• Structural Engineering and Mechanics
• /
• v.81 no.3
• /
• pp.367-378
• /
• 2022

In this study, combined size and shape optimization of spatial truss tower structures are presented by using new optimization algorithms named Rao-1, and Rao-2. The nodal displacements, allowable stress and buckling for compressive members are taken into account as structural constraints for truss towers. The discrete and continuous design variables are used as design variables for size and shape optimization. To show the efficiency of the proposed optimization algorithm, 25-bar, and 39-bar 3D truss towers are solved for combined size and shape optimization. The 72-bar, and 160-bar 3D truss towers are solved only by size optimization. The optimal results obtained from this study are compared to those given in the literature to illustrate the efficiency and robustness of the proposed algorithm. The structural analysis and the optimization process are coded in MATLAB programming.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.16 no.1
• /
• pp.175-180
• /
• 2008

Due to the global environment problems and the consumer's need for higher vehicle performance, it becomes very important for the global car makers to reduce vehicle weight. To reduce vehicle weight, many car makers have tried to use lightweight materials, for example, aluminum, magnesium, and plastics, for the vehicle structures and components. Especially, the ASF(aluminum space frame) is known for the excellent concept of the vehicle to satisfy structural rigidity, safety performance and weight reduction. In this research, the design of experiments and the multi-disciplinary optimization technique were utilized to meet the weight and structural rigidity target of the ASF. For the structural performance of the ASF, the locations and the size of aluminum extruded frames, aluminum cast nodes, and the aluminum sheets were optimized. As a result, the optimization design procedure has been set up to meet both structural and weight target of the ASF, and the assembled ASF showed good structural performance and weight reduction.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.10a
• /
• pp.40-47
• /
• 2000

One of the drawbacks of GA-based structural optimization is that the fitness evaluation of a population of hundreds of individuals requiring hundreds of structural analyses at each CA generation is computational too expensive. Therefore, a parallel genetic algorithm is developed for structural optimization on a cluster of personal computers in this paper. Based on the parallel genetic algorithm, a population at every generation is partitioned into a number of sub-populations equal to the number of slave computers. Parallelism is exploited at sub-population level by allocationg each sub-population to a slave computer. Thus, fitness of a population at each generation can be concurrently evaluated on a cluster of personal computers. For implementation of the algorithm a virtual distributed computing system in a collection of personal computers connected via a 100 Mb/s Ethernet LAN. The algorithm is applied to the minimum weight design of a steel structure. The results show that the computational time requied for serial GA-based structural optimization process is drastically reduced.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.12 no.3
• /
• pp.82-88
• /
• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.11
• /
• pp.1225-1232
• /
• 2009

This study suggests a structural design process for the upper control arm installed at a vehicle. Static strength and durability are the most important responses in the structural design of a control arm. This study considers the static strength in the optimization process. The inertia relief method for FE analysis is utilized to simulate the static loading conditions. According to the classification of structural optimization, the structural design of a control arm is included in the category of shape optimization. In this study, the metamodel technique using the kriging method is adopted to obtain the minimum weight satisfying the strength constraint. Then, the final design is suggested by considering the durability criteria. The durability assessment is obtained by the index of fatigue durability called the SWT (Smith-Watson-Topper) index. The final optimum shape has been proposed by trial and error method.

• Structural Engineering and Mechanics
• /
• v.13 no.5
• /
• pp.479-490
• /
• 2002

The structural dynamic optimization problem based on probability is studied. Considering the randomness of structural physical parameters and the given constraint values, we develop a dynamic optimization mathematical model of engineering structures with the probability constraints of frequency, forbidden frequency domain and the vibration mode. The sensitivity of structural dynamic characteristics based on probability is derived. Two examples illustrate that the optimization model and the method applied are rational and efficient.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.25 no.9
• /
• pp.1444-1451
• /
• 2001

The evolutionary structural optimization(ESO) method has been under continuous development since 1992. The bidirectional evolutionary structural optimization(BESO) method is made of additive and removal procedure. The BESO method is very useful to search the global optimum and to reduce the computational time. This paper presents the ranked bidirectional evolutionary structural optimization(R-BESO) method which adds elements based on a rank, and the performance indicator which can estimate a fully stressed model. The R-BESO method can obtain the optimum design using less iteration number than iteration number of the BESO.

• Journal of the Korean Society for Precision Engineering
• /
• v.17 no.5
• /
• pp.93-99
• /
• 2000

In many dynamic structural optimization problems, the goal is to reduce the total weight of the structure without causing the resonance. Up to now, gradient informations(i.e., design sensitivity) have been used to achieve the goal. For some class of dynamic problems, especially coalescent eigenvalue Problems with multiobjective optimization, the design sensitivity analysis is too much complicated mathematically and numerically. Therefore, this article proposes a new technique fur structural dynamic modification using a mode modification method with Genetic Algorithm(GA). In GA formulation, fitness is defined based on penalty function approach. Design variables are iteratively improved by using genetic algorithm. Two numerical examples are shown, (ⅰ) a cantilevered plate, and (ⅱ) H-shaped structure. The results demonstrate that the proposed method is highly efficient.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.8 s.239
• /
• pp.1061-1069
• /
• 2005

In part I of this papter Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is developed to conduct optimization for nonlinear behavior structures. The method/algorithm is also verified to show its convergency and optimality. In this present paper, the NROESL algorithm is applied to several structural problems with geometric and/or material nonlinearity. Conventional optimization with sensitivity analysis using the finite difference method is also applied to the same examples. The results of the optimizations are compared. The proposed method is very efficient and derives good solutions.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2001.10a
• /
• pp.183-190
• /
• 2001

Topology optimization. has been evolved into a very efficient conceptual design tool and has been utilized into design engineering processes in many industrial parts. In recent years, topology optimization has become the focus of structural optimization design and has been researched and widely applied both in academy and industry. Traditional topology optimization has been using homogenization method and optimality criteria method. Homogenization method provides relationship equation between structure which includes many holes and stiffness matrix in FEM. Optimality criteria method is used to update design variables while maintaining that volume fraction is uniform. Traditional topology optimization has advantage of good convergence but has disadvantage of too much convergency time and additive checkerboard prevention algorithm is needed. In one way to solve this problem, element remove method is presented. Then, it is applied to many examples. From the results, it is verified that the time of convergence is very improved and optimal designed results is obtained very similar to the results of traditional topology using 8 nodes per element.