• 한국전산유체공학회:학술대회논문집
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• 2006.05a
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• pp.268-270
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• 2006

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.1
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• pp.9-18
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• 2007

This study shows an implementation of partial holes in an initial design domain in order to improve convergences of topology optimization algorithms. The method is associated with a bubble method as introduced by Eschenauer et al. to overcome slow convergence of boundary-based shape optimization methods. However, contrary to the bubble method, initial holes are only implemented for initializations of optimization algorithm in this approach, and there is no need to consider a characteristic function which defines hole's deposition during every optimization procedure. In addition, solid and void regions within the initial design domain are not fixed but merged or split during optimization Procedures. Since this phenomenon activates finite changes of design parameters without numerically calculating movements and positions of holes, convergences of topology optimization algorithm can be improved. In the present study, material topology optimization designs of Michell-type beam utilizing the initial design domain with initial holes of varied sizes and shapes is carried out by using SIMP like a density distribution method. Numerical examples demonstrate the efficiency and simplicity of the present method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.1 s.244
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• pp.66-75
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• 2006

Nonlinear response structural optimization using equivalent static loads (NROESL) has been proposed. Nonlinear response optimization is solved by sequential linear response optimization with equivalent static loads which are generated from the nonlinear responses and linear stiffness matrix. The linear stiffness matrix should be obtained in NROESL, and this process can be fairly difficult for some applications. Proportional transformation of loads (PTL) is proposed to overcome the difficulties. Equivalent static loads are obtained by PTL. It is the same as NROESL except for the process of calculating equivalent static loads. PTL is developed for large-scale probems. First, linear and nonlinear responses are evaluated from linear and nonlinear analyses, respectively. At a DOF of the finite element method, the ratio of the two responses is calculated and an equivalent static load is made by multiplying the ratio and the loads for linear analysis. Therefore, the mumber of the equivalent static loads is as many as that of DOF's and an equivalent static load is used with the reponse for the corresponding DOF in the optimization process. All the equivalent static loads are used as multiple loading conditions during linear response optimization. The process iterates until it converges. Examples are solved by using the proposed method and the results are compared with conventional methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.3
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• pp.311-318
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• 2015

A nonlinear dynamic response structural optimization process is proposed for the television (TV) packing system that protects the damage from a drop situation using the equivalent static loads (ESLs). Topology optimization using ESLs is carried out for conceptual design, and shape optimization using stress ESLs for a virtual model is performed for detailed design. Stress ESLs are static loads that generate the same displacement as well as the stress fields of linear static analysis as those of nonlinear dynamic analysis. Thus, the response of nonlinear dynamic analysis can be utilized as a constraint in the linear static structural optimization. An actual example is solved to validate the process. The drop test of a television packaging system is analyzed by LS-DYNA, and NASTRAN is used for optimization.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.4
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• pp.16-22
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• 2013

In this study, we propose the method of optimally changing material of BIW for minimizing weight while satisfying vehicle requirements on static stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimization, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the linear polynomial regression (PR) model. Using the linear PR model, optimization is carried out an evolutionary algorithm (EA) that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 44.8% while satisfying all the design constraints.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.5
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• pp.501-507
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• 2010

Structural optimization is performed to minimize the weight of a RC building structure, which has eight floors above ground and three underground, under gravity, wind, and seismic loads. Design optimization problem is formulated to find the values of the design variables that minimize the volume while satisfying various design and side constraints. To solved the optimization problem posed, several design techniques equipped in PIAnO, a commercial PIDO tool, are used. DOE is used to generate training points and structural analysis is performed using MIADS Gen, a general-purpose structural analysis CAE tool. Then, meta-models are generated from structural analysis results and accuracies of meta-models are evaluated. Next, design optimization is performed by using the verified meta-models and optimization technique equipped in PIAnO. Finally, we obtained optimal results, which could demonstrate the effectiveness of our design method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.4
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• pp.412-418
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• 2011

Reliability-based Topology optimization(RBTO) is to get an optimal design satisfying uncertainties of design variables. Although RBTO based on homogenization and density distribution method has been done, RBTO based on BESO has not been reported yet. This study presents a reliability-based topology optimization(RBTO) using bi-directional evolutionary structural optimization(BESO). Topology optimization is formulated as volume minimization problem with probabilistic displacement constraint. Young's modulus, external load and thickness are considered as uncertain variables. In order to compute reliability index, four methods, i.e., RIA, PMA, SLSV and ADL(adaptive-loop), are used. Reliability-based topology optimization design process is conducted to obtain optimal topology satisfying allowable displacement and target reliability index with the above four methods, and then each result is compared with respect to numerical stability and computing time. The results of this study show that the RBTO based on BESO using the four methods can effectively be applied for topology optimization. And it was confirmed that DLSV and ADL had better numerical efficiency than SLSV. ADL and SLSV had better time cost than DLSV. Consequently, ADL method showed the best time efficiency and good numerical stability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.568-583
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• 2015

In this paper, the conceptual design procedure and results of Earth observation satellite through Multidisciplinary Design Optimization (MDO) are described. The conceptual design equations for major parameters are developed based on the established database of Earth observation satellite so far. The MDO conceptual design tool for Earth observation satellite was developed by applying the Collaborative Optimization (CO) architecture amongst several MDO architecture techniques available today. The objective for this research was set to minimize the total mass of satellite as well as satisfy all design constraints by utilizing the Sequential Quadratic Programming (SQP) algorithm. Eventually the effectiveness of MDO conceptual design tool was verified through proposing a comparison between the conceptual design results with MDO applied and the design specification of ASNARO-1 & IKONOS-2 Earth observation satellite.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.4
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• pp.363-370
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• 2014

A new NURBS-based shape optimization method is proposed. Most shape optimization studies consider only control point positions as design variables. Some shape optimization processes present problems with mesh quality and convergence when control points are constrained to a limited space. If the weights of the control points are regarded as additional design variables, it should be possible to attain a better degree of shape control. In this study, positions and weights of NURBS control points are used as design variables, and a shape optimization algorithm incorporates position optimization and weight optimization steps. This method is applied to shape optimization benchmarking problems to verify its advantages.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.821-826
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• 2011

CAE-based structural optimization techniques are applied for the design of a lightweight crane. The boom of the crane is designed by shape optimization with the shape of the cross section of the boom as the design variable. The design objective is mass minimization, and the static strength and dynamic stiffness of the system are set as the design constraints. Hyperworks, a commercial analysis and optimization software, is used for shape and topology optimization. In order to consistently change the shape of the elements of the boom with respect to the change in the shape of its cross section, the morphing function in Hyperworks is used. The support of the boom of the original model is simplified to model the design domain for topology optimization, which is discretized by using three-dimensional solid elements. The final result after shape and topology optimization is 19% and 17% reduction in the masses of the boom and support, respectively, without a deterioration in the system stiffness.