• CDE review
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• v.21 no.2
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• pp.39-49
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• 2015

This paper presents the single objective design optimization of plate-fin heat sink equipped with fan cooling system using Genetic Algorithm. The proper heat sink and fan model are selected based on the previous studies. And the thermal resistance of heat sinks and fan efficiency during operation are calculated according to specific design parameters. The objective function is combination of thermal resistance and fan efficiency which have been taken to measure the performance of the heat sink. And Decision making procedure is suggested considering life time of semiconductor and Fan Operating cost. And also Analytical Model used for optimization is validated by Fluent, Ansys 13.0 and this model give a quite reasonable and reliable design.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.138-144
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• 2011

The design flexibility and robustness have become key factors to handle various sources of uncertainties at the early phase of design. Even though designers are uncertain about which single values to specify, they usually have a preference for certain values over others. In the first and second reports of a four-part paper, a set-based design approach has been proposed for achieving design flexibility and robustness while capturing designer's preference, and its effectiveness has been illustrated with a simple vehicle side-door impact beam design problem. This report presents the applicability of the proposed design approach to the large-scale multi-objective design optimization with a successful implementation of real vehicle side-door structure design.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.235-240
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• 1998

A procedure for the optimal design of high quality rectifiers is introduced in this paper. The procedure is capable of producing different optimal designs for the same rectifier based on the objective performance required from that rectifier. A FORTRAN-based computer system designed to solve large-scale optimization problems was used in this work to obtain the optimal designs. The optimization program uses Wolfe algorithm in conjunction with a quasi-Newton algorithm as well as a projected augmented Lagrangian algorithm to solve the highly nonlinear optimization problem. The paper also introduces a detailed analysis and an application of the procedure on a boost-type zero-current switch (ZCS) single-switch three-phase rectifier introduced recently in the literature. The obtained results are compared with popular simulation packages (i. e. PSPICE and SIMCAD) to support the validity of the proposed concept.

• Steel and Composite Structures
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• v.48 no.5
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• pp.583-597
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• 2023

This paper introduces a novel approach to multi-material topology optimization (MTO) targeting in-plane bi-directional functionally graded (IBFG) non-uniform thickness Reissner-Mindlin plates, employing an alternative active phase approach. The mathematical formulation integrates a first shear deformation theory (FSDT) to address compliance minimization as the objective function. Through an alternating active-phase algorithm in conjunction with the block Gauss-Seidel method, the study transforms a multi-phase topology optimization challenge with multi-volume fraction constraints into multiple binary phase sub-problems, each with a single volume fraction constraint. The investigation focuses on IBFG materials that incorporate adequate local bulk and shear moduli to enhance the precision of material interactions. Furthermore, the well-established mixed interpolation of tensorial components 4-node elements (MITC4) is harnessed to tackle shear-locking issues inherent in thin plate models. The study meticulously presents detailed mathematical formulations for IBFG plates in the MTO framework, underscored by numerous numerical examples demonstrating the method's efficiency and reliability.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.85-93
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• 1996

Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. This objective is similar to that of optimization problems and thus, mathematical programming techniques may be applied to simulation. However, the application of mathematical programming techniques, e.g., the gradient methods, to simulation is compounded by the random nature of simulation responses and by the complexity of the statistical issues involved. In this paper, therefore, we explain the Reverse-Simulation method to optimize a simulation model in a single simulation run. First, we point the problem of the previous Reverse-Simulation method. Secondly, we propose the new algorithm to solve the previous method and show the efficiency of the proposed algorithm.

• Journal of Ocean Engineering and Technology
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• v.38 no.2
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• pp.53-63
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• 2024

Submerged breakwaters can be installed underneath floating structures to reduce the external wave loads acting on the structure. The objective of this study was to establish an optimization analysis framework to determine the corresponding shape and position of the submerged breakwater that can minimize or maximize the external forces acting on the floating structure. A two-dimensional frequency-domain boundary element method (FD-BEM) based on the linear potential theory was developed to perform the hydrodynamic analysis. A metaheuristic algorithm, the advanced particle swarm optimization, was newly coupled to the FD-BEM to perform the optimization analysis. The optimization analysis process was performed by calling FD-BEM for each generation, performing a numerical analysis of the design variables of each particle, and updating the design variables using the collected results. The results of the optimization analysis showed that the height of the submerged breakwater has a significant effect on the surface piercing body and that there is a specific area and position with an optimal value. In this study, the optimal values of the shape and position of a single submerged breakwater were determined and analyzed so that the external force acting on a surface piercing body was minimum or maximum.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1058-1063
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• 2004

This paper presents a new approach regarding thermal characteristics associated with a design of the high efficiency motor. Electrical conduction materials, such as coil and aluminum embedded in the core generate high heat exerting negative influence on both lifetime and performance of machine. Thus, it is necessary to design high efficiency motor considering heat transfer in order to improve motor performance and to be protected against overheating. In this paper, firstly, numerical analysis of electromagnetic field is carried out by the nonlinear transient finite element method (FEM). Secondly, the linear static FEA of magneto-thermal field is implemented by applying source current computed by the nonlinear transient analysis. FE results are validated in terms of electromagnetics and heat transfer by experiments. And then, the pseudo-transient topology optimization using a multi-objective function is performed. The proposed method is applied to a squirrel cage single-phase induction motor of the scroll compressor.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.