• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.8
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• pp.695-702
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• 2012

In this study, Multi-fidelity analysis is performed to improve the accuracy of analysis result during conceptual design stage. Multidisciplinary Design Optimization(MDO) method is also considered to satisfy the total system requirements. Low-fidelity analysis codes which are based on empirical equations are developed and validated for analyzing the Unmanned Aerial Vehicle(UAV) which have unconventional configurations. Analysis codes consist of initial sizing, aerodynamics, propulsion, mission, weight, performance, and stability modules. Design synthesis program which is composed of those modules is developed. To improve the accuracy of the design method for UAV, Vortex Lattice Method is used for the strategy of MFA. Multi-Disciplinary Feasible(MDF) method is used for MDO technique. To demonstrate the validity of presented method, the optimization results of both methods are compared. According to those results, the presented method is demonstrated to be applicable to improve the accuracy of the analyses during conceptual design stage.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.73-82
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• 2011

Most of countries start to restrict the emission gases of vehicles especially CO2 because of the global warming. Many vehicle companies including Toyota have launched various HEVs to satisfy the restriction laws and to improve the vehicle's efficiency. However, development for heavy-duty hybrid system is not plentiful rather than the passenger car. In this study, we choose the optimal size of engine, motor and battery for heavy-duty hybrid systems using dynamic programming. Also we analyze the correlation of the system's cost and efficiency because the added cost of vehicle to make the hybrid system is very important factor for the manufacturing companies. Finally, this study suggests a method to choose the appropriate system components size considering its performance and the cost. With this method, it is possible to select the component size for various systems.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.17-27
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• 2005

This paper presents an integrated optimum design and a cost effectiveness evaluation method of a viscoelastically damped structural system. The criterion selected for the optimization is the minimization of the life?cycle cost which is a function of structural sizing variables and the amount of the viscoelastic damper. A genetic algorithm is used as a numerical searching technique in order to simultaneously find the optimum parameters of the integrated system. Optimal distributions of design variables according to various seismic characteristics are investigated by applying the proposed design method to a numerical example of a 10?story building structure. The cost effectiveness of viscoelastically damped structural system is also evaluated by comparing the life-cycle cost of the structure without viscoelastic dampers. The results show that the viscoelastic damper is effective in a region of low to moderate seismicity.

• Structural Engineering and Mechanics
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• v.8 no.2
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• pp.165-180
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• 1999

The cost effectiveness of using steel roof systems for residential buildings is becoming increasingly apparent with the decrease in manufacturing cost of steel components, reliability and efficiency in construction practices, and the economic and environmental concerns. While steel has been one of the primary materials for structural systems, it is only recently that its use for residential buildings is being explored. A comprehensive system for the design of residential steel roof truss systems is presented. In the first stage of the research the design curves obtained from the AISI-LRFD code for the manufactured cross-sections were verified experimentally. Components of the truss systems were tested in order to determine their member properties when subjected to axial force and bending moments. In addition, the experiments were simulated using finite element analysis to provide an additional source of verification. The second stage of the research involved the development of an integrated design approach that would automatically design a lowest cost roof truss given minimal input. A modified genetic algorithm was used to handle sizing, shape and topology variables in the design problem. The developed methodology was implemented in a software system for the purpose of designing the lowest cost truss that would meet the AISI code provisions and construction requirements given the input parameters. The third stage of the research involved full-scale testing of a typical residential steel roof designed using the developed software system. The full scale testing established the factor of safety while validating the analysis and design procedures. Evaluation of the test results indicates that designs using the present approach provide a structure with enough reserve strength to perform as predicted and are very economical.

• Journal of Hydrogen and New Energy
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• v.32 no.4
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• pp.245-255
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• 2021

Various studies about metallic bipolar plates have been conducted to improve fuel cell performance through flow field design optimization. These research works have been mainly focused on fuel cells for vehicle, but not fuel cells for building. In order to reduce the price and volume of fuel cell stacks for building, it is necessary to apply a metallic flow field, In this study, for a metallic flow field applied to a fuel cell for building, the effect of a change in the flow field shape on the performance of a polymer electrolyte membrane fuel cell was confirmed using a model and experiments with a down-sizing single cell. As a result, the flow field using a metal foam outperforms the channel type flow field because it has higher internal differential pressure and higher reactants velocity in gas diffusion layer, resulting in higher water removal and higher oxygen concentration in the catalyst layer than the channel type flow field. This study is expected to contribute to providing basic data for selecting the optimal flow field for the full stack of polymer electrolyte membrane fuel cells for buildings.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.4
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• pp.229-237
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• 2022

The sewer capacity design have been based on the Huff model or the rational equation in South Korea and often failed to determine optimal capacity, resulting in frequent urban flooding or over-sizing. A time distribution of rainfall (i.e., Huff or ABM method) could be used instead of a rainfall hyetograph obtained from statistical analysis of previous rainfalls. In this study, the Huff method and the ABM method, which predict the time distribution of rain intensity, which are widely used to calculate sewage pipe drainage capacity using the SWMM, were compared with the standard rainfall intensity hyetograph of Seoul. If the rainfall duration was 30 minutes to 180 minutes, the rainfall intensity value calculated by the Huff model tended to be less than the rainfall intensity value of the standard rainfall intensity in the initial 5-10 minutes. As a result, more than 10% to 30% of under-design would be made. In addition, the rainfall intensity value calculated by the Huff model from the section excluding the initial 5-10 minutes of rainfall to the rainfall duration was calculated larger than the value using the standard rainfall intensity equation, which would result in an over-design of 10% to 30%. In the case of a relatively long rainfall duration of 360 minutes (6 hours) to 1,440 minutes (24 hours), it showed an lower rainfall intensity of 60 to 90% in the early stages of rainfall, but the problem of under-design had been solved as the rainfall duration time had elapsed. On the other hand, in the alternating block method (ABM) method, it was found that the rainfall intensity at the entire period at each assumed rainfall duration accurately matched the standard rainfall intensity hyetograph of Seoul.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.6
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• pp.555-562
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• 2016

Seismic design of braced frames that simultaneously considers economic issues and structural performance represents a rather complicated engineering problem, and therefore, a systematic and well-established methodology is needed. This study proposes a multi-objective seismic design method for an inverted V-braced frame with suspended zipper struts that uses the non-dominated sorting genetic algorithm-II(NSGA-II). The structural weight and the maximum inter-story drift ratio as the objective functions are simultaneously minimized to optimize the cost and seismic performance of the structure. To investigate which of strength- and performance-based design criteria for braced frames is the critical design condition, the constraint conditions on the two design methods are simultaneously considered (i.e. the constraint conditions based on the strength and plastic deformation of members). The linear static analysis method and the nonlinear static analysis method are adopted to check the strength- and plastic deformation-based design constraints, respectively. The proposed optimal method are applied to three- and six-story steel frame examples, and the solutions improved for the considered objective functions were found.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1995.12a
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• pp.9-21
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• 1995

A planning support system enhances our ability to use water capacity expansion as an urban growth management strategy. This paper reports the development of capacity expansion modeling of water supply as part of the continuing development of such a planning support system (PEGASUS: Planning Environment for Generation and Analysis of Spatial Urban Systems) to incorporate water supply, This system is designed from the understanding that land use and development drive the demand for infrastructure and infrastructure can have a significant influence on the ways in which land is developed and used. Capacity expansion Problems of water supply can be solved in two ways: 1) optimal control theory, and 2) mixed integer nonlinear programming (MINLP). Each method has its strengths and weaknesses. In this study the MINLP approach is used because of its strength of determining expansion sizing and timing simultaneously. A dynamic network optimization model and a water-distribution network analysis model can address the dynamic interdependence between water planning and land use planning. While the water-distribution network analysis model evaluates the performance of generated networks over time, the dynamic optimization model chooses alternatives to meet expanding water needs. In addition, the user and capacity expansion modeling-to-generate-alternatives (MGA) can generate alternatives. A cost benefit analysis module using a normalization technique helps in choosing the most economical among those alternatives. GIS provide a tool for estimating the volume of demanded water and showing results of the capacity expansion model.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.