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Pohang, South Korea

Lee K.K.,Consulting Team | Han S.H.,Dong - A University
Transactions of the Korean Society of Mechanical Engineers, A | Year: 2012

Wind energy is becoming one of the most preferable alternatives to conventional sources of electric power that rely on fossil fuels. For stable electric power generation, constant rotating speed control of a wind turbine is performed through pitch control and stall control of the turbine blades. Recently, variable pitch control has been implemented in modern wind turbines to harvest more energy at variable wind speeds that are even lower than the rated one. Although wind turbine pitch controllers are currently optimized using a step response via the Ziegler-Nichols autotuning process, this approach does not satisfy the requirements of variable pitch control. In this study, the variable pitch controller was optimized by a genetic algorithm using a neural network model that was constructed by the Latin Hypercube sampling method to improve the Ziegler-Nichols auto-tuning process. The optimized solution shows that the root mean square error, rise time, and settle time are respectively improved by more than 7.64%, 15.8%, and 15.3% compared with the corresponding initial solutions obtained by the Ziegler-Nichols auto-tuning process. ©2012 The Korean Society of Mechanical Engineers. Source


Lee K.-K.,Consulting Team | Lee K.-H.,Dong - A University | Woo E.-T.,Dong - A University | Han S.-H.,Dong - A University
International Journal of Precision Engineering and Manufacturing | Year: 2014

Concept design requiring complicated feed-forward and feed-back processes makes it difficult for engineers to determine the global behaviors of design variables and objective functions in a design space. Although design of experiments and response surface models have been applied to overcome these problems, the design variables satisfying the objective functions can't be found due to violations of given constraints. In this study, a new optimization process, i.e., the GEO (Generate, Explore and Optimize) process for the concept design of a tactical missile, based on a MOGA (Multi-Objective Genetic Algorithm) was proposed, which was first adapted to generate a Pareto Front in order to simultaneously satisfy the constraints and the objective functions. In the first step, the weights between the objective functions were determined by using an AHP (Analytic Hierarchy Process). Then, the design space exploration followed, and was implemented by surrogate models constructed from the Pareto Front with a neural network. In the last step, a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and a desirability function were applied together to determine the optimal solution. The TOPSIS merged the multi-objective design problem to a single entity, and the desirability function normalized each objective function. © 2014 Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg. Source


Lee K.K.,Consulting Team | Park C.K.,Korea Railroad Research Institute | Kim G.Y.,Dong - A University | Lee K.H.,Dong - A University | And 2 more authors.
Transactions of the Korean Society of Mechanical Engineers, A | Year: 2013

A robust optimization is only one of the ways to minimize the effects of variances in design variables on the objective functions at the preliminary design stage. To predict the variances and to formulate the probabilistic constraints are the most important procedures for the robust optimization formulation. Though several methods such as the process capability index and the six sigma technique were proposed for the prediction and formulation of the variances and probabilistic constraints, respectively, there are few attempts using a percent defective which has been widely applied in the quality control of the manufacturing process for probabilistic constraints. In this study, the robust optimization for a lower control arm of automobile vehicle was carried out, in which the design space showing the mean and variance sensitivity of weight and stress was explored before robust optimization for a lower control arm. The 2nd order Taylor expansion for calculating the standard deviation was used to improve the numerical accuracy for predicting the variances. Simplex algorithm which does not use the gradient information in optimization was used to convert constrained optimization into unconstrained one in robust optimization. © 2013 The Korean Society of Mechanical Engineers. Source


Lee K.-K.,Consulting Team | Ro Y.-C.,Dong - A University | Kim Y.-G.,Dong - A University | Lee K.-H.,Dong - A University | Han S.-H.,Dong - A University
International Journal of Precision Engineering and Manufacturing | Year: 2014

A direct-drive generator that uses a permanent magnet provides higher energy density and fewer constraint conditions. For a sufficient production of output power, however, an electronic machine system with a very large diameter must be used due to the need for operation at a low rotational speed. The structural weight of a direct-drive generator for a large-scale wind turbine can be as much as 80% of the total weight. Among the direct-drive generators, the AFPM (Axial Flux Permanent Magnet)-type machine has been the most attractive due to its higher torque effect per unit volume and higher power density. In this study, shape optimization was accomplished based on the desirability function for a direct-drive generator in 2.5MW wind turbine using the AFPM-type machine proposed in this study. Electro-magnetic and structural-coupling analyses were carried out to determine the optimal design variables that would meet the requirements of structural stiffness, such as limitations in air-gap clearance, as well as conforming to acceptable global mechanical behaviors. Compared with the initial generator, the structural weight and stress of the proposed model was reduced by 13.6 and 21% under conditions that satisfied the constraint requirements of the limitations of air-gap clearance. © 2014, Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg. Source


Lee K.-K.,Consulting Team | Ro Y.-C.,Dong - A University | Han S.-H.,Dong - A University
International Journal of Precision Engineering and Manufacturing | Year: 2014

Tolerance optimization that considers variances of design variables should be performed before beginning the manufacturing process from a cost-effective perspective in the design process. The authors used a genetic algorithm and the process capability index (Cpk) to solve the robust objectives and probability constraints and to formulate a constrained optimization problem into an unconstrained one. The design space provided by the Cpk-values of weight and stress on the lower arm of a vehicle's suspension was explored by using the central composite design method and the 2nd order Taylor series expansion. The optimal solutions were found via the genetic algorithm, in which the Cpk-values took into account the variances occurring in a design variable's tolerances. The mean and standard deviation of Mass and Smax were predicted by using the 2nd order Taylor series expansion and the 2nd order polynomial response surface models generated from the central composite design method. The Cpk of Mass and Smax were calculated, where the Pareto set was generated by maximizing the Cpk-values via the MOGA (Multi-Objective Genetic Algorithm). From the Pareto set, optimal alternatives were selected and verified by simulated results from FE (Finite Element) analysis and Monte-Carlo simulation. © 2014 Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg. Source

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