• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1199-1208
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• 2005

Nowadays, it is performed actively to optimize by using an approximate model. This is called the approximate optimization. In addition, the sequential approximate optimization (SAO) is the repetitive method to find an optimum by considering the convergence of an approximate optimum. In some recent studies, it is proposed to increase the fidelity of approximate models by applying the sequential sampling. However, because the accuracy and efficiency of an approximate model is directly connected with the design area and the termination criteria are not clear, sequential sampling method has the disadvantages that could support an unreasonable approximate optimum. In this study, the SAO is executed by using trust region, Kriging model and Optimal Latin Hypercube design (OLHD). Trust region is used to guarantee the convergence and Kriging model and OLHD are suitable for computer experiment. finally, this SAO method is applied to various optimization problems of highly nonlinear mathematical functions. As a result, each approximate optimum is acquired and the accuracy and efficiency of this method is verified by comparing with the result by established method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.95-100
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• 2008

An active micro-mixer, which is composed of an oscillating micro-stirrer in the micro-channel to provide effective mixing was optimized. The effects of molecular diffusion and disturbance by the stirrer were considered with regard to two types of mixer models: the simple straight micro-channel and micro-channel with an oscillating stirrer. Two types of mixer models were studied by analyzing mixing behaviors such as their interaction after the stirrer. The mixing was calculated by Lattice Boltzmann methods using the D2Q9 model. In this study, the time-averaged mixing index formula was used to estimate the mixing performance of time-dependent flow. The mixing indices of the two models were compared. From the results, it was found that the mixer with an oscillating stirrer was much more enhanced and stabilized. Therefore, an approximate optimization of an active micro-mixer with an oscillating stirrer was performed using Kriging method with OLHD(Optimal Latin Hypercube Design) in order to determine the optimal design variables. The design parameters were established as the frequency, the length and the angle of the stirrer. The optimal values were obtained as 1.0346, 0.66D and $\pm45^{\circ}$, respectively. It was found that the mixing index of the optimal design increased by 88.72% compared with that of the original design.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.6
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• pp.468-476
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• 2016

In this study, the conic winglet which is made by rotating wing tip airfoil by each 3 axis is applied to the dual type combined fan to reduce the wing tip leakage loss. Computational Fluid Dynamics is used to calculate the loss and optimum technique is used to get minimum loss. Optimization results shows that total pressure loss coefficient was reduced by 3.4 %, and optimization model was a bended shape at the end of wing forward to pressure side.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.46-51
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• 2012

Injection pressure, an important factor in filling process, should be minimized to enhance injection molding quality. Injection pressure can be controlled by valve gate open timing. In this work, we decided the valve gate open timing to minimize the injection pressure. To solve this design problem, we integrated MAPS-3D (Mold Analysis and Plastic Solution-3Dimension), a commercial injection molding CAE tool, to PIAnO (Process Integration, Automation and Optimization), a commercial PIDO (Process Integration, and Design Optimization) tool using the file parsing method. In order to reduce computational cost, we performed an approximate optimization using meta-models that replaced expensive computer simulations. At first, we carried out DOE (Design of Experiments) using OLHD (Optimal Latin Hypercube Design) available in PIAnO. Then, we built Kriging models using the simulation results at the sampling points. Finally, we used micro GA (Genetic Algorithm) available in PIAnO. Using the proposed design approach, the injection pressure has been reduced by 13.7% compared to the initial one. This design result clearly shows the validity of the proposed design approach.