• 한국유체기계학회 논문집
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• 제16권2호
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• pp.35-41
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• 2013

Inlet part of a printed circuit heat exchanger has been optimized by using three-dimensional Reynolds-Averaged Navier-Stokes analysis and surrogate modeling techniques. Kriging model has been used as the surrogate model. The objective function for the optimization has been defined as a linear combination of uniformity of mass flow rate and the pressure loss with a weighting factor. For the optimization, the angle of the inlet plenum wall, radius of curvature of the inlet plenum wall, and width of the inlet pipes have been selected as design variables. Twenty six design points are obtained by Latin Hypercube Sampling in design space. Through the optimization, considerable improvement in the objective function has been obtained in comparison with the reference design of PCHE.

• Structural Engineering and Mechanics
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• 제51권2호
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• pp.229-248
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• 2014

In this paper, the development of a new optimization software for finite element model updating of engineering structures titled as FemUP is described. The program is used for computational FEM model updating of structures depending on modal testing results. This paper deals with the FE model updating procedure carried out in FemUP. The theoretical exposition on FE model updating and optimization techniques is presented. The related issues including the objective function, constraint function, different residuals and possible parameters for FE model updating are investigated. The issues of updating process adopted in FemUP are discussed. The ideas of optimization to be used in FE model updating application are explained. The algorithm of Sequential Quadratic Programming (SQP) is explored which will be used to solve the optimization problem. The possibilities of the program are demonstrated with a three dimensional steel frame model. As a result of this study, it can be said that SQP algorithm is very effective in model updating procedure.

• International Journal of Precision Engineering and Manufacturing
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• 제3권1호
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• pp.13-19
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• 2002

Although the design of magnetic bearing needs a systematic optimization due to several design variables, constraints, geometric limitations, nonlinearities, and so on, the present designs for magnetic bearings have been conducted in the linear region of the characteristics for magnetic materials by trial and error considering design constraints. This article, therefore, provides the possibility of a genetic algorithm(GA) based optimization with two dimensional nonlinear finite element magnetic field analysis for the design of a radial heteropolar magnetic bearing. The magnetic bearing design by GA based optimization makes good agreements with that by a commercial optimization software DOT using the sensitivity analysis.

• 한국정밀공학회지
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• 제16권8호
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• pp.53-58
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• 1999

Although the design of magnetic bearing needs a systematic optimization du e to several design variables, constraints, geometric limitations, nonlinearities, and so on, the present for magnetic bearings have been conducted in the linear region of the characteristics for magnetic by trial and error considering design constraints. This article, therefore, provides the possibility of a genetic algorithm(GA) based optimization with two dimensional-nonlinear finite element magnetic field analysis for design of a radial heteropolar magnetic bearing. The magnetic bearing design by GA based optimization makes good agreements with that by a commercial optimization software DOT using the sensitivity analysis.

• 한국유체기계학회 논문집
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• 제12권4호
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• pp.44-53
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• 2009

Numerical design optimization of a fan-shaped hole for film-cooling has been carried out to improve film-cooling effectiveness by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. The injection angle of hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. Twenty training points are obtained by Latin Hypercube sampling for three design variables. Sequential quadratic programming is used to search for the optimal point from the constructed surrogate. The film-cooling effectiveness has been successfully improved by the optimization with increased value of all design variables as compared to the reference geometry.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 2003년도 유체기계 연구개발 발표회 논문집
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• pp.639-645
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• 2003

Design optimization of a transonic compressor rotor (NASA rotor 37) using response surface method and three-dimensional Navier-Stokes analysis has been carried out in this work. Baldwin-Lomax turbulence model was used in the flow analysis. Two design variables were selected to optimize the stacking line of the blade, and mass flow was used as a design variable, as well, to obtain new design point at peak efficiency. Data points for response evaluations were selected by D-optimal design, and linear programming method was used for the optimization on the response surface. As a main result of the optimization, adiabatic efficiency was successfully improved, and new design mass flow that is appropriate to an improved blade was obtained. Also, it is found that the design process provides reliable design of a turbomachinery blade with reasonable computing time.

• International Journal of Automotive Technology
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• 제8권5호
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• pp.563-573
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• 2007

Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.

• 한국의학물리학회지:의학물리
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• 제2권2호
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• pp.121-128
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• 1991

뇌정위적 방사선 수술 시 정확한 3차원적 선량분포에 대한 정보가 필요한다. 3차원적 치료계획은 최적선량분포를 얻기위한 것이며 환자 데이타, 선량분포, 방사선 조사 요소들에 대한 3차원적인 관계를 다루어야만 한다. 원형 조사면에 대한 single 조사면 선량 데이타와 3차원 선량 알고리듬을 이용하여 non-coplanar moving arcs 에 대한 3차원적 선량모델이 개발되었다. 뇌정위적 방사선 수술시 3차원 선량 알고리듬의 적용과 여러경우에 대한 응용에 대하여 논의되어진다.

• 전자공학회논문지B
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• 제30B권5호
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• pp.18-26
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• 1993

This paper presents the design method for two-dimensional FIR digital filter using optimization scheme. The proposed design method is to extend the optimal one-dimensional filter design algorithm proposed by Parks and McClellan to two-dimensional case. When extending one-dimensional design scheme to two-dimensional one, some problems occur. In this paper we solved the problems by using the least square error model, the two-dimensional Lagrange interpolation, and the modified alternation theory. As a result, the equi-ripple FIR filter is obtained that is more optimal and more specific than the conventional methods.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.