• 한국전산유체공학회:학술대회논문집
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• 1999.11a
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• pp.152-157
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• 1999

For the simulation of the blood flow in coronary artery, the system modeling of coronary hemodynamics is combined with CFD technique. The blood flow in coronary artery interacts with the global coronary circulation. Especially in case of the coronary artery with stenosis, the interaction plays an important role in the hemodynamics of the circulation. In this study we present a combined numerical approach using both the CFD technique for flow simulation and the global system model of coronary circulation. We use a lumped parameter model for the global simulation of coronary circulation whereas the finite element method is employed to compute the viscous flow field in stenosed coronary artery, The time variation of the pressure drop due to stenosis is obtained from the proposed numerical method. Numerical results shows that the flow resistance and pressure drop due to stenosis has a relatively large value in systole.

• Ocean and Polar Research
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• v.34 no.2
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• pp.229-237
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• 2012

Using results from an Earth System model, we investigated change in primary production in the East China Sea, under a global warming scenario. As global warming progresses, the vertical stratification of water becomes stronger, and nutrient supply from the lower part to the upper part is reduced. Consequently, so is the primary production. In addition to the warming trend, there is strong decadal to interdecadal scale variability, and it takes a few decades before the warming trend surpasses natural variability. Thus, it would be very hard to investigate the global warming trend using data of several years' length.

• Coupled systems mechanics
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• v.6 no.4
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• pp.501-522
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• 2017

This paper introduces a numerical procedure to incorporate elasto-plastic local deformation effects in the dynamic analysis of beams. The appealing feature is that simple beam type finite elements can be used for the global model which needs not to be altered by the localized elasto-plastic deformations. An overlapping local sophisticated 2D membrane model replaces the internal forces of the beam elements in the predefined region where the localized deformations take place. An iterative coupling technique is used to perform this replacement. Comparisons with full membrane analysis are provided in order to illustrate the accuracy and efficiency of the method developed herein. In this study, the membrane formulation is able to capture the elasto-plastic material behaviour based on the von Misses yield criterion and the associated flow rule for plane stress. The Newmark time integration method is adopted for the step-by-step dynamic analysis.

• Steel and Composite Structures
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• v.20 no.3
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• pp.693-718
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• 2016

This paper presents a finite element procedure based on Bridging multi-scale method (BMM) in order to incorporate the effect of local/cross-sectional deformations (e.g., flange local buckling and web crippling) on the global behaviour of thin-walled members made of fibre-reinforced polymer composite laminates. This method allows the application of local shell elements in critical regions of an existing beam-type model. Therefore, it obviates the need for using computationally expensive shell elements in the whole domain of the structure, which is otherwise necessary to capture the effect of the localized behaviour. Consequently, highly accurate analysis results can be achieved with this method by using significantly smaller finite element model, compared to the existing methods. The proposed method can be used for composite polymer laminates with arbitrary fibre orientation directions in different layers of the material, and under various loading conditions. Comparison with full shell-type finite element analysis results are made in order to illustrate the efficiency and accuracy of the proposed technique.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1169-1182
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• 2006

Robust design technology has been applied to versatile engineering problems to ensure consistency in product performance. Since 1980s, the concept of robust design has been introduced to numerical optimization field, which is called the robust optimization. The robustness in the robust optimization is determined by a measure of insensitiveness with respect to the variation of a response. However, there are significant difficulties associated with the calculation of variations represented as its mean and variance. To overcome the current limitation, this research presents an implementation of the approximate statistical moment method based on kriging metamodel. Two sampling methods are simultaneously utilized to obtain the sequential surrogate model of a response. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method. Then, the simulated annealing algorithm of global optimization methods is adopted to find the global robust optimum. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.443-450
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• 2021

The study is devoted to the formation of a economic principles cargo delivery management in global supply chains. Mathematical model of delivering special categories of goods by road is a key element of these principles. The article analyzes the existing studies on solving the problem of cargo delivery in various aspects. It was noted that the greatest attention is paid to legal regulation, last mile delivery, optimization of routes and delivery schemes, information support, technological innovations, cluster routing, etc. In the developed mathematical model a minimum of total costs of forming loading units and freight shipments was defined as the criterion of optimality of organizing delivery by motor transport. The authors propose the creation of logistics clusters allowing the integration of urban transport flows and global supply chains.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.312-319
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• 2023

This study aims to enhance the engineering education for non-majors by incorporating the concept of Global Citizenship Education and addressing the need for education that responds to climate and ecological changes. The study uses robot programming as a tool to foster the development of global citizens. Non-majors often struggle with producing more than just motionless forms or solid productions, due to a lack of understanding of mechanisms and coding. The study proposes the use of the Convergence D-SteamRobot (DSR) to address this issue by blending humanities and engineering. This is achieved by presenting problems through books to increase empathy, integrating simple machine mechanisms, and creating prototypes to solve self-defined problems. Through this process, learners determine the SDGs topic they want to solve and learn about the simple mechanical mechanism involved in producing the prototype. The educational model provides a constructivist learning environment that emphasizes empathy and exploration, encourages peer-learning, and improves divergent thinking and problem-solving skills.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.693-696
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• 2022

The purpose of this paper is to explore the factors affecting the purchase intention of new energy vehicles, utilizing the BRA and VAM models. Based on self-interest and altruism, a model for willingness to use new energy vehicles was developed. Through analysis with analytical tools such as SPSS and AMOS, we obtained the following conclusions: Perceived value, perceived entertainment, and environmental values all have a significant positive impact on the purchase intention of new energy vehicles; Perceived risk has a negative impact on purchase intention. By conducting this research, useful suggestions can be made for the formulation of enterprise strategies, and new directions and inspirations can be provided for enterprises.

• Steel and Composite Structures
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• v.50 no.3
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• pp.337-345
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• 2024

This research proposed a particle swarm optimization (PSO) based seismic retrofit design of moment frame structures using a steel frame assembly. Two full scale specimens of the steel frame assembly with different corner details were attached to one-story RC frames for seismic retrofit, and the lateral load resisting capacities of the retrofitted frames subjected to cyclic loads were compared with those of a bare RC frame. The open source software framework Opensees was used to develop an analytical model for validating the experimental results. The developed analytical model and the optimization scheme were applied to a case study structure for economic seismic retrofit design, and its seismic performance was assessed before and after the retrofit. The results show that the developed steel frame assembly was effective in increasing seismic load resisting capability of the structure, and the PSO algorithm could be applied as convenient optimization tool for seismic retrofit design of structures.

• Atmosphere
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• v.28 no.3
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• pp.247-261
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• 2018

The objective of this paper is to compare probabilistic temperature forecasts from different regional and global ensemble prediction systems over PyeongChang area. A statistical post-processing method is used to take into account combination and calibration of forecasts from different numerical prediction systems, laying greater weight on ensemble model that exhibits the best performance. Observations for temperature were obtained from the 30 stations in PyeongChang and three different ensemble forecasts derived from the European Centre for Medium-Range Weather Forecasts, Ensemble Prediction System for Global and Limited Area Ensemble Prediction System that were obtained between 1 May 2014 and 18 March 2017. Prior to applying to the post-processing methods, reliability analysis was conducted to identify the statistical consistency of ensemble forecasts and corresponding observations. Then, ensemble model output statistics and bias-corrected methods were applied to each raw ensemble model and then proposed weighted combination of ensembles. The results showed that the proposed methods provide improved performances than raw ensemble mean. In particular, multi-model forecast based on ensemble model output statistics was superior to the bias-corrected forecast in terms of deterministic prediction.