• The Transactions of the Korean Institute of Electrical Engineers B
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• 제54권4호
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• pp.163-173
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• 2005

The conventional optimization study for electromagnetic systems has been mostly on the shape or size optimization. The goal for these optimization methods is to improve performance of electromagnetic systems by optimizing the interface shape of two different materials while their given layout or initial topology are held. The feasible topology can be diverse and an appropriate topology will give much better design results. In this paper we propose a theory and an algorithm for topology optimization of electromagnetic systems, which are based on the finite element method. The topology optimization technique employes a direct searching method of sensitivity analysis in which the information of material sensitivity is used. Two numerical examples of a switched reluctance motor and an electrostatic actuator of MEMS are tested and their design results show that the optimization method is valid and useful for the topology and basic layout design of electromagnetic systems.

• Journal of Magnetics
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• 제18권3호
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• pp.283-288
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• 2013

This paper presents structural topology optimization that is being applied for the design of electromagnetic vibration energy harvester. The design goal is to maximize the root-mean-square value of output voltage generated by external vibration leading structures. To calculate the output voltage, the magnetic field analysis is performed by using the finite element method, and the obtained magnetic flux linkage is interpolated by using Lagrange polynomials. To achieve the design goal, permanent magnet is designed by using topology optimization. The analytical design sensitivity is derived from the adjoint variable method, and the formulated optimization problem is solved through the method of moving asymptotes (MMA). As optimization results, the optimal location and shape of the permanent magnet are provided when the magnetization direction is fixed. In addition, the optimization results including the design of magnetization direction are provided.

• Steel and Composite Structures
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• 제47권5호
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• pp.569-585
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• 2023

This paper proposes an efficient approach for the structural topology optimization of bi-directional functionally graded structures by incorporating popular radial basis functions (RBFs) into an implicit level set (ILS) method. Compared to traditional element density-based methods, a level set (LS) description of material boundaries produces a smoother boundary description of the design. The paper develops RBF implicit modeling with multiquadric (MQ) splines, thin-plate spline (TPS), exponential spline (ES), and Gaussians (GS) to define the ILS function with high accuracy and smoothness. The optimization problem is formulated by considering RBF-based nodal densities as design variables and minimizing the compliance objective function. A LS-RBF optimization method is proposed to transform a Hamilton-Jacobi partial differential equation (PDE) into a system of coupled non-linear ordinary differential equations (ODEs) over the entire design domain using a collocation formulation of the method of lines design variables. The paper presents detailed mathematical expressions for BiDFG beams topology optimization with two different material models: continuum functionally graded (CFG) and mechanical functionally graded (MFG). Several numerical examples are presented to verify the method's efficiency, reliability, and success in accuracy, convergence speed, and insensitivity to initial designs in the topology optimization of two-dimensional (2D) structures. Overall, the paper presents a novel and efficient approach to topology optimization that can handle bi-directional functionally graded structures with complex geometries.

• Journal of Mechanical Science and Technology
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• 제15권7호
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• pp.876-888
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• 2001

For a large scaled optimization based on response surface methods, an efficient quadratic approximation method is presented in the context of the trust region model management strategy. If the number of design variables is η, the proposed method requires only 2η+1 design points for one approximation, which are a center point and tow additional axial points within a systematically adjusted trust region. These design points are used to uniquely determine the main effect terms such as the linear and quadratic regression coefficients. A quasi-Newton formula then uses these linear and quadratic coefficients to progressively update the two-factor interaction effect terms as the sequential approximate optimization progresses. In order to show the numerical performance of the proposed method, a typical unconstrained optimization problem and two dynamic response optimization problems with multiple objective are solved. Finally, their optimization results compared with those of the central composite designs (CCD) or the over-determined D-optimality criterion show that the proposed method gives more efficient results than others.

• Journal of Mechanical Science and Technology
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• 제20권3호
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• pp.366-375
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• 2006

In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps: the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.

• Smart Structures and Systems
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• 제33권3호
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• pp.177-188
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• 2024

The fatigue-induced sequential failure of a structure having structural redundancy requires system-level analysis to account for stress redistribution. System reliability-based design optimization (SRBDO) for preventing fatigue-initiated structural failure is numerically costly owing to the inclusion of probabilistic constraints. This study incorporates the Branch-and-Bound method employing system reliability Bounds (termed the B³ method), a failure-path structural system reliability analysis approach, with a metaheuristic optimization algorithm, namely grey wolf optimization (GWO), to obtain the optimal design of structures under fatigue-induced system failure. To further improve the efficiency of this new optimization framework, an additional bounding rule is proposed in the context of SRBDO against fatigue using the B³ method. To demonstrate the proposed method, it is applied to complex problems, a multilayer Daniels system and a three-dimensional tripod jacket structure. The system failure probability of the optimal design is confirmed to be below the target threshold and verified using Monte Carlo simulation. At earlier stages of the optimization, a smaller number of limit-state function evaluation is required, which increases the efficiency. In addition, the proposed method can allocate limited materials throughout the structure optimally so that the optimally-designed structure has a relatively large number of failure paths with similar failure probability.

• Transactions of the Korean Society of Mechanical Engineers A
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• 제24권1호
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• pp.190-195
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• 2000

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure. Nikolakopoulos et. al. used the intersection point of the superposed contours that correspond to the eigenfrequency caused by the crack presence. However the intersecting point of the superposed contours is not only difficult to find but also incorrect to calculate. A method is presented in this paper which uses optimization technique for the location and depth of the crack. The basic idea is to find parameters which use the structural eigenfrequencies on crack depth and location and optimization algorithm. With finite element model of the structure to calculate eigenfrequencies, it is possible to formulate the inverse problem in optimization format. Method of optimization is augmented lagrange multiplier method and search direction method is BFGS variable metric method and one dimensional search method is polynomial interpolation.

• Journal of the Society of Naval Architects of Korea
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• 제47권1호
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• pp.58-66
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• 2010

Topology design optimization is a method to determine the optimal distribution of material that yields the minimal compliance of structures, satisfying the constraint of allowable material volume. The method is easy to implement and widely used so that it becomes a powerful design tool in various disciplines. In this paper, a large-scale topology design optimization method is developed using the efficient adjoint sensitivity and optimality criteria methods. Parallel computing technique is required for the efficient topology optimization as well as the precise analysis of large-scale problems. Parallelized finite element analysis consists of the domain decomposition and the boundary communication. The preconditioned conjugate gradient method is employed for the analysis of decomposed sub-domains. The developed parallel computing method in topology optimization is utilized to determine the optimal structural layout of human powered vessel.

• International Journal of Aeronautical and Space Sciences
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• 제14권4호
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• pp.324-333
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• 2013

The purpose of this paper is to improve the efficiency of multi-objective topology optimization using a genetic algorithm (GA) with bar-system representation. We proposed a new GA using an elite initial population obtained from a Solid Isotropic Material with Penalization (SIMP) using a weighted sum method. SIMP with a weighted sum method is one of the most established methods using sensitivity analysis. Although the implementation of the SIMP method is straightforward and computationally effective, it may be difficult to find a complete Pareto-optimal set in a multi-objective optimization problem. In this study, to build a more convergent and diverse global Pareto-optimal set and reduce the GA computational cost, some individuals, with similar topology to the local optimum solution obtained from the SIMP using the weighted sum method, were introduced for the initial population of the GA. The proposed method was applied to a structural topology optimization example and the results of the proposed method were compared with those of the traditional method using standard random initialization for the initial population of the GA.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.216-221
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• 2008

Shape design optimization for linear elasticity problem is performed using isogeometric analysis method. In many design optimization problems for real engineering models, initial raw data usually comes from CAD modeler. Then designer should convert this CAD data into finite element mesh data because conventional design optimization tools are generally based on finite element analysis. During this conversion there is some numerical error due to a geometry approximation, which causes accuracy problems in not only response analysis but also design sensitivity analysis. As a remedy of this phenomenon, the isogeometric analysis method is one of the promising approaches of shape design optimization. The main idea of isogeometric analysis is that the basis functions used in analysis is exactly same as ones which represent the geometry, and this geometrically exact model can be used shape sensitivity analysis and design optimization as well. In shape design sensitivity point of view, precise shape sensitivity is very essential for gradient-based optimization. In conventional finite element based optimization, higher order information such as normal vector and curvature term is inaccurate or even missing due to the use of linear interpolation functions. On the other hands, B-spline basis functions have sufficient continuity and their derivatives are smooth enough. Therefore normal vector and curvature terms can be exactly evaluated, which eventually yields precise optimal shapes. In this article, isogeometric analysis method is utilized for the shape design optimization. By virtue of B-spline basis function, an exact geometry can be handled without finite element meshes. Moreover, initial CAD data are used throughout the optimization process, including response analysis, shape sensitivity analysis, design parameterization and shape optimization, without subsequent communication with CAD description.