• Smart Structures and Systems
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• v.11 no.1
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• pp.103-122
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• 2013

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.210-212
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• 2009

In object tracking based on the particle filter algorithm controlling the proper distribution of the samples is essential to accurately track the target. If the samples are spread too wide compared to the target size, the tracking accuracy may degrade as some samples can be caught by background clutters that is similar to the target. On the other hands if the samples are spread too narrow, the particle filter may fail to track the abrupt motion of the target. To solve this problem we propose an improved particle filter that adopts "re-weighting" technique at the observe step. We estimate the distribution of the weights of the current samples by its mean and variance. Then the samples are re-weighted so that the appropriate distribution of the samples in proportional to the target scale is obtained at the next select step. The proposed tracking method can avoid convergence to local mean and improve the accuracy of the estimated target state.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3803-3810
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• 2022

OpenMC is a community-driven open-source Monte Carlo neutron and photon transport simulation code. The Weight Window Mesh (WWM) function and an automatic Global Variance Reduction (GVR) method was recently developed and implemented in a developmental branch of OpenMC. This WWM function and GVR method broaden OpenMC's usage in general purposes deep penetration shielding calculations. However, the Local Variance Reduction (LVR) method, which suits the source-detector problem, is still missing in OpenMC. In this work, the Weight Window Generator (WWG) function has been developed and benchmarked for the same branch. This WWG function allows OpenMC to generate the WWM for the source-detector problem on its own. Single-material cases with varying shielding and sources were used to benchmark the WWG function and investigate how to set up the particle histories utilized in WWG-run and WWM-run. Results show that there is a maximum improvement of WWM generated by WWG. Based on the above results, instructions on determining the particle histories utilized in WWG-run and WWM-run for optimal computation efficiency are given and tested with a few multi-material cases. These benchmarks demonstrate the ability of the OpenMC WWG function and the above instructions for the source-detector problem. This developmental branch will be released and merged into the main distribution in the future.

• Steel and Composite Structures
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• v.21 no.6
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• pp.1327-1345
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• 2016

$Al_2O_3$/SiC particulate reinforced (Metal Matrix Composites) MMCs which were produced by using stir casting process, bending strength and hardening behaviour were obtained using an analysis of variance (ANOVA) technique that uses full factorial design. Factor variables and their ranges were: particle size $2-60{\mu}m$; the stirring speed 450 rpm, 500 rpm and the stirring temperature $620^{\circ}C$, $650^{\circ}C$. An empirical equation was derived from test results to describe the relationship between the test parameters. This model for the tensile strength of the hybrid composite materials with $R^2$ adj = 80% for the bending strength $R^2$ adj = 89% were generated from the data. The regression coefficients of this model quantify the tensile strength and bending strengths of the effects of each of the factors. The interactions of all three factors do not present significant percentage contributions on the tensile strength and bending strengths of hybrid composite materials. Analysis of the residuals versus was predicted the tensile strength and bending strengths show a normalized distribution and thereby confirms the suitability of this model. Particle size was found to have the strongest influence on the tensile strength and bending strength.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.5
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• pp.70-75
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• 2018

This study analyzes the change of Al 6061-T6 specimen surface shape when undergoing microparticle spraying and analyzes the influence of factors on the experiment. Fine particle spraying is applied to the specimen and the surface shape of the processed surface is measured through a surface shape measuring device. The measured data was analyzed by the ANOVA method to investigate the effect of factors such as particle, nozzle diameter, pressure, injection height, and injection time on the injection depth and injection diameter.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.148-154
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• 2016

A study on the micro particle blasting was conducted to find the optimum conditions of the blasted surface of aluminum 6061. The particle type such as $Al_2O_3$ and SiC, nozzle diameter, pressure, standoff distance and injection time were used as blasting conditions. Statistical method of orthogonal arrays(ANOVA) was used to find optimum conditions of maximum depth and maximum diameter of blasted surface. Particle type, nozzle diameter, and pressure were found to be the main factors of maximum blasted depth and diameter. Maximum blasted diameter was affected by increasing pressure and nozzle diameter but saturated maximum diameter. Maximum blasted depth was affected by pressure and nozzle diameter when aluminum 6061 was blasted with $Al_2O_3$ particle. The value of surface roughness was increased as pressure and nozzle diameter increased when aluminum 6061 was blasted with SiC.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.426-433
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• 2016

This paper investigates the effects of operating conditions of an air classifier mill (ACM) on the particle sizes of PVC and rice hull. Based on the Box-Behnken matrix, the pulverization experiments were performed considering three operating factors: the air flow rate, the classifier speed and the mill speed. The response surface methodology was applied to identify the effects of the operating factors on the particle size. Results show that the particle sizes are governed by the linear variations of the operating factors. As less air is supplied and the mill rotates more slowly, the powder of both PVC and rice hull becomes finer. Furthermore, the classifier speed has a significant effect on the PVC powder but almost no effect on the rice hull powder. Thus, it is found that strong interactions exist between the material characteristics of a particle and the operating conditions of the ACM.

• Earthquakes and Structures
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• v.5 no.3
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• pp.359-378
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• 2013

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

• ETRI Journal
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• v.42 no.6
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• pp.932-942
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• 2020

To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1120-1125
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• 2016

The neutron decay constant, ${\alpha}$, and effective delayed neutron fraction, ${\beta}_{eff}$, are important parameters for the control of the dynamic behavior of nuclear reactors. For the heavy water zero power reactor (HWZPR), this document describes the measurements of the neutron decay constant by noise analysis methods, including variance to mean (VTM) ratio and endogenous pulse source (EPS) methods. The measured ${\alpha}$ is successively used to determine the experimental value of the effective delayed neutron fraction as well. According to the experimental results, ${\beta}_{eff}$ of the HWZPR reactor under study is equal to 7.84e-3. This value is finally used to validate the calculation of the effective delayed neutron fraction by the Monte Carlo methods that are discussed in the document. Using the Monte Carlo N-Particle (MCNP)-4C code, a ${\beta}_{eff}$ value of 7.58e-3 was obtained for the reactor under study. Thus, the relative difference between the ${\beta}_{eff}$ values determined experimentally and by Monte Carlo methods was estimated to be < 4%.