• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1760-1767
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• 2004

Sequential sampling approaches of a metamodel that sampling points are updated sequentially become a significant consideration in metamodeling technique. Sequential sampling design is more effective than classical space filling design of all-at-once sampling because sequential sampling design is to add new sampling points by means of distance between sampling points or precdiction error obtained from metamodel. However, though the extremum points can strongly reflect the behaviors of responses, the existing sequential sampling designs are inefficient to approximate extremum points of original model. In this research, new sequential sampling approach using the sensitivity of Kriging model is proposed, so that new approach reflects the behaviors of response sequentially. Various sequential sampling designs are reviewed and the performances of the proposed approach are compared with those of existing sequential sampling approaches by using mean squared error. The accuracy of the proposed approach is investigated against optimization results of test problems so that superiority of the sensitivity approach is verified.

• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.1-8
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• 2012

Multidisciplinary design optimization (MDO) for a suspension component of the vehicle front suspension was performed in this research. Shapes and thicknesses of the subframe were optimized to satisfy multi-disciplinary design requirements; weight, fatigue, crash, noise, vibration, and harshness (NVH), and kinematic and compliance (K&C). Analyses procedures of the performance disciplines were integrated and automated by using the process integration and design optimization (PIDO) technique, and the integrated and automated analyses environments enabled various types of analytic design methodologies for solving the MDO problem. We applied an approximate optimization technique which involves sequential sampling and metamodeling. Since the design variables for thicknesses should be dealt as discrete variables. the evolutionary algorithm is selected as optimization technique. The MDO problem was formulated three types of problems according to the order of priorities among the performance disciplines, and the results of MDO provided design alternatives for various design situations.

• Smart Structures and Systems
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• v.14 no.5
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• pp.811-830
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• 2014

A numerical study has been carried out to simulate an innovative monitoring procedure to detect and localize damage in reinforced concrete beams retrofitted with carbon fiber reinforced polymer (CFRP) unidirectional laminates. The main novelty of the present simulation is its ability to conduct the electromechanical admittance monitoring technique by considerably compressing the amount of data required for damage detection and localization. A FEM simulation of electromechanical admittance-based sensing technique was employed by applying lead zirconate titanate (PZT) transducers to acquire impedance spectrum signatures. Response surface methodology (RSM) is finally adopted as a tool for solving inverse problems to estimate the location and size of damaged areas from the relationship between damage and electromechanical admittance changes computed at PZT transducer surfaces. This statistical metamodel technique allows polynomial models to be produced without requiring complicated modeling or numerous data sets after the generation of damage, leading to considerably lower cost of creating diagnostic database. Finally, a numerical example is carried out regarding a steel-reinforced concrete (RC) beam model monotonically loaded up to its failure which is also retrofitted by a CFRP laminate to verify the validity of the present metamodeling monitoring technique. The load-carrying capacity of concrete is predicted in the present paper by utilizing an Ottosen-type failure surface in order to better take into account the passive confinement behavior of retrofitted concrete material under the application of FRP laminate.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.3
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• pp.432-439
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• 2003

Inconel 690 for nuclear steam generator tube has more Chromium than the conventionally used Inconel 600 in order to increase the corrosion resistance. To evaluate the tribological characteristics of Inconel 690 under fretting condition the fretting tests were carried out in air and elevated temperature water. Fretting tests of the cross-cylinder type were done under various vibrating amplitudes and applied normal loads in order to measure the friction forces and wear volumes. From the results of fretting wear tests. the wear of Inconel 690 can be predictable using the work rate model. The amounts of friction forces were proportional to relative movement between two fretting surfaces. The friction coefficients were decreased as increasing the normal loads and deceasing the vibrating amplitudes. Depending on fretting environment, distinctively different wear mechanisms and often drastically different wear rates can occur It was found that the fretting wearfactors in air and water at 2$0^{\circ}C$, 5$0^{\circ}C$, and 8$0^{\circ}C$ were 7.38 $\times$ $10^{-13}$$Pa^{-1}$, 2.12 $\times$$10^{-13}$$Pa^{-1}$, 3.34$\times$$10^{-13}$$Pa^{-1}$and 5.21$\times$$10^{-13}$$Pa^{-1}$, respectively flexibility to model response data with multiple local extreme. In this study, metamodeling techniques are adopted to carry out the shape optimization of a funnel of Cathode Ray Tube, which finds the shape minimizing the local maximum principal stress. Optimum designs using two metamodels are compared and proper metamodel is recommended based on this research.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.7
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• pp.703-714
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• 2010

This paper describes the design optimization of a rotating rectangular channel with staggered arrays of pin-fins by Kriging metamodeling technique. Two non-dimensional variables, the ratio of the height to the diameter of the pin-fins and the ratio of the spacing between the pin-fins to the diameter of the pin-fins are chosen as the design variables. The objective function that is a linear combination of heat transfer and friction loss related terms with a weighting factor is selected for the optimization. To construct the Kriging model, objective function values at 20 training points generated by Latin hypercube sampling are evaluated by a three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis method with the SST turbulence model. The Kriging model predicts the objective function value that agrees well with the value calculated by the RANS analysis at the optimum point. The objective function is reduced by 11% by the optimization of the channel.

• Journal of the Korean Magnetics Society
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• v.23 no.3
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• pp.110-116
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• 2013

This paper proposes a sampling-based sensitivity method for dealing with electromagnetic coupled design problems effectively. The black-box modeling technique is basically applied to obtain an optimum regardless of how strong the electromagnetic, thermal and structural analyses are coupled with each other. To achieve this, Kriging surrogate models are produced in a hyper-cubic local window with the center of a current design point. Then design sensitivity values are extracted from the differentiation of basis functions which consist of the models. The proposed method falls under a hybrid optimization method which takes advantages of the sampling-based and the sensitivity-based methods. Owing to the aforementioned feature, the method can be applied even to electromagnetic problems of which the material properties are strongly coupled with thermal or structural outputs. To examine the accuracy and validity of the proposed method, a strongly nonlinear mathematical example and a coil design problem for local induction heating are tested.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1217-1224
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• 2005

To improve the accuracy of a metamodel, additional sample points can be selected by using a specified criterion, which is often called sequential sampling approach. Sequential sampling approach requires small computational cost compared to one-stage optimal sampling. It is also capable of monitoring the process of metamodeling by means of identifying an important design region for approximation and further refining the fidelity in the region. However, the existing critertia such as mean squared error, entropy and maximin distance essentially depend on the distance between previous selected sample points. Therefore, although sufficient sample points are selected, these sequential sampling strategies cannot guarantee the accuracy of metamodel in the nearby optimum points. This is because criteria of the existing sequential sampling approaches are inefficient to approximate extremum and inflection points of original model. In this research, new sequential sampling approach using the sensitivity of metamodel is proposed to reflect the response. Various functions that can represent a variety of features of engineering problems are used to validate the sensitivity approach. In addition to both root mean squared error and maximum error, the error of metamodel at optimum points is tested to access the superiority of the proposed approach. That is, optimum solutions to minimization of metamodel obtained from the proposed approach are compared with those of true functions. For comparison, both mean squared error approach and maximin distance approach are also examined.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.873-879
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• 2012

Metamodels have been developed with a variety of design optimization techniques in the field of structural engineering over the last decade because they are efficient, show excellent prediction performance, and provide easy interconnections into design frameworks. To construct a metamodel, a sequential procedure involving steps such as the design of experiments, metamodeling techniques, and validation techniques is performed. Because validation techniques can measure the accuracy of the metamodel, the number of presampled points for an accurate kriging metamodel is decided by the validation technique in the sequential kriging metamodel. Because the interpolation model such as the kriging metamodel based on computer experiments passes through responses at presampled points, additional analyses or reconstructions of the metamodels are required to measure the accuracy of the metamodel if existing validation techniques are applied. In this study, we suggest a sensitivity validation that does not require additional analyses or reconstructions of the metamodels. Fourteen two-dimensional mathematical problems and an engineering problem are illustrated to show the feasibility of the suggested method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.223-233
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• 2011

A design optimization problem was formulated to minimize the volume of an RC building structure while satisfying design constraints on structural displacements under vertical, wind and seismic loads. We employed metamodel-based design optimization using design of experiments, metamodeling and optimization algorithm to circumvent the difficulty of the automation of structural analysis procedure. Especially, we proposed a design approach of repetitive design optimizations by stages with changing the side constraint values on design variables and limit values on design constraints until a satisfactory design result was obtained. Using the proposed design approach, the volume of the RC building structure has been reduced by 53.3 % compared to the initial one while satisfying all the design constraints. This design result clearly shows the validity of the proposed design approach.