• Journal of Aerospace System Engineering
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• v.15 no.2
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• pp.1-9
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• 2021

In this paper, a thickness compensation function is introduced to consider the shear deformation and warping effect resulting from increased thickness in the composite multi-cell wing box. The thickness compensation function is used to perform the structure optimization of the multi-cell. It is determined by minimizing the error of an analytical formula using solid mechanics and the Ritz method. It is used to define a structural performance prediction expression due to the increase in thickness. The parameter is defined by the number of spars and analyzed by the critical buckling load and the limited failure index as a response. Constraints in structural optimization are composed of displacements, torsional angles, the critical buckling load, and the failure index. The objective function is the mass, and its optimization is performed using a genetic algorithm.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.2157-2161
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• 2003

In this paper, the response surface method using three-dimensional Navier-Stokes analysis to optimize the shape of a forward-curved blades centrifugal fan, is described. For numerical analysis, Reynolds-averaged Navier-Stokes equations with standard k-e turbulence model are transformed into non-orthogonal curvilinear coordinate system, and are discretized with finite volume approximations. Due to the large number of blades in forward-curved blades centrifugal fan, the flow inside of the fan is regarded as steady flow by introducing the impeller force models for economic calculations. Linear Upwind Differencing Scheme(LUDS) is used to approximate the convection terms in the governing equations. SIMPLEC algorithm is used as a velocity-pressure correction procedure. Design variables, location of cur off, radius of cut off, expansion angle of scroll and width of impeller were selected to optimize the shapes of scroll and blades. Data points for response evaluations were selected by D-optimal design, and linear programming method was used for the optimization on the response surface. As a main result of the optimization, the efficiency was successfully improved. It was found that the optimization process provides reliable design of this kind of fans with reasonable computing time

• Wind and Structures
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• v.34 no.3
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• pp.291-301
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• 2022

Although mostly used in wind turbine market, single rotor wind turbines have problems with transportation and installation costs due to their large size. In order to solve such problems, multi-rotor wind turbine is being proposed; however, light weight design of multi-rotor wind turbine is required considering the installation at offshore or deep sea. This study proposes the systematic design process of the multi-rotor wind turbine focused on its supporting structure with simultaneous consideration of static and dynamic behaviors in an ideal situation. 2D and successive 3D topology optimization process based on the density method were applied to minimize the compliance of supporting structure. To realize the conceptual design obtained by topology optimization for manufacturing feasibility, the derived 3D structure was modified to have shell structures and optimized again through parametric design using the design of experiments and the response surface method for detail design of their thicknesses and radii. The resultant structure was determined to satisfy the stress and the buckling load constraint as well as to minimize the weight and the resultant supporting structure were verified numerically.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.661-666
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• 2000

Optimization of the elastic joint of train is performed according to the minimization of ten responses which represent driving safety and ride comfort of train and analyzed by using the each response se surface model from stochastic design of experiments. After the each response surface model is constructed, the main effect and sensitivity analyses are successfully performed by 2nd order approximated regression model as described in this paper. We can get the optimal solutions using by nonlinear programming method such as simplex or interval optimization algorithms. The response surface models and the optimization algorithms are used together to obtain the optimal design of the elastic joint of train. the ten 2nd order polynomial response surface models of the three translational stiffness of the elastic joint (design factors) are constructed by using CCD(Central Composite Design) and the multi-objective optimization is also performed by applying min-max and distance minimization techniques of relative target deviation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.177-186
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• 2000

An improved multi-level（IML) optimization algorithm using automatic differentiation (AD) for multi-objective optimum design of framed structures is proposed in this paper. In order to optimize the steel frames under seismic load, two main objective functions need to be considered for minimizing the structural weight and maximizing the strain energy. For the efficiency of the proposed algorithm, multi-level optimization techniques using decomposition method that separately utilizes both system-level and element-level optimizations and an artificial constraint deletion technique are incorporated in the algorithm. And also to save the numerical efforts, an efficient reanalysis technique through approximated structural responses such as moments, frequencies, and strain energy with respect to intermediate variables is proposed in the paper. Sensitivity analysis of dynamic structural response is executed by AD that is a powerful technique for computing complex or implicit derivatives accurately and efficiently with minimal human effort. The efficiency and robustness of the IML algorithm, compared with a plain multi-level (PML) algorithm, is successfully demonstrated in the numerical examples.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1187-1192
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• 2008

In this paper, a versatile multi-objective optimization concept for fatigue life prediction is introduced. Multi-objective decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.894-900
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• 2000

Optimization of chassis frame is performed according to the minimization of eleven responses representing one total frame weight, three natural frequencies and seven strength limits of chassis frame that are analyzed by using each response surface model from D-optimal design of experiments. After each response surface model is constructed form D-optimal design and random orthogonal array, the main effect and sensitivity analyses are successfully carried out by using this approximated regression model and the optimal solutions are obtained by using a nonlinear programming method. The response surface models and the optimization algorithms are used together to obtain the optimal design of chassis frame. The eleven-polynomial response surface models of the thirteen frame members (design factors) are constructed by using D-optimal Design and the multi-disciplinary design optimization is also performed by applying dual response analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.108-113
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• 2002

This paper presents a multi-step structural design optimization method fur machine tool structures using a genetic algorithm with dynamic penalty. The first step is a sectional topology optimization, which is to determine the best sectional construction that minimize the structural weight and the compliance responses subjected to some constraints. The second step is a static design optimization, in which the weight and the static compliance response are minimized under some dimensional and safety constraints. The third step is a dynamic design optimization, where the weight static compliance, and dynamic compliance of the structure are minimized under the same constraints. The proposed design method was examined on the 10-bar truss problem of topology and sizing optimization. And the results showed that our solution is better than or just about the same as the best one of the previous researches. Furthermore, we applied this method to the topology and sizing optimization of a crossbeam slider for a high-speed machining center. The topology optimization result gives the best desirable cross-section shape whose weight was reduced by 38.8% than the original configuration. The subsequent static and dynamic design optimization reduced the weight, static and dynamic compliances by 5.7 %, 2.1% and 19.1% respectively from the topology-optimized model. The examples demonstrated the feasibility of the suggested design optimization method.

• Transactions of Materials Processing
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• v.30 no.3
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• Structural Engineering and Mechanics
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• v.67 no.3
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• pp.301-310
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• 2018

In this paper, a multi-objective multiparameter optimization procedure is developed by combining rigorously developed metamodels with an evolutionary search algorithm-Genetic Algorithm (GA). Response surface methodology (RSM) is used for developing the metamodels to replace the tedious finite element analyses. A nine-node isoparametric plate bending element is used for conducting the finite element simulations. Highly accurate numerical data from an author compiled FORTRAN finite element program is first used by the RSM to develop second-order mathematical relations. Four material parameters-${\frac{E_1}{E_2}}$, ${\frac{G_{12}}{E_2}}$, ${\frac{G_{23}}{E_2}}$ and ${\upsilon}_{12}$ are considered as the independent variables while simultaneously maximizing fundamental frequency, ${\lambda}_1$ and frequency separation between the $1^{st}$ two natural modes, ${\lambda}_{21}$. The optimal material combination for maximizing ${\lambda}_1$ and ${\lambda}_{21}$ is predicted by using a multi-objective GA. A general sensitivity analysis is conducted to understand the effect of each parameter on the desired response parameters.