• Journal of the Korean Chemical Society
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• v.56 no.4
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• pp.431-439
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• 2012

Dissipative particle dynamics (DPD) was carried out to study the nucleation and crystal growth process of $CeO_2$ nanoparticles in different alcohol aqueous solutions. The results showed that the nucleation and crystal growth process of $CeO_2$ can be classified into three stages: nuclei growth, crystal stabilization and crystal aggregation except the initial induction stage, which could be reproduced by collecting simulation results after different simulation time. Properly selecting the sizes of $CeO_2$ and water bead was crucial in the simulation system. The influence of alcohol type and content in solutions, and precipitation temperature on the particle dimension were investigated in detail and compared with the experimental results. The consistency between simulation results and experimental data verify that the simulation can reproduce the macroscopic particle aggregation process. The effect of solvent on the nucleation and crystal growth of $CeO_2$ nanoparticles are different at three stages and can not be simply described by Derjaguin-Landau-Verwey-Overbeek (DLVO) theory or nucleation thermodynamics theory. Our work demonstrated that DPD methods can be applied to study nanoparticle forming process.

• Journal of Korea Game Society
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• v.11 no.6
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• pp.193-199
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• 2011

Producing high density crowd simulation is time-consuming task as increasing the number of individuals in the crowds. In this paper, we propose a new control technique that can create realistic high density crowd simulation by using Smoothed Particle Hydrodynamics (SPH) method from fluid simulation field. Equations in SPH method are modified for evacuation, distance maintenance, and group maintenance forces for individual behaviors in the crowds. Experimental results showed that the proposed system could enable natural high density crowd simulation efficiently.

• Journal of Powder Materials
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• v.4 no.1
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• pp.55-62
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• 1997

Flow and heat transfer characteristics of gas, and trajectories and cooling characteristics of droplets/particles in a gas atomizer were investigated by a numerical simulation using FLUENT code. Among several kinds of solution method, the k-$\varepsilon$ turbulent model, power-law scheme, SIMPLE algorithm is adopted in this study. Momentum and heat exchange between a continuous phase(gas) and a dispersed phase(particle) were taken into account. Particle trajectories are simulated using the Lagrangian method, and Rosin-Rammler formula is used for the particle size distribution. Streamlines, velocities and pressures of gas, and trajectories, velocities and cooling rates of particles have been investigated for the various gas inlet conditions. Small but very intensive recirculation is found just below the melt orifice, and this recirculation seems to cause the liquid metal to spread radially. Particle trajectory depends on the particle size, the location of particle formation and the turbulent motion of gas. Small particle cools down rapidly, while large diameter particles solidify slowly, and this is mainly due to the differences in thermal inertia.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

• Progress in Superconductivity and Cryogenics
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• v.18 no.1
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• pp.10-13
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• 2016

In this study, we developed a new volume reduction technique for cesium contaminated soil by magnetic separation. Cs in soil is mainly adsorbed on clay which is the smallest particle constituent in the soil, especially on paramagnetic 2:1 type clay minerals which strongly adsorb and fix Cs. Thus selective separation of 2:1 type clay with a superconducting magnet could enable to reduce the volume of Cs contaminated soil. The 2:1 type clay particles exist in various particle sizes in the soil, which leads that magnetic force and Cs adsorption quantity depend on their particle size. Accordingly, we examined magnetic separation conditions for efficient separation of 2:1 type clay considering their particle size distribution. First, the separation rate of 2:1 type clay for each particle size was calculated by particle trajectory simulation, because magnetic separation rate largely depends on the objective size. According to the calculation, 73 and 89 % of 2:1 type clay could be separated at 2 and 7 T, respectively. Moreover we calculated dose reduction rate on the basis of the result of particle trajectory simulation. It was indicated that 17 and 51 % of dose reduction would be possible at 2 and 7 T, respectively. The difference of dose reduction rate at 2 T and 7 T was found to be separated a fine particle. It was shown that magnetic separation considering particle size distribution would contribute to the volume reduction of contaminated soil.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.1
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• pp.47-58
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• 2004

The $textsc{k}$-$\varepsilon$ algebraic stress model (KEASM) was applied to atmospheric dispersion simulation using the Lagrangian particle dispersion model and was compared with the most popular turbulence closure model in the field of atmospheric simulation, the Mellor-Yamada (MY) model. KEASM has been rarely applied to atmospheric simulation, but it includes the pressure redistribution effect of buoyancy due to heat and momentum fluxes. On the other hand, such effect is excluded from MY model. In the simulation study, the difference in the two turbulence models was reflected to both the turbulent velocity and the Lagrangian time scale. There was little difference in the vertical diffusion coefficient $\sigma$$_{z}$. However, the horizontal diffusion coefficient or calculated by KEASM was larger than that by MY model, coincided with the Pasquill-Gifford (PG) chart. The applicability of KEASM to atmospheric simulations was demonstrated by the simulations.s.

• Journal of Ocean Engineering and Technology
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• v.31 no.6
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• pp.388-396
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• 2017

A Lagrangian approach based computational fluid dynamics (CFD) was used to simulate large and/or sharp deformations and fragmentations of interfaces, including free surfaces, through tracing each particle with physical quantities. According to the concept of the particle-based CFD method, it is possible to apply it to both fluid particles and solid particles such as sand, gravel, and rock. However, the presence of more than two different phases in the same domain can make it complicated to calculate the interaction between different phases. In order to solve multiphase problems, particle interaction models for multiphase problems, including surface tension, buoyancy-correction, and interface boundary condition models, were newly adopted into the moving particle semi-implicit (MPS) method. The newly developed MPS method was used to simulate a typical validation problem involving dam breaking. Because the soil and other particles, excluding the water, may have different viscosities, various viscosity coefficients were applied in the simulations for validation. The newly developed and validated MPS method was used to simulate the mobile beds induced by broken dam flows. The effects of the viscosity on soil particles were also investigated.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3367-3382
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• 2023

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian computational fluid dynamics method that has been widely used in the analysis of physical phenomena characterized by large deformation or multi-phase flow analysis, including free surface. Despite the recent implementation of eddy-viscosity models in SPH methodology, sophisticated turbulent analysis using Lagrangian methodology has been limited due to the lack of computational performance and numerical consistency. In this study, we implement the standard and dynamic Smagorinsky model and dynamic Vreman model as sub-particle scale models based on a weakly compressible SPH solver. The large eddy simulation method is numerically identical to the spatial discretization method of smoothed particle dynamics, enabling the intuitive implementation of the turbulence model. Furthermore, there is no additional filtering process required for physical variables since the sub-grid scale filtering is inherently processed in the kernel interpolation. We simulate lid-driven flow under transition and turbulent conditions as a benchmark. The simulation results show that the dynamic Vreman model produces consistent results with experimental and numerical research regarding Reynolds averaged physical quantities and flow structure. Spectral analysis also confirms that it is possible to analyze turbulent eddies with a smaller length scale using the dynamic Vreman model with the same particle size.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.278-285
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• 2011

In the present study, the numerical prediction of the oil amount leaked from the hole of a damaged tank is investigated using the improved MPS (Moving Particle Semi-implicit) method, which was originally proposed by Koshizuka and Oka (1996) for incompressible flow. The governing equations, which consist of the continuity and Navier-Stokes equations, are solved by Lagrangian moving particles, and all terms expressed by differential operators should be replaced by the particle interaction models based on a Kernel function. The simulation results are validated though the comparison with the analytic solution based on Torricelli's equilibrium relation. Furthermore, a series of numerical simulations under the various conditions are performed in order to estimate more accurately the initial amount of leaked oil.