• The KSFM Journal of Fluid Machinery
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• v.12 no.3
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• pp.7-12
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• 2009

In this paper, shape optimization based on three-dimensional flow analysis has been performed for impeller design of centrifugal compressor. To evaluate the objective function of an isentropic efficiency, Reynolds-averaged Navier-Stokes equations are solved with SST (Shear Stress Transport) turbulence model. The governing equations are discretized by finite volume approximations. The optimization techniques based on the radial basis neural network method are used for the optimization. Latin hypercube sampling as design of experiments is used to generate thirty design points within design space. Sequential quadratic programming is used to search the optimal point based on the radial basis neural network model. Four geometrical variables concerning impeller shape are selected as design variables. The results show that the isentropic efficiency is enhanced effectively from the shape optimization by the radial basis neural network method.

• The KSFM Journal of Fluid Machinery
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• v.10 no.3 s.42
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• pp.39-46
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• 2007

This study presents a numerical procedure to optimize the shape of staggered dimple surface to enhance turbulent heat transfer in a rectangular channel. The RBNN method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport (SST) turbulence model. The dimple depth-to-dimple print diameter (d/D), channel height-to-dimple print diameter ratio (H/D), and dimple print diameter-to-pitch ratio (D/S) are chosen as design variables. The objective function is defined as a linear combination of heat transfer related term and friction loss related term with a weighting factor. Latin Hypercube Sampling (LHS) is used to determine the training points as a mean of the design of experiment. The optimum shape shows remarkable performance in comparison with a reference shape.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.85-91
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• 2015

The press-fit shaft is an important part used in automobiles, vessels, and trains. This study proposes an optimized design method to reduce damage that may occur in the press-fitted shaft by modifying the shape of the boss step of the press-fitted shaft. To reduce the time and cost of running the optimized design method, an approximate design optimization is applied and an optimized algorithm is generated using a genetic algorithm that is widely used in engineering fields and an approximate model using a response surface method. The planned experiments for the data that are needed to generate the approximate model use a central composite design (CCD) and Latin hypercube sampling (LHS), and the results of the approximate optimization using the above two design of experiments are to be compared.

• The KSFM Journal of Fluid Machinery
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• v.11 no.2
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• pp.46-54
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• 2008

This study presents a numerical optimization to optimize an axial flow fan blade to increase the efficiency. The radial basis neural network is used as an optimization method with the numerical analysis by Reynolds-averaged Navier-Stokes equations using SST model as turbulence closure. Four design variables related to airfoil maximum camber, maximum camber location, leading edge radius and trailing edge radius, respectively, are selected, and efficiency is considered as objective function which is to be maximized. Thirty designs are evaluated to get the objective function values of each design used to train the neural network. Optimum shape shows the efficiency increased by 1.0%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.2
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• pp.143-159
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• 2017

A probabilistic fragility assessment procedure is developed in this paper to predict risks of damage arising from seismic loading to the two-cell RC box tunnel. Especially, the paper focuses on establishing a simplified methodology to derive fragility curves which are an indispensable ingredient of seismic fragility assessment. In consideration of soil-structure interaction (SSI) effect, the ground response acceleration method for buried structure (GRAMBS) is used in the proposed approach to estimate the dynamic response behavior of the structures. In addition, the damage states of tunnels are identified by conducting the pushover analyses and Latin Hypercube sampling (LHS) technique is employed to consider the uncertainties associated with design variables. To illustrate the concepts described, a numerical analysis is conducted and fragility curves are developed for a large set of artificially generated ground motions satisfying a design spectrum. The seismic fragility curves are represented by two-parameter lognormal distribution function and its two parameters, namely the median and log-standard deviation, are estimated using the maximum likelihood estimates (MLE) method.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.899-907
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• 2022

Purpose: Although many studies on seismic fragility analysis of general bridges have been conducted using machine learning methods, studies on curved bridge structures are insignificant. Therefore, the purpose of this study is to analyze the seismic fragility of bridges with I-shaped curved girders based on the machine learning method considering the material property and geometric uncertainties. Method: Material properties and pier height were considered as uncertainty parameters. Parameters were sampled using the Latin hypercube technique and time history analysis was performed considering the seismic uncertainty. Machine learning data was created by applying artificial neural network and response surface analysis method to the original data. Finally, earthquake fragility analysis was performed using original data and learning data. Result: Parameters were sampled using the Latin hypercube technique, and a total of 160 time history analyzes were performed considering the uncertainty of the earthquake. The analysis result and the predicted value obtained through machine learning were compared, and the coefficient of determination was compared to compare the similarity between the two values. The coefficient of determination of the response surface method was 0.737, which was relatively similar to the observed value. The seismic fragility curve also showed that the predicted value through the response surface method was similar to the observed value. Conclusion: In this study, when the observed value through the finite element analysis and the predicted value through the machine learning method were compared, it was found that the response surface method predicted a result similar to the observed value. However, both machine learning methods were found to underestimate the observed values.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.275-276
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• 2009

This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

• The KSFM Journal of Fluid Machinery
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• v.13 no.6
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• pp.42-50
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• 2010

The present analysis deals with a numerical procedure for optimizing the shape of elliptical dimples in a cooling channel. The three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis is employed in conjunction with the SST model for predictions of the turbulent flow and the heat transfer. Three non-dimensional geometric design variables, such as the ellipse dimple diameter ratio, ratio of the dimple depth to the average diameter, and ratio of the distance between dimples to the pitch are considered in the optimization. Twenty-one experimental points within design space are selected by Latin Hypercube Sampling. Each objective function values at these points are evaluated by RANS analysis and producing optimal point using surrogate model. The linear combination of heat transfer coefficient and friction loss related terms with a weighting factor is defined as the objective function. The results show that the optimized elliptical dimple shape improves considerably the heat transfer performance than the circular dimple shape.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.9
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• pp.103-110
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• 2010

This paper presents the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method and approximation modeling method with Radial Basis Function (RBF) neural network structure. The complex tool holder is used for holding a (milling/drilling) tool of a machine tool. The engineering problem of complex tool holder results from the twisting of spindle of tool holder. For this purpose, we present the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method (specifically a module of $iSight^{(R)}$ FD-3.1) and approximation modeling method with Radial Basis Function (RBF) (another module of $iSight^{(R)}$ FD-3.1) neural network structure