Climate Modeling and Analysis)에서 제공되는 CGCM3.1/T63 20C3M 시나리오를 사용하였으며, 관측값으로는 AWS에서 제공된 일 누적강수를 사용하였다. 인공신경망의 학습기간은 1997년부터 2000년이며, 검증기간은 2001년부터 2004년으로 구성하였다.

• Composites Research
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• v.19 no.1
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• pp.1-8
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• 2006

Prediction of damage caused by low-velocity impact in laminated composite plate is an important problem faced by designers using composites. Not only the inplane stresses but also the interlaminar normal and shear stresses playa role in estimating the damage caused. But it is well known that the conventional approach based on the homogenization has the limit in description of damage. The work reported here is an effort in getting better predictions of dynamic behavior and damage in composite plate using DNS approach. In the DNS model, we discretize the composite plates through separate modeling of fiber and matrix for the local microscopic analysis. In the view of microscopic mechanics with DNS model, interlaminar stress behaviors in the inside of composite materials are investigated and compared with the results of the homogenized model which has been used in the conventional approach to impact analysis. Also the multiscale model based on DNS concept is developed in order to enhance the effectiveness of impact analysis, and we present the results of multiscale analysis considering micro and macro structures simultaneously.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.3
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• pp.275-282
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• 2014

Particle-reinforced metal matrix composites exhibit a strengthening effect due to the particle size-dependent length scale that arises from the strain gradient, and thus from the geometrically necessary dislocations between the particles and matrix that result from their CTE(Coefficient of Thermal Expansion) and elastic-plastic mismatches. In this study, the influence of the size-dependent length scale on the particle-matrix interface failure and ductile failure in the matrix was examined using finite-element punch zone modeling whereby an augmented strength was assigned around the particle. The failure behavior was observed by a parametric study, while varying the interface failure properties such as the interface strength and debonding energy with different particle sizes and volume fractions. It is shown that the two failure modes (interface failure and ductile failure in the matrix) interact with each other and are closely related to the particle size-dependent length scale; in other words, the composite with the smaller particles, which is surrounded by a denser dislocation than that with the larger particles, retards the initiation and growth of the interface and matrix failures, and also leads to a smaller amount of decrease in the flow stress during failure.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.796-806
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• 2009

SVC (Scalable Video Coding) is a new video coding standard to provide convergence media service in heterogeneous environments with different networks and diverse terminals through spatial-temporal-quality combined flexible scalabilities. This paper presents the performance analysis on packet loss in the delivery of SVC over IP networks and an efficient adaptation method to packet loss caused by buffer overflow. In particular, SVC with MGS (Medium Grained Scalability) as well as spatial and temporal scalabilities is addressed in the consideration of packet-based adaptation since finer adaptation is possible with a sufficient numbers of quality layers in MGS. The effect on spatio-temporal quality due to the packet loss of SVC with MGS is evaluated. In order to minimize quality degradation resulted by packet loss, the proposed adaptation of MGS based SVC first sets adaptation unit of AU (Access Unit) or GOP corresponding to allowed delay and then selectively discards packets in order of importance in terms of layer dependency. In the experiment, the effects of packet loss on quantitative qualities are analyzed and the effectiveness of the proposed adaptation to packet loss is shown.

• Journal of the Korean GEO-environmental Society
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• v.13 no.9
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• pp.45-51
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• 2012

The calcium hydroxide$(Ca(OH)_2)$ which is the cement hydrate flowed into the tunnel by groundwater is reacted with microorganism in the soil, carbon dioxide$(CO_2)$ and the vehicle's exhaust gas$(SO_3)$. So its by-products are precipitated at the drainage pipe and these cause the drainage clogging. By this phenomenon, Degradation of water flow at the drainage system of the tunnel occurred and also pore water pressure is increased. Hence the acceleration of seepage and degradation of lining is occurred. The purpose of this study is to evaluate the field applicability of the Quantum Stick and Magnetic treatment in prevention of scale deposits at the Namsan ${\bigcirc}{\bigcirc}$ tunnel and the Zone ${\bigcirc}{\bigcirc}{\bigcirc}$ of subway. These technologies were installed into drainpipes with their performance monitored through occasional site visits. SEM and XRD were also performed on scale collected from these drainpipes. As a result, in case which factor technology is applied, scale creation is remarkably decreased and especially Quantum Stick treatment performing better than Magnetic treatment. Therefore, additional application of Quantum Stick or Magnetic treatment to the existing drainage is expected to decrease the drainage clogging of the drainage.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.2
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• pp.11-22
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• 2017

To successfully create a low-carbon landscape in order to become a low-carbon city, it is necessary to understand the dynamics of artificial greening's resources on a multi-scale. Additionally, the effects of carbon storage should be quantitatively evaluated. The purpose of this study is to simulate and evaluate the changes in carbon storages of artificial ground trees using system dynamics throughout a long-term period. The process consisted of analyzing the dynamics of the multi-scale carbon cycle by using a casual loop diagram as well as simulating carbon storage changes in the green roof of the Gangnam-gu office building in 2008, 2018, 2028, and 2038. Results of the study are as follows. First, the causal loop diagram representing the relationship between the carbon storage of the artificial ground trees and the urban carbon cycle demonstrates that the carbon storage of the trees possess mutual cross-scale dynamics. Second, the main variables for the simulation model collected 'Biomass,' 'Carbon storage,' 'Dead organic matter,' and 'Carbon absorption,'and validated a high coefficient of determination, the value being ($R^2$=0.725, p<0.05). Third, as a result of the simulation model, we found that the variation in ranking of tree species was changing over time. This study also suggested the specific species of tree-such as Acer palmatum var. amoenum, Pinus densiflora, and Betula platyphylla-are used to improve the carbon storage in the green roof of the Gangnam-gu office building. This study can help contribute to developing quantitative and scientific criteria when designing, managing, and developing programs on low-carbon landscapes.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.218-236
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• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.25-58
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• 2021

This study investigated the corporate growth with more emphasis on longitudinal characteristics, not the results of companies with relatively more emphasis on cross-sectional, in the 21st-century entrepreneurial context. As of the end of 2019, sampled 479 global unicorn companies, and 333 high-growth companies with revenue of more than $100 million among 5,000 private companies in the U.S. with a compound annual growth rate (CAGR) exceeding 15% for the past three years. They were examined with 3 perspectives in terms of corporate growth that 1) the growth of enterprise value, 2) the pace of growth, and 3) the effectiveness of growth. As a result of our study, the corporate growth of the perspective of creating enterprise value had a relatively higher relationship with the characteristics of industries and markets. The pace of growth was more fully explained by the characteristics of the industry and the market environment and the choice of strategies that make up a valid combination. In addition, growth in terms of the effectiveness of corporate performance was influenced by the choice of strategy, the characteristics of the industry and market environment, and its business age, the proxy variable of resource accumulation, comprehensively. This study through a sample based on companies with an enterprise value of more than $1 billion and annual revenue of more than $100 million can be a valid reference in terms of creating milestones and roadmaps for scale-up of early-stage startups, particularly in terms of practitioners' point of view. It also provides a critical reference for overcoming the limitations of mainstream theories of the 20th century and developing the theory of corporate growth that fits the 21st-century entrepreneurial context.

• Composites Research
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• v.30 no.5
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• pp.323-330
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• 2017

In this study, multiscale analysis in which the information obtained from molecular dynamics simulation is applied to the continuum mechanics level is conducted to investigate the effects of clustering of silicon carbide nanoparticles reinforced into polypropylene matrix on mechanical behavior of nanocomposites. The elastic behavior of polymer nanocomposites is observed for various states of nanoparticulate agglomeration according to the model reflecting the degradation of interphase properties. In addition, factors which mainly affect the mechanical behavior of the nanocomposites are identified, and new index 'clustering density' is defined. The correlation between the clustering density and the elastic modulus of nanocomposites is understood. As the clustering density increases, the interfacial effect decreased and finally the improvement of mechanical properties is suppressed. By considering the random distribution of the nanoparticles, the range of elastic modulus of nanocomposites for same value of clustering density can be investigated. The correlation can be expressed in the form of exponential function, and the mechanical behavior of the polymer nanocomposites can be effectively predicted by using the nanoparticulate clustering density.