• Structural Engineering and Mechanics
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• v.55 no.5
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• pp.901-911
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• 2015

The goal of this study is to investigate computational convergence of optimal solutions, with respect to optimality criteria (OC) method and methods of moving asymptotes (MMA) as optimization model for non-linear programming of material topology optimization using an acceleration method that makes design variables rapidly move toward almost 0 and 1 values. 99 line topology optimization MATLAB code uses loop vectorization and memory pre-allocation as properly exploiting the strengths of MATLAB and moves portions of code out of the optimization loop so that they are only executed once as restructuring the program. Numerical examples of a simple beam under a lateral load and a given material density limitation provide merits and demerits of the present OC and MMA for 99 line topology optimization code of continuous material topology optimization design.

• Computational Structural Engineering
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• v.8 no.2
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• pp.123-132
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• 1995

Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.434-441
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• 2017

Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the "black-box" code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.

• The KIPS Transactions:PartA
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• v.14A no.3 s.107
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• pp.167-172
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• 2007

VM(Virtual Machine) can be considered as a software processor which interprets the machine code. Also, it is considered as a conceptional computer that consists of logical system configuration. But, the execution speed of VM system is much slower than that of a real processor system. So, it is very important to optimize the code for virtual machine to enhance the execution time. Especially the optimizer for a virtual machine code on embedded devices requires the highly efficient performance to the ordinary optimizer in the respect to the optimized ratio about cost. Fundamentally, functions and basic blocks which influence the execution time of virtual machine is found, and then an optimization for them nay get the high efficiency. In this paper, we designed and implemented the optimizer for the virtual(or abstract) machine code(VMC) using profiling. Firstly, we defined the profiling information which is necessary to the optimization of VMC. The information can be obtained from dynamically executing the machine code. And we implemented VMC optimizer using the profiling information. In our implementation, the VMC is SIL(Standard Intermediate Language) that is an intermediate code of EVM(Embedded Virtual Machine). Also, we tried a benchmark test for the VMC optimizer and obtained reasonable results.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1556-1564
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• 2005

Various optimization techniques are deployed in the compilation process of a source program for improving the program's execution speed and reducing the size of the source code. Of the optimization pattern matching techniques, the string pattern matching technique involves finding an optimal pattern that corresponds to the intermediate code. However, it is deemed inefficient due to excessive time required for optimized pattern search. The tree matching pattern technique can result in many redundant comparisons for pattern determination, and there is also the disadvantage of high cost involved in constructing a code tree. The objective of this paper is to propose a table-driven code optimizer using the DFA(Deterministic Finite Automata) optimization table to overcome the shortcomings of existing optimization techniques. Unlike other techniques, this is an efficient method of implementing an optimizer that is constructed with the deterministic automata, which determines the final pattern, refuting the pattern selection cost and expediting the pattern search process.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.7
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• pp.2347-2355
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• 1996

Purpose of the present study is to develop a computational design program for shape optimization, combining the numerical optimization technique with the flow analysis code. The present methodology is then validated in three cases of aerodynamic shape optimization. In the numerical optimization, a feasible direction optimization algorithm and shape functions are considered. In the flow analysis, the Navier-Stokes equations are discretized by a cell-centered finite volume method, and Roe's flux difference splitting TVD scheme and ADI method are used. The developed design code is applied to a transonic channel flow over a bump, and an external flow over a NACA0012 airfoil to minimize the wave drag induced by shock waves. Also a separated subsonic flow over a NACA0024 airfoil is considered to determine a maximum allowable thickness of the airfoil without separation.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.771-776
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• 2001

By matching a well established fast throughflow code, with standard loss correlations, and an efficient optimization algorithm, a new design system has been developed, which optimizes inlet and exit flow-field parameters for each blade row of a multistage axial flow turbine. The compressible steady state inviscid throughflow code based on streamline curvature method is suitable for fast and accurate flow calculation and performance prediction of a multistage axial flow turbine. A general purpose hybrid constrained optimization package, iSIGHT has been used, which includes the following modules: genetic algorithm, simulated annealing, modified method of feasible directions. The design system has been demonstrated using an example of a 5-stage low pressure steam turbine for 800MW thermal power plant previously designed by HANJUNG. The comparison of computed performance of initial and optimized design shows significant improvement in the turbine efficiency.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.64.2-64.2
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• 2005

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.7 s.238
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• pp.1013-1021
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• 2005

In this paper, we developed an automatic design optimization system for parametric shape optimization of cooling passages inside axial turbine blades. A parallel three-dimensional thermoelasticity finite element analysis code from an open source system was used to perform automatic thermal and stress analysis of different blade configuration. The developed code was connected to an evolutionary optimizer and built in a design optimization system. Using the optimization system, 279 feasible and optimal solutions were searched. It is provided not only one best solution of the searched solutions, but also information of variation structure and correlation of the 279 solutions in function, variable, and real design spaces. To explore design information, it is proposed a new interpretation approach based on evolutionary clustering and principal component analysis. The interpretation approach might be applicable to the increasing demands in the general area of design optimization.

• 한국전산유체공학회:학술대회논문집
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• 1995.10a
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• pp.154-159
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• 1995

Numerical optimization technique with Navier-Stokes code has been used to reduce the drag of conventional ogival nose. Forebody optimizations are performed for supersonic laminar and turbulent flow conditions. To alleviate the computing time of aerodynamic drag calculation, axisymmetric boundary condition is implemented in the 3-dimensional Navier-Stokes code. The automated optimization procedure with gradient based method results in a drag reduction of $4\;\%$.