• 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.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1756-1769
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• 1999

Parallel paths in an interconnection network have some significance in that message transmission time can be reduced because message is divided into packets and transmitted in parallel through several paths, and also an whose nodes has 2n binary bit string, is an interconnection network which has a lower network cost than hypercube and its variation. In this paper, we analyze node disjoint parallel path in Folded Hyper-Star graph FHS(2n,n) proposed as the topology of parallel computers and, using the result, prove that the fault diameter of a Folded Hyper-Star graph FHS(2n,n) is 2n-1.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.12
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• pp.954-962
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• 2008

This study presents a numerical procedure to optimize the shape of cylindrical cooling hole to enhance film-cooling effectiveness. 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 turbulent model. The hole length-to-diameter ratio and injection angle are chosen as design variables and film-cooling effectiveness is considered as objective function which is to be maximized. Twelve training points are obtained by Latin Hypercube Sampling for two design variables. In the sensitivity analysis, it is found that the objective function is more sensitive to the injection angle of hole than the hole length-to diameter ratio. Optimum shape gives considerable increase in film-cooling effectiveness.

• International Journal of Fluid Machinery and Systems
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• v.3 no.1
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• pp.29-38
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• 2010

This paper presents a procedure for the design optimization of a centrifugal compressor. The centrifugal compressor consists of a centrifugal impeller, vaneless diffuser and volute. And, optimization techniques based on the radial basis neural network method are used to optimize the impeller of a centrifugal compressor. The Latin-hypercube sampling of design-of-experiments is used to generate the thirty design points within design spaces. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by using finite volume approximations and solved on hexahedral grids to evaluate the objective function of the total-to-total pressure ratio. Four variables defining the impeller hub and shroud contours are selected as design variables in this optimization. The results of optimization show that the total-to-total pressure ratio of the optimized shape at the design flow coefficient is enhanced by 2.46% and the total-to-total pressure ratios at the off-design points are also improved significantly by the design optimization.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2999-3029
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• 2024

To improve the predictive capability of a nuclear thermal hydraulic safety analysis code by developing a better constitutive equation for individual phenomenon has been the general research direction until now. This paper proposes a new method to directly use complex experimental data obtained from integral effect test (IET) to improve constitutive models holistically and simultaneously. The method relies on the sensitivity of a simulation result of IET data to the multiple constitutive equations utilized during the simulation, and the sensitivity of individual model determines the direction of modification for the constitutive model. To develop a robust and generalized method, a clustering algorithm using an artificial neural network, sample space size determination using non-parametric statistics, and sampling method of Latin hypercube sampling are used in a combined manner. The value of the proposed methodology is demonstrated by applying the method to the ATLAS DSP-05 IET experiment. A sensitivity of each observation parameter to the constitutive models is analyzed. The new methodology suggested in the study can be used to improve the code prediction results of complex IET data by identifying the direction for constitutive equations to be modified.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1173-1183
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• 2006

Matrix-star, Pancake, and RFM graphs have such a good property of Star graph and a lower network cost than Hypercube. Matrix-star graph has Star graph as a basic module and the node symmetry, the maximum fault tolerance, and the hierarchical decomposition property. Also it is an interconnection network that improves the network cost against Star graph. In this paper, we propose a method to embed among Matrix-star Pancake, and RFM graphs using the edge definition of graphs. We prove that Matrix-star $MS_{2,n}$ can be embedded into Pancake $P_{2n}$ with dilation 4, expansion 1, and $RFM_{n}$ graphs can be embedded into Pancake $P_{n}$ with dilation 2. Also, we show that Matrix-star $MS_{2,n}$ can be embedded into the $RFM_{2n}$ with average dilation 3.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.1-9
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• 2005

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.49-54
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• 2007

Structural integrity assessment technique for pavement system is studied considering the uncertainties among the material properties. The artificial neural networks technique is applied for the inverse analysis to estimate the elastic modulus based on the measured deflections from the FWD test. A computer code based on the spectral element method was developed for the accurate and fast analysis of the multi-layered soil structures, and the developed program was used for generating the training and testing patterns for the neural network. Neural networks was applied to estimate the elastic modulus of pavement system using the maximum deflections with and without the uncertainties in the material properties. It was found that the estimation results by the conventiona1 neural networks were very poor when there exist the uncertainties and the estimation results could be significantly improved by adopting the proposed method for generating training patterns considering the uncertainties among material properties.