• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.5
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• pp.535-541
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• 2010

In this paper, we have derived a continuum-based adjoint design sensitivity of general performance functionals with respect to Young' modulus and heat conduction coefficient for steady-state nonlinear thermoelastic problems. An adjoint equation for temperature and displacement fields is defined for the efficient computation of the coupled field design sensitivity. Through numerical examples, we investigated the mesh dependency of the topology optimization method in the thermoelastic problems. Also, comparing the dominant loading cases of thermal and mechanical ones, the loading dependency of topology design optimization in coupled multi-physics problems is investigated.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.9 no.3
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• pp.410-425
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• 1997

Buildings are mostly under part load conditions causing an inefficient system operation in terms of energy consumption. It is critical to operate building air-conditioning system with a scientific or optimal manner which minimizes energy consumption and maintains thermal comfort by matching building sensible and latent loads. Little research has been performed in developing general methodologies for the optimal operation of air-conditioning system. Based on this research motivation, system simulation program was developed by adopting various equipment operating strategies which are energy efficient especially for humidity control in summer. A numerical optimization technique was utilized to search optimal solution for multi-independent variables and then linked to the developed system simulation model within a mam program. The main goal of the study is to provide a systematic framework and guideline for the optimal operation of air-conditioning system focusing on air-side. For given cooling loads and ambient outdoor conditions the optimal operating strategies of a commercial building are determined by minimizing a constrained objective function by a nonlinear programming technique. Desired space setpoint conditions were found through evaluating the trade-offs between comfort and system power consumption. The results show that supply airflow rate and compressor fraction play main roles in the optimization process. It was found that variable setpoint optimization technique could produce lower indoor humidity level demanding less power consumption which will be benefits for building applications of humidity problem.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1086-1092
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of a given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a mega model that has a regression model made by sampling data through simulation. In this paper, the neural network is used a mega model that have twenty-nine design variables and forty-six responses. After this mega model is constructed, multi-objective optimal solutions are achieved by using the differential evolution. This paper shows that this optimization method using the neural network and the differential evolution is a very efficient tool to solve the complex optimization problem.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.12
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• pp.1262-1269
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• 2008

Though multilayered foam-panel structures have been widely used to reduce sound transmission in various fields, most of the previous works to design them were conducted by repeated analyses or experiments based on initially given configurations or sequences. Therefore, it was difficult to obtain an optimal sequence of multilayered foam-panel structure yielding superior sound isolation capability. In this work, we propose a new design method to sequence a multi-panel structure lined with a poroelastic material having maximized sound transmission loss. Being formulated as a one-dimensional topology optimization problem fur a given target frequency, the optimal sequencing of panel-poroelastic layers is systematically carried out in an iterative manner. In this method, a panel layer is expressed as a limiting case of a poroelastic layer to facilitate the optimization process. This means that main material properties of a poroelastic material are treated as interpolated functions of design variable. The designed sequences of panel-poroelastic multilayer were shown to be significantly affected by the target frequencies; more panels were obtained at higher target frequency. The sound transmission loss of the system was calculated by the transfer matrix derived from Biot's theory.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.33-36
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• 2006

As the computing environment is rapidly improved, the interests of CFD are gradually focused on large-scale computation over complex geometry. Keeping pace with the trend, essential computational tools to obtain solutions of complex aerospace flow analysis and design problems are examined. An accurate and efficient flow analysis and design codes for large-scale aerospace problem are presented in this work. With regard to original numerical schemes for flow analysis, high-fidelity flux schemes such as RoeM, AUSMPW+ and higher order interpolation schemes such as MLP (Multi-dimensional Limiting Process) are presented. Concerning the grid representation method, a general-purpose basis code which can handle multi-block system and overset grid system simultaneously is constructed. In respect to design optimization, the importance of turbulent sensitivity is investigated. And design tools to predict highly turbulent flows and its sensitivity accurately by fully differentiating turbulent transport equations are presented. Especially, a new sensitivity analysis treatment and geometric representation method to resolve the basic flow characteristics are presented. Exploiting these tools, the capability of the proposed approach to handle complex aerospace simulation and design problems is tested by computing several flow analysis and design problems.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.10
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• pp.14-22
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• 2010

The most costly and time-consuming process in the fabrication of today's multi-layer circuit board is drilling interconnection holes between adjacent layers and via holes within a layer. Decreasing size of via holes being demanded and growing number of via holes per panel increase drilling costs. Component density and electronic functionality of today's multi-layer circuit boards can be improved with the introduction of cost-effective, variable depth laser drilled blind micro via holes, and interconnection holes. Laser technology is being quickly adopted into the circuit board industry but can be accelerated with the introduction of a true production laser drilling system. In order to get optimized condition for drilling to FPCB (Flexible Printed Circuit Board), we use various drill pattern as drill step. For productivity, we investigate drill path optimization method. And for the precise drilling the thermal drift of scanner and temperature change of scan system are tested.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.175-184
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• 2008

Uncertainties are unavoidable in engineering applications. In this paper we propose an alpha reliable multi-variable network design problem under demand uncertainty. In order to decide the optimal capacity enhancement, three performance measures based on 3E(Efficiency, Equity, and Environmental) are considered. The objective is to minimize the total budget required to satisfy alpha reliability constraint of total travel time, equity ratio, and total emission, while considering the route choice behavior of network users. The problem is formulated as the chance-constrained model for application of alpha confidence level and solved as a lexicographic optimization problem to consider the multi-variable. A simulation-based genetic algorithm procedure is developed to solve this complex network design problem(NDP). A simple numerical example ispresented to illustrate the features of the proposed NDP model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.2
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• pp.433-442
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• 1995

For the line search of a multi-variable optimization, an efficient algorithm is presented. The algorithm sequentially employs several polynomial approximations such as 2-point quadratic interpolation, 3-point cubic interpolation/extrapolation and 4-point cubic interpolation/extrapolation. The order of polynomial function is automatically increased for improving the accuracy of approximation. The method of approximation (interpolation or extrapolation) is automatically switched by checking the slope information of the sample points. Also, for selecting the initial step length along the descent vector, a new approach is presented. The performance of the proposed method is examined by solving typical test problems such as mathematical problems, mechanical design problems and dynamic response problems.