• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.822-827
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• 2005

Recently engineering systems problems become quite large and complicated. For those problems, design requirements are fairly complex. It is not easy to design such systems by considering only one discipline. Therefore, we need a design methodology that can consider various disciplines. Multidisciplinary Design Optimization (MDO) is an emerging optimization method to include multiple disciplines. So far, about seven MDO methodologies have been proposed for MDO. They are Multidisciplinary Feasible (MDF), Individual Feasible (IDF), All-at-Once (AAO), Concurrent Subspace Optimization (CSSO), Collaborative Optimization (CO), Bi-Level Integrated System Synthesis (BLISS) and Multidisciplinary Optimization Based on Independent Subspaces (MDOIS). In this research, the performances of the methods are evaluated and compared. Practical engineering problems may not be appropriate for fairness. Therefore, mathematical problems are developed for the comparison. Conditions for fair comparison are defined and the mathematical problems are defined based on the conditions. All the methods are coded and the performances of the methods are compared qualitatively as well as quantitatively.

• Advances in Computational Design
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• v.2 no.3
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• pp.241-256
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• 2017

This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant's experience, construction cost and etc. The MOF will generate, evaluate all different possible configurations within the predefined each parameter, present the most optimized set of solution, and then feed BIM environment to minimize data loss across software platform. This paper illustrates how Multi-objective optimization methodology can be utilized in design practice by integrating advanced simulation, optimization algorithm and BIM.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.10
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• pp.1189-1195
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• 2011

For side airbag, the pipe type inflators have been wide used while the disk type inflators have been used for front airbag. For helping to prevent injury and death the airbag inflator system should be design with great care. The present study deal with optimizing the design of side airbag inflator by finite element analysis and design of experiment method. An optimization process was integrated to determine the optimum design variable values related to the side airbag inflator. Free shape optimization method has been carried out to find a optimal shape on an side airbag inflator model. Optimization of the air bag inflator was successfully developed using Sharpe optimization was carried out to find a new geometry. The improved results compared to the base design specification were achieved from design of experiment and optimization.

• Structural Engineering and Mechanics
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• v.31 no.4
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• pp.407-421
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• 2009

The present paper addresses the optimal design of composite laminates with the aim of minimizing free-edge delamination stresses. A technique involving the application of particle swarm optimization (PSO) integrated with FEM was developed for the optimization. Optimization was also conducted with the zero-order method (ZOM) included in ANSYS. The semi-analytical method, which provides an approximation of the interlaminar normal stress of laminates under in-plane load, was used to partially validate the optimization results. It was found that optimal results based on ZOM are sensitive to the starting design points, and an unsuitable initial design set will lead to a result far from global solution. By contrast, the proposed method can find the global optimal solution regardless of initial designs, and the solutions were better than those obtained by ZOM in all the cases investigated.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.5
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• pp.25-36
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• 2003

Aircraft design pursues integrated design efforts by considering all design elements together. In the integrated design environment, it is crucial for the design data to be consistent, free of errorm, and most recent. Database design process consists of the analysis of the data which shall be stored and managed, the construction of the E-R Diagram, and the mapping of the database table. As a DBMS (DataBase Management System), Oracle 8i is employed to design and construct the database. The database design methodology is devised to apply for the several MDO(Multidisciplinary Design Optimization) techniques like MDF(MultiDisplinary Feasible), IDF(Individual Discipline Feasible), and CO(Collaborative Optimization). The defined process is demonstrated through a couple of design examples, including a simple numerical example and a UCAV(Unmanned Combat Aerial Vehicle) design optimization.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.1
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• pp.66-72
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• 2011

Injection pressure is an important factor in filling procedure for injection molded parts. In addition, weldlines should be avoided to successfully produce injection molded parts. In this study, we optimally obtained injection molding process parameters that minimize injection pressure. Then, we determined the thickness of the part to avoid weldlines. To solve the optimization problem proposed, we employed MAPS-3D (Mold Analysis and Plastics Solution-3 Dimension), a commercial CAE tool for injection molding analysis, and PIAnO (Process Integration, Automation, and Optimization) as a commercial PIDO (Process Integration and Design Optimization) tool. We integrated MAPS-3D into PIAnO, automated the analysis and design procedure, and performed optimization by employing PQRSM (Progressive Quadratic Response Surface Method) equipped in PIAnO. We successfully obtained optimization results, which demonstrates the effectiveness of our design method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1972-1986
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• 2017

With the advent of the knowledge economy, science and technology resources have played an important role in economic competition, and their optimal allocation has been regarded as very important across the world. Thus, allocation optimization research for regional science and technology resources is significant for accelerating the reform of regional science and technology systems. Regional science and technology resource allocation optimization is modeled as a double-layer optimization model: the entire system is characterized by top-layer optimization, whereas the subsystems are characterized by bottom-layer optimization. To efficaciously solve this optimization problem, we propose a mixed search method based on the orthogonal genetic algorithm and sensitivity analysis. This novel method adopts the integrated modeling concept with a combination of the knowledge model and heuristic search model, on the basis of the heuristic search model, and simultaneously highlights the effect of the knowledge model. To compare the performance of different methods, five methods and two channels were used to address an application example. Both the optimized results and simulation time of the proposed method outperformed those of the other methods. The application of the proposed method to solve the problem of entire system optimization is feasible, correct, and effective.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1408-1418
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• 2000

Gradient-based optimization methods are inefficient in applications which require expensive function evaluations, and useless in applications where objective and/or constraint functions are 'noisy' due to modeling and cumulative numerical inaccuracy since gradient evaluation results cannot be reliable. Moreover, it is difficult to be integrated with commercial analysis software, and they cannot be employed when only experimental analysis results are available. In this research an optimization program based on a response surface method has been developed to overcome the aforementioned difficulties. Various methods for design of experiments and new proposed approximation models are implemented in the program. The effectiveness of the optimization program is tested on several test problems and results are discussed.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1348-1356
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• 2017

Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.2
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• pp.135-146
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• 2017

This paper considers redundancy optimization problems of multi-level systems and reviews existing papers which proposed various optimization models and used different algorithms in this research area. Three different mathematical models are studied: Multi-level redundancy allocation (MRAP), multiple multi-level redundancy allocation, and availability-based MRAP models. Many meta-heuristics are applied to find optimal solutions in the several optimization problems. We summarized key idea of meta-heuristics applied to the existing MARP problems. Two extended models (MRAP with interval reliability of units and an integrated optimization problem of MRAP and preventive maintenance) are studied and further research ideas are discussed.