• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.756-760
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• 2008

Optimum shape of Double-cone supersonic inlet is studied by using numerical methods. Double-cone intake shape is used for the design optimization study. And the total pressure recovery at the exit is used to assess the aerodynamic performance of the inlet.

• Journal of Mechanical Science and Technology
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• v.20 no.1
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• pp.133-146
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• 2006

Collaborative optimization (CO) is a multi-level decomposed methodology for a large-scale multidisciplinary design optimization (MDO). CO is known to have computational and organizational advantages. Its decomposed architecture removes a necessity of direct communication among disciplines, guaranteeing their autonomy. However, CO has several problems at convergence characteristics and computation time. In this study, such features are discussed and some suggestions are made to improve the performance of CO. Only for the system level optimization, genetic algorithm is used and gradient-based method is used for subspace optimizers. Moreover, response surface models are replaced as analyses in subspaces. In this manner, CO is applied to aero-structural design problems of the aircraft wing and its results are compared with the multidisciplinary feasible (MDF) method and the original CO. Through these results, it is verified that the suggested approach improves convergence characteristics and offers a proper solution.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.4
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• pp.247-254
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• 2015

Ride comfort is one of the major factors in evaluating the performance of the vehicle. Tire is closely related to the ride comfort of the vehicle as the only parts in contact with the road surface directly. Vertical stiffness which is one of the parameters to evaluate the tire performance is great influence on the ride comfort. In general, the lower the vertical stiffness, the ride comfort is improved. Research for improving the ride comfort has been mainly carried out by optimizing the shape of the pneumatic tire. However, demand for safety of the vehicle has been increased recently such as a run-flat tire which is effective in safety improvement. But a run-flat tire have trouble in practical use because of poor ride comfort than general tire. Therefore, In this paper, the research was carried out for improving the ride comfort through the optimization of the SIR shape inside a run-flat tire. Meta-model was generated by using the design of experiment and it was able to reduce the time for the finite element analysis of optimization. In addition, Shape optimization for improving the ride comfort was performed by using the genetic algorithm which is one of the global optimization techniques.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.359-366
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• 2005

Most engineering optimization are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithm, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search(HS) meta-heuristic algorithm-based approach for structural size optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Two classical space truss optimization problems are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to structural engineering problems than those obtained using current algorithms.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.4
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• pp.8-15
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• 2013

Recently many kinds of electric vehicles have been developed as many governments demand the environmental friendly vehicles. In this paper, study of load optimization for the electric vehicle which has multiple axle power system was conducted. For the analysis of the vehicle which has three or four driving axles, a method based on the geometry and assumptions that considering axles as a spring model and normal forces of the axles are proportional to the displacement of the axles was applied with basic vehicle dynamics. With the developed vehicle analysis technique, algorithm to find the optimal motor operating points was developed. Using this algorithm, it was possible to find the optimization of vehicle load distribution for multiple axles according to the driving cycles. Also, control logic for the vehicle can be developed based on the optimization simulation results.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2019-2024
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• 2014

This paper presents a method to optimize the design of a planar-type antenna and reduce the number of design parameters for rapid computation. The electromagnetic characteristics of the structure are analyzed, and Taguchi method is used to identify critical design parameters. Adaptive particle swarm optimization, which has a faster convergence rate than particle swarm optimization, is used to achieve the design goal effectively. A compact dual-band USB dongle antenna is tested to verify the advantage of the proposed method. In this case, we can use only five selected geometrical parameters instead of eighteen to accelerate the optimization of the antenna design. The 10 dB bandwidth for return loss ranges from 2.3 GHz to 2.7 GHz and from 5.1 GHz to 5.9 GHz, covering all the WiBro, Bluetooth, WiMAX, and 802.11 b/g/n WLAN bands in both simulation and measurement. The optimization process enables the antenna design to achieve the required performance with fewer design parameters.

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1099-1120
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• 2014

In this paper, reliability-based design optimization (RBDO) of structures is addressed. For this purpose, the global search and optimization capabilities of genetic algorithm (GA) are combined with the efficiency and reasonable accuracy of an advanced moment-based finite element reliability method. For performing RBDO, three variants of GA including a real-coded, a binary-coded and an improved binary-coded GA are developed. In these methods, GA performs (finite element) reliability analyses to evaluate reliability constraints. For truss structures which include finite element modeling, reliability constraints are evaluated using finite element reliability analysis. Response sensitivity required for finite element reliability analysis is obtained by direct differentiation method (DDM) rather than finite difference method (FDM). The proposed methods are examined within four standard test examples and real-world design problems. The results illustrate the superiority and efficiency of the improved binary-coded GA. Results also illustrate that DDM significantly reduces the computational cost and improves the efficiency of the optimization procedure.

• Steel and Composite Structures
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• v.21 no.3
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• pp.501-515
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• 2016

Seismic analysis for steel frame structure with brace configuration using topology optimization based on truss-like material model is studied. The initial design domain for topology optimization is determined according to original steel frame structure and filled with truss-like members. Hence the initial truss-like continuum is established. The densities and orientation of truss-like members at any point are taken as design variables in finite element analysis. The topology optimization problem of least-weight truss-like continuum with stress constraints is solved. The orientations and densities of members in truss-like continuum are optimized and updated by fully-stressed criterion in every iteration. The optimized truss-like continuum is founded after finite element analysis is finished. The optimal bracing system is established based on optimized truss-like continuum without numerical instability. Seismic performance for steel frame structures is derived using dynamic time-history analysis. A numerical example shows the advantage for frame structures with brace configuration using topology optimization in seismic performance.

• Steel and Composite Structures
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• v.27 no.3
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• pp.297-310
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• 2018

A simple and efficient numerical optimization approach for the lightweight optimal design of composite laminated beams is presented in this paper. The proposed procedure is a combination between the finite element method (FEM) and a global optimization algorithm developed recently, namely Jaya. In the present procedure, the advantages of FEM and Jaya are exploited, where FEM is used to analyze the behavior of beam, and Jaya is modified and applied to solve formed optimization problems. In the optimization problems, the objective aims to minimize the overall weight of beam; and fiber volume fractions, thicknesses and fiber orientation angles of layers are selected as design variables. The constraints include the restriction on the first fundamental frequency and the boundaries of design variables. Several numerical examples with different design scenarios are executed. The influence of the design variable types and the boundary conditions of beam on the optimal results is investigated. Moreover, the performance of Jaya is compared with that of the well-known methods, viz. differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The obtained results reveal that the proposed approach is efficient and provides better solutions than those acquired by the compared methods.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.359-370
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• 2017

This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.