• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1726-1731
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• 2004

This paper presents the response surface optimization method using three-dimensional Navier-Stokes analysis to optimize the shape of a forward-curved blades centrifugal fan. For numerical analysis, Reynolds-averaged Navier-Stokes equations with $k-{\varepsilon}$ turbulence model are discretized with finite volume approximations. In order to reduce huge computing time due to a 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. Three geometric variables, i.e., location of cut off, radius of cut off, and width of impeller, and one operating variable, i.e., flow rate, were selected as design variables. As a main result of the optimization, the efficiency was successfully improved. And, optimum design flow rate was found by using flow rate as one of design variables. It was found that the optimization process provides reliable design of this kind of fans with reasonable computing time.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.395-405
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• 2019

In the present work, the effects of cutting parameters on surface roughness parameters (Ra), tool wear parameters (VBmax), tool vibration (Vy) and material removal rate (MRR) during hard turning of AISI 4140 steel using coated carbide tool have been evaluated. The relationships between machining parameters and output variables were modeled using response surface methodology (RSM). Analysis of variance (ANOVA) was performed to quantify the effect of cutting parameters on the studied machining parameters and to check the adequacy of the mathematical model. Additionally, Multi-objective optimization based desirability function was performed to find optimal cutting parameters to minimize surface roughness, and maximize productivity. The experiments were planned as Box Behnken Design (BBD). The results show that feed rate influenced the surface roughness; the cutting speed influenced the tool wear; the feed rate influenced the tool vibration predominantly. According to the microscopic imagery, it was observed that adhesion and abrasion as the major wear mechanism.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.39-46
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• 2017

In this paper, a model updating procedure based on the response surface method combined with the multi-objective optimization was proposed and applied for updating of the FE models representing a low-rise reinforced concrete building before and after the seismic retrofit. The dynamic properties to be matched were obtained from vibration tests using a small shaker system. By varying the structural parameters according to the central composite design, analysis results from the initial FE model using a commercial software were collected and used to produce two regression functions each of which representing the errors in the natural frequencies and mode shapes. The two functions were used as the objective functions for multi-objective optimization. Final solution was determined by examining the Pareto solutions with one iteration. The parameters representing the stiffnesses of existing concrete, masonry, connection stiffness in expansion joint, new concrete, retrofitted members with steel section jacketing were selected and identified.

• International Journal of Fluid Machinery and Systems
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• v.3 no.2
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• pp.150-159
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• 2010

Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.537-542
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• 2001

In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

• Computers and Concrete
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• v.28 no.5
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• pp.465-478
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• 2021

On the premise of ensuring that the static performance of the concrete spreader is met, the first-order natural frequency of the concrete spreader is increased, and the weight of the main beam is reduced. ANSYS is used as an analysis tool to perform modal analysis on the concrete spreader. The natural frequency, mode shape and modal test verification will be obtained to ensure the accuracy of finite element model analysis. Using the ANSYS designxplorer module, the size of the main beam is set, and the response surface model between the parameter variables and the optimization objective is established according to the experimental design points. Screening algorithm and MOGA algorithm are used to multi-optimize the stress, first-order natural frequency and girder weight, and the optimal solution is obtained by comparison. The results of modal analysis are consistent with those of the experiment, and a set of optimal solutions is obtained through the optimization algorithm. The optimal solution obtained can meet the purpose of increasing the first-order natural frequency of the concrete spreader and reducing the weight of the main beam under the premise of ensuring the overall dynamic and static performance of the concrete spreader.

• Journal of Mechanical Science and Technology
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• v.20 no.1
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• pp.133-146
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• 2006

Collaborative optimization (CO) is a multi-level decomposed methodology for a large-scale multidisciplinary design optimization (MDO). CO is known to have computational and organizational advantages. Its decomposed architecture removes a necessity of direct communication among disciplines, guaranteeing their autonomy. However, CO has several problems at convergence characteristics and computation time. In this study, such features are discussed and some suggestions are made to improve the performance of CO. Only for the system level optimization, genetic algorithm is used and gradient-based method is used for subspace optimizers. Moreover, response surface models are replaced as analyses in subspaces. In this manner, CO is applied to aero-structural design problems of the aircraft wing and its results are compared with the multidisciplinary feasible (MDF) method and the original CO. Through these results, it is verified that the suggested approach improves convergence characteristics and offers a proper solution.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.4
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• pp.184-189
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• 2004

In this paper, multi-object optimization based on a progressive quadratic response surface method (PQRSM) and a time stepping finite element method (FEM) is proposed. The new PQRSM and FEM are able to decide optimal geometric and electric variables of the switched reluctance motor (SRM) with two objective functions: torque ripple minimization and average torque maximization. The result of the optimum design for SRM demonstrates improved performance of the motor and enhanced relationship between torque ripple and average torque.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.609-610
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• 2008

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.65-67
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• 2008

An adaptive response surface method with Latin Hypercube sampling strategy is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed algorithm consists of the multi-objective Pareto optimization and ($1+{\lambda}$) evolution strategy to find the global optimal points with relatively fewer sampling data. In the adaptive RSM, an adaptive sampling point insertion method is developed utilizing the design sensitivities computed by using finite element method to set a reasonable response surface with a relatively small number of sampling points. The developed algorithm is applied to the shape optimization of PM poles for 6MW BLDC motor.