• 한국표면공학회지
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• 제50권1호
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• pp.1-9
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• 2017

Design of novel materials in renewable energy systems plays a key role in powering transportation vehicles and portable electronics. This review introduces the research work of first principles-based computational design for the materials over the last decade to accomplish the goal with less financial and temporal cost beyond the conventional approach, especially, focusing on electrocatalyst toward a proton exchange membrane fuel cell (PEMFC). It is proposed that the new method combined with experimental validation, can provide fundamental descriptors and mechanical understanding for optimal efficiency control of a whole system. Advancing these methods can even realize a computational platform of the materials genome, which can substantially reduce the time period from discovery to commercialization into markets of new materials.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2004년도 춘계 학술대회논문집
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• pp.99-105
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• 2004

A kinetic theory analysis is made of low-speed gas flows in microchannels. The Boltzmann equation simplified by a collision model is solved by means of a finite difference approximation with the discrete ordinate method. The method does not suffer from statistical noise which is common in particle based methods and requires much less amount of computational effort. Calculations are made for flows in simple microchannels and a microfluidic system consisting of two microchannels in series. The method is assessed by comparing the results with those from several different methods and available experimental data.

• Geomechanics and Engineering
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• 제12권1호
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• pp.161-183
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• 2017

This paper investigates the damage identification of the concrete pile element through axial wave propagation technique using computational and experimental studies. Now-a-days, concrete pile foundations are often common in all engineering structures and their safety is significant for preventing the failure. Damage detection and estimation in a sub-structure is challenging as the visual picture of the sub-structure and its condition is not well known and the state of the structure or foundation can be inferred only through its static and dynamic response. The concept of wave propagation involves dynamic impedance and whenever a wave encounters a changing impedance (due to loss of stiffness), a reflecting wave is generated with the total strain energy forked as reflected as well as refracted portions. Among many frequency domain methods, the Spectral Finite Element method (SFEM) has been found suitable for analysis of wave propagation in real engineering structures as the formulation is based on dynamic equilibrium under harmonic steady state excitation. The feasibility of the axial wave propagation technique is studied through numerical simulations using Elementary rod theory and higher order Love rod theory under SFEM and ABAQUS dynamic explicit analysis with experimental validation exercise. Towards simulating the damage scenario in a pile element, dis-continuity (impedance mismatch) is induced by varying its cross-sectional area along its length. Both experimental and computational investigations are performed under pulse-echo and pitch-catch configuration methods. Analytical and experimental results are in good agreement.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• 한국전산구조공학회논문집
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• 제23권6호
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• pp.675-682
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• 2010

The flood impact pressure acting on a vertical wall resulting from a dam-breaking problem is simulated using a navier-Stokes(N-S) solver. The N-S solver uses Eulerian Finite Volume Method(FVM) along with Volume Of Fluid(VOF) method for 2-D incompressible free surface flows. A Split Lagrangian Advection(SLA) scheme for VOF method is implemented in this paper. The SLA scheme is developed based on an algorithm of Piecewise Linear Interface Calculation(PLIC). The coupling between the continuity and momentum equations is affected by using a well-known Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm. Several two-dimensional numerical simulations of the dam-breaking problem are presented to validate the accuracy and demonstrate the capability of the present algorithm. The significance of the time step and grid resolution are also discussed. The computational results are compared with experimental data and with computations by other numerical methods. The results showed a favorable agreement of water impact pressure as well as the global fluid motion.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2004년도 춘계 학술대회논문집
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• pp.106-112
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• 2004

A kinetic theory analysis is made of low-speed gas flows around a micro-plate. The Boltzmann equation simplified by a collision model is solved by means of a finite difference approximation with the discrete ordinate method. The method does not suffer from statistical noise which is common in particle based methods and requires much less amount of computational effort. Calculations are made for flows around a micro-scale flat plate with a finite length of 20 microns. The method is assessed by comparing the results with those from several different methods and available experimental data.

• Nuclear Engineering and Technology
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• 제52권6호
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• pp.1148-1155
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• 2020

This work presents two different methods for quantifying and propagating the uncertainty associated with fuel composition at end of life for cask criticality calculations. The first approach, the computational approach uses parametric uncertainty including those associated with nuclear data, fuel geometry, material composition, and plant operation to perform forward depletion on Monte-Carlo sampled inputs. These uncertainties are based on experimental and prior experience in criticality safety. The second approach, the data-driven approach relies on using radiochemcial assay data to derive code bias information. The code bias data is used to perturb the isotopic inventory in the data-driven approach. For both approaches, the uncertainty in k_eff for the cask is propagated by performing forward criticality calculations on sampled inputs using the distributions obtained from each approach. It is found that the data driven approach yielded a higher uncertainty than the computational approach by about 500 pcm. An exploration is also done to see if considering correlation between isotopes at end of life affects k_eff uncertainty, and the results demonstrate an effect of about 100 pcm.

• Journal of Computational Design and Engineering
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• 제3권4호
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• pp.295-311
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• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.457-464
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• 2005

The coupled time-integration method with a staggered algorithm based on computational structural dynamics (CSD), finite element method (FEM) and computational fluid dynamics (CFD) has been developed in order to demonstrate physical vibration phenomena due to dynamic aeroelastic excitations. Virtual flutter tests for the spanwise curved wing model have been effectively conducted using the present advanced computational methods with high speed parallel processing technique. In addition, the present system can simultaneously give a recorded data fie to generate virtual animation for the flutter safety test. The results for virtual flutter test are compared with the experimental data of wind tunnel test. It is shown from the results that the effect of spanwise curvature have a tendency to decrease the flutter dynamic pressure for the same flight condition.

• Steel and Composite Structures
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• 제47권6호
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.