• International Journal of Automotive Technology
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• v.8 no.1
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• pp.93-102
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• 2007

The frontal crash optimization of an engine room member using the response surface method was studied. The engine room member is composed of the front side member and the sub-frame. The thicknesses of the panels on the front side member and the sub-frame were selected as the design variables. The purpose of the optimization was to reduce the weight of the structure, under the constraint that the objective quantity of crash energy is absorbed. The response surface method was used to approximate the crash behavior in mathematical form for optimization procedure. To research the effect of the regression method, two different methodologies were used in constructing the response surface model, the least square method and the moving least square method. The optimum with the two methods was verified by the simulation result. The precision of the surrogate model affected the optimal design. The moving least square method showed better approximation than the least square method. In addition to the deterministic optimization, the reliability-based design optimization using the response surface method was executed to examine the effect of uncertainties in design variables. The requirement for reliability made the optimal structure be heavier than the result of the deterministic optimization. Compared with the deterministic optimum, the optimal design using the reliability-based design optimization showed higher crash energy absorption and little probability of failure in achieving the objective.

• Structural Engineering and Mechanics
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• v.55 no.6
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• pp.1121-1137
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• 2015

High powered computers and engineering computer systems allow designers to routinely simulate complex physical phenomena. The presented work deals with the analysis of two finite element method optimization techniques (First Order Method-FOM and Subproblem Approximation Method-SAM) implemented in the individual Design Optimization module in the Ansys software to analyze the behavior of real problems. A design optimization is a difficult mathematical process, intended to find the minimum or maximum of an objective function, which is mostly based on iterative procedure. Using optimization techniques in engineering designs requires detailed knowledge of the analyzed problem but also an ability to select the appropriate optimization method. The methods embedded in advanced computer software are based on different optimization techniques and their efficiency is significantly influenced by the specific character of a problem. The efficiency, robustness and accuracy of the methods are studied through strictly convex two-dimensional optimization problem, which is represented by volume minimization of two bars' plane frame structure subjected to maximal vertical displacement limit. Advantages and disadvantages of the methods are described and some practical tips provided which could be beneficial in any efficient engineering design by using an optimization method.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.8
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• pp.224-232
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• 1999

Topology optimization has evolved into a very efficient concept design tool and has been incorporated into design engineering processes in many industrial sectors. In recent years, topology optimization has become the focus of structural design community and has been researched and applied widely both in academia and industry. There are mainly tow approaches for topology optimization of continuum structures ; homogenization and density methods. The homogenization method is to compute is to compute an optimal distribution of microstructures in a given design domain. The sizes of the micro-calvities are treated as design variables for the topology optimization problem. the density method is to compute an optimal distribution of an isotropic material, where the material densities are treated as design variables. In this paper, the density method is used to formulate the topology optimization problem. This optimization problem is solved by using an optimality criteria method. Several example problems are solved to show the usefulness of the present approach.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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• pp.49-56
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• 2010

This study was conducted to find out the best optimum design method for the design of reinforced concrete agricultural aqueduct abutment and pier structures. The mixed-discrete optimization and continuous optimization method were applied to the design of reinforced concrete agricultural aqueduct abutment and pier and the results of these optimization methods were compared each other. It is proved that mixed-discrete optimization method is more reliable, efficient and reasonable than continuous optimization method for the optimum design of the reinforced concrete agricultural aqueduct abutment and pier.

• Structural Engineering and Mechanics
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• v.77 no.6
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• pp.779-795
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• 2021

In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of Crow Search Algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow's memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood; it is considered as a "local superior design" (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1143-1155
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• 2001

In order to reduce the expensive CPU time for design sensitivity analysis in dynamic response optimization, this study introduces the design sensitivities approximated within estimated confidence radius in dynamic response optimization with ALM method. The confidence radius is estimated by the linear approximation with Hessian of quasi-Newton formula and qualifies the approximate gradient to be validly used during optimization process. In this study, if the design changes between consecutive iterations are within the estimated confidence radius, then the approximate gradients are accepted. Otherwise, the exact gradients are used such as analytical or finite differenced gradients. This hybrid design sensitivity analysis method is embedded in an in-house ALM based dynamic response optimizer, which solves three typical dynamic response optimization problems and one practical design problem for a tracked vehicle suspension system. The optimization results are compared with those of the conventional method that uses only exact gradients throughout optimization process. These comparisons show that the hybrid method is more efficient than the conventional method. Especially, in the tracked vehicle suspension system design, the proposed method yields 14 percent reduction of the total CPU time and the number of analyses than the conventional method, while giving similar optimum values.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.2
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• pp.27-34
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• 1999

The growth-strain method was used for shape optimization, which carries out the optimization by distributing uniformly the distributed parameter such as von Mises stress and shear strain energy density. Shape optimization is carried out by iteration of stress analysis and growth strain analysis. In this study, the effect of growth ratio in the method was investigated and then the range of the adequate value of the growth ratio was determined. Also the growth-strain method was improved by applying the linear PID control theory in order to control volume required by a designer. Finally, an automatic shape optimization system was built up by the improved growth-strain method with a commercial software using finite element method. The effectiveness and practicability of the developed shape optimization system was verified by some examples.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.267-280
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• 2022

Multiscale structure has attracted significant interest due to its high stiffness/strength to weight ratios and multifunctional performance. However, most of the existing concurrent topology optimization works are carried out under deterministic load conditions. Hence, this paper proposes a robust concurrent topology optimization method based on the bidirectional evolutionary structural optimization (BESO) method for the design of structures composed of periodic microstructures subjected to uncertain dynamic loads. The robust objective function is defined as the weighted sum of the mean and standard deviation of the module of dynamic structural compliance with constraints are imposed to both macro- and microscale structure volume fractions. The polynomial chaos expansion (PCE) method is used to quantify and propagate load uncertainty to evaluate the objective function. The effective properties of microstructure is evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The proposed method is a non-intrusive method, and it can be conveniently extended to many topology optimization problems with other distributions. Several numerical examples are used to validate the effectiveness of the proposed robust concurrent topology optimization method.

• Bulletin of the Society of Naval Architects of Korea
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• v.25 no.3
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• pp.35-45
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• 1988

This paper presents a sophisticated Sequential Linear Approximation Method(SLAM) to solve nonlinear optimization problem and the performance of this method is compared with those of the Penalty Function Method(SUMT), Tangent Search Method(TSM) and Flexible Tolerance Method(FTM). To improve the convenience and flexibility in using the proposed SLAM, an user oriented design optimization language is developed and the application examples are shown for the optimization of propeller principal dimensions and the optimization of bulk carrier principal particulars.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.5
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• pp.637-644
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• 2010

This paper presents a reliability-based shape optimization (RBSO) using the growth-strain method. An actual design involves uncertain conditions such as material property, operational load, Poisson's ratio and dimensional variation. The purpose of the RBSO is to consider the variations of probabilistic constraint and performances caused by uncertainties. In this study, the growth-strain method was applied to shape optimization of reliability analysis. Even though many papers for reliability-based shape optimization in mathematical programming method and ESO (Evolutionary Structural Optimization) were published, the paper for the reliability-based shape optimization using the growth-strain method has not been applied yet. Growth-strain method is applied to performance measure approach (PMA), which has probabilistic constraints that are formulated in terms of the reliability index, is adopted to evaluate the probabilistic constraints in the change of average mises stress. Numerical examples are presented to compare the DO with the RBSO. The results of design example show that the RBSO model is more reliable than deterministic optimization. It was verified that the reliability-based shape optimization using growth-strain method are very effective for general structure. The purpose of this study is to improve structure's safety considering probabilistic variable.