• Nuclear Engineering and Technology
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• v.19 no.2
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• pp.85-98
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• 1987

To analyze the aerosol dynamics in severe accidents of LMFBR, a new computer code entitled MCAD (Multicomponent Aerosol Dynamics) has been developed. The code can treat two component aerosol system using relative collision probability of each particles as sequences of accident scenarios. Coagulation and removal mechanisms incorporating Brownian diffusion and gravitational sedimentation are included in this model. In order to see the effect of particle geometry, the code makes use of the concept of density correction factor and shape factors. The code is verified using the experimental result of NSPP-300 series and compared to other code. At present, it fits the result of experiment well and agrees to the existing code. The input variables included are very uncertain. Hence, it requires uncertainty and sensitivity analysis as a supplement to code development. In this analysis, 14 variables are selected to analyze. The input variables are compounded by experimental design method and Latin hypercube sampling. Their results are applied to Response surface method to see the degree of regression. The stepwise regression method gives an insight to which variables are significant as time elapse and their reasonable ranges. Using Monte Carlo Method to the regression model of LHS, the confidence level of the results of MCAD and their variables is improved.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.747-754
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• 2021

Purpose: Seismic safety evaluation of a curved bridge must be performed since the curved bridges exhibit the complex behavior rather than the straight bridges, due to geometrical characteristics. In order to conduct the probabilistic seismic assessment of the curved bridge, Seismic fragility evaluation was performed using the uncertainty of the steel material properties of a curved bridge girde, in this study. Method: The finite element (FE) model using ABAQUS platform of the curved bridge girder was constructed, and the statistical parameters of steel materials presented in previous studies were used. 100 steel material models were sampled using the Latin Hypercube Sampling method. As an input ground motion in this study, seismic fragility evaluation was performed by the normalized scale of the Gyeongju earthquake to 0.2g, 0.5g, 0.8g, 1.2g, and 1.5g. Result: As a result of the seismic fragility evaluation of the curved girder, it was found that there was no failure up to 0.03g corresponding to the limit state of allowable stress design, but the failure was started from 0.11g associated with using limit state design. Conclusion: In this study, seismic fragility evaluation was performed considering steel materials uncertainties. Further it must be considered the seismic fragility of the curved bridge using both the uncertainties of input motions and material properties.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.98-106
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• 2021

The paper deals with comparative study on sensitivity analysis using various methods regarding to design of experiments for structure design of an active type DSF (Deck support frame) that was developed for float-over installation of offshore plant. The thickness sizing variables of structure member of the active type DSF were considered the design factors. The output responses were defined from the weight and the strength performances. Various methods such as orthogonal array design, Box-Behnken design, and Latin hypercube design were applied to the comparative study. In order to evaluate the approximation performance of the design space exploration according to the design of experiments, response surface method was generated for each design of experiment, and the accuracy characteristics of the approximation were reviewed. The design enhancement results such as numerical costs, weight minimization, etc. via the design of experiment methods were compared to the results of the best design. The orthogonal array design method represented the most improved results for the structure design of the active type DSF.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.161-171
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• 2021

This paper presents the findings of a comparative study on minimum weight design and sensitivity evaluation using different experimental design methods for the structural design of an active-type deck support frame (DSF) developed for the float-over installation of an of shore plant topside. The thickness sizing variables of the structural members of a passive-type DSF were considered the design factors, and the output responses were defined using the weight and strength performances. The design of the experimental methods applied in the comparative study of the minimum weight design and the sensitivity evaluation were the orthogonal array design, Box- Behnken design, and Latin hypercube design. A response surface method was generated for each design of the experiment to evaluate the approximation performance of the design space exploration according to the experimental design, and the accuracy characteristics of the approximation were reviewed. Regarding the minimum weight design, the design results, such as numerical costs and weight minimization, of the experimental design for the best design case, were evaluated. The Box- Behnken design method showed the optimum design results for the structural design of the passive-type DSF.

• 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.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• Geomechanics and Engineering
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• v.23 no.3
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• pp.261-273
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• 2020

This paper presents an efficient method utilizing user-defined computer functional codes to determine the reliability of an embankment slope with spatially varying soil properties in real time. The soils' mechanical properties varied with the soil layers that had different degrees of compaction and moisture content levels. The Latin Hypercube Sampling (LHS) for the degree of compaction and Kriging simulation of moisture content variation were adopted and programmed to predict their spatial distributions, respectively, that were subsequently used to characterize the spatial distribution of the soil shear strengths. The shear strength parameters were then integrated into the Geostudio command file to determine the safety factor of the embankment slope. An explicit metamodal for the performance function, using the Kriging method, was established and coded to efficiently compute the failure probability of slope with varying moisture contents. Sensitivity analysis showed that the proposed method significantly reduced the computational time compared to Monte Carlo simulation. About 300 times LHS Geostudio computations were needed to optimize precision and efficiency in determining the failure probability. The results also revealed that an embankment slope is prone to high failure risk if the degree of compaction is low and the moisture content is high.

• 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.

• Journal of Radiation Protection and Research
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• v.16 no.1
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• pp.53-72
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• 1991

An integrated model is developed in this paper for the performance assessment of high level radioactive waste repository. This integrated model consists of two simple mathematical models. One is a multiple-barrier failure model of the repository system based on constant failure rates which provides source terms to biosphere. The other is a biosphere model which has multiple pathways for radionuclides to reach to human. For the parametric uncertainty and sensitivity analysis for the risk assessment of high level radioactive waste repository, Latin hypercube sampling and rank correlation techniques are applied to this model. The former is cost-effective for large computer programs because it gives smaller error in estimating output distribution even with smaller number of runs compared to crude Monte Carlo technique. The latter is good for generating dependence structure among samples of input parameters. It is also used to find out the most sensitive, or important, parameter groups among given input parameters. The methodology of the mathematical modelling with statistical analysis will provide useful insights to the decision-making of radioactive waste repository selection and future researches related to uncertain and sensitive input parameters.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.8
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• pp.663-671
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• 2007

In this work, shape optimization of a wire-wrapped fuel assembly in a liquid metal reactor has been carried out by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. Sequential Quadratic Programming is used to search the optimal point from the constructed surrogate. Two geometric design variables are selected for the optimization and design space is sampled using Latin Hypercube Sampling. The optimization problem has been defined as a maximization of the objective function, which is as a linear combination of heat transfer and friction loss related terms with a weighing factor. The objective function value is more sensitive to the ratio of the wire spacer diameter to the fuel rod diameter than to the ratio of the wire wrap pitch to the fuel rod diameter. The optimal values of the design variables are obtained by varying the weighting factor.