• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.11 no.1
• /
• pp.7-19
• /
• 1999

The energy transfer between sea-wave components by way of nonlinear wave-wave interactions plays a central role in spectral evolution. Since huge calculation time is required to exact computation of the resulting Boltzmann integral, however, the exact nonlinear energy transfer has not been directly introduced into operational wave models. Thus, effective calculation methods were examined in the present study which exploit the scale property of a scattering coefficient and the detailed balance of interactions. The improved Webb's method (IWM) has inherent stability because singularities degenerate into a negligible point. The improved Masuda's method (IMM) makes a quasi-analytical treatment of the inherent singularities and requires only 1.3 seconds of computer time via Pentium 300MHz processor. The IMM is, therefore, projected to be very useful for theoretical researches in spectral evolution with fetch- or duration-limited situations.

• Journal of Convergence for Information Technology
• /
• v.9 no.8
• /
• pp.248-253
• /
• 2019

Early diagnosis of mild cognitive impairment (MCI) can reduce the incidence of dementia. This study developed the MCI prediction model for the elderly in Korea. The subjects of this study were 3,240 elderly (1,502 men, 1,738 women) aged 65 and over who participated in the Korean Longitudinal Survey of Aging (KLoSA) in 2012. Outcome variables were defined as MCI prevalence. Explanatory variables were age, marital status, education level, income level, smoking, drinking, regular exercise more than once a week, average participation time of social activities, subjective health, hypertension, diabetes Respectively. The prediction model was developed using Restricted Boltzmann Machine (RBM) neural network. As a result, age, sex, final education, subjective health, marital status, income level, smoking, drinking, regular exercise were significant predictors of MCI prediction model of rural elderly people in Korea using RBM neural network. Based on these results, it is required to develop a customized dementia prevention program considering the characteristics of high risk group of MCI.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.2
• /
• pp.181-186
• /
• 2022

Poisson-Boltzmann equation (PBE) is used to model problems arising from various disciplinary including bio-pysics and colloid chemistry. Therefore, to predict a numerical solution of PBE is an important issue. The authors proposed deep learning based methods to solve PBE while the computational time to generate finite element method (FEM) solutions were bottlenecks of the algorithms. In this work, we shorten the generation time of FEM solutions in two directions. First, we experimentally find certain penalty parameter in a bilinear form. Second, we applied algebraic multigrids methods to the algebraic system so that condition number is bounded regardless of the meshsize. In conclusion, we have reduced computation times to solve algebraic systems for PBE. We expect that algebraic multigrids methods can be further employed in various disciplinary to generate deep learning samples.

• Proceedings of the KSME Conference
• /
• 2007.05b
• /
• pp.2797-2802
• /
• 2007

It has been confirmed that implementation of the no-slip boundary conditions for the lattice-Boltzmann method play an important role in the overall accuracy of the numerical solutions as well as the stability of the solution procedure. We in this paper propose a new algorithm, i.e. the method of the dynamic boundary condition for no-slip boundary condition. The distribution functions on the wall along each of the links across the physical boundary are assumed to be composed of equilibrium and nonequilibrium parts which inherit the idea of Guo's extrapolation method. In the proposed algorithm, we apply a dynamic equation to reflect the computational slip velocity error occurred on the actual wall boundary to the correction; the calculated slip velocity error dynamically corrects the fictitious velocity on the wall nodes which are subsequently employed to the computation of equilibrium distribution functions on the wall nodes. Along with the dynamic selfcorrecting process, the calculation efficiently approaches the steady state. Numerical results show that the dynamic boundary method is featured with high accuracy and simplicity.

• Journal of computational fluids engineering
• /
• v.16 no.2
• /
• pp.24-29
• /
• 2011

Immersed boundary lattice Boltzmann method has been applied to analyze the characteristics of the self-propelled fish motion swimming robot. The airfoil NACA0012 with caudal fin stroke model was considered to examine the characteristics. The foil in steady forward motion and a combination of steady-state harmonic deformation produces thrust through the formation of a flow downstream from the trailing edge. The harmonic motion of the foil causes unsteady shedding of vorticity from the trailing edge, while forming the vortices at the leading edge as well. The resultant thrust is developed by the pressure difference formed on the upper and lower surface of the airfoil. and the time averaged thrust coefficient increases as Re increase in the region of $Re{\leqq}700$. The suggested numerical method is suitable to develop the fish-motion model to control the swimming robot, however It would need to extend in 3D analysis to examine the higher Re and to determine the more detail mechanism of thrust production.

• Journal of Advanced Marine Engineering and Technology
• /
• v.34 no.1
• /
• pp.53-61
• /
• 2010

In the present study, a laminar natural convection flow of $H_2O$-Cu nanofluid in a two dimensional enclosure has been investigated using a thermal lattice Boltzmann approach with the Bhatnagar-Gross-Krook (BGK) model. The effect of suspended nanoparticles on the fluid flow and heat transfer process have been studied for different controlling parameters such as particle volume fraction ($\Phi$), Rayleigh number (Ra). For this investigation the Rayleigh number changes from 104 to 106 and volume fraction varied from 0 to 10% with three different particle diameters (dp), say 10 nm, 20 nm and 40 nm. It is shown that increasing the Rayleigh number (Ra) and the volume fraction of nanofluid causes an increase of the effective heat transfer rate in terms of average Nusselt number (Nu) as well as the thermal conductivity of nanofluid. On the other hand, increasing the particle diameter causes the decrease of the heat transfer rate and thermal conductivity. The result of the analysis are compared with experimental and numerical data both for pure and nanofluids and it is seen a relatively good agreement.

• 한국전산유체공학회:학술대회논문집
• /
• 2009.11a
• /
• pp.97-102
• /
• 2009

Translocation of biopolymers such as DNA and RNA through a nano-pore is an important process in biotechnology applications. The translocation process of a biopolymer through an artificial nano-pore in the presence of a fluid solvent is simulated. The polymer motion is simulated by Langevin molecular dynamics (MD) techniques while the solvent dynamics are taken into account by lattice-Boltzmann method (LBM). The hydrodynamic interactions are considered explicitly by coupling the polymer and solvent through the frictional and the random forces. From simulation results we found that the hydrodynamic interactions between polymer and solvent speed-up the translocation process. The translocation time ${\tao}_T$ scales with the chain length N as ${{\tau}_T}^{\propto}N^{\alpha}$. The value of scaling exponents($\alpha$) obtained from our simulations are $1.29{\pm}0.03$ and $1.41{\pm}0.03$, with and without hydrodynamic interactions, respectively. Our simulation results are in good agreement with the experimentally observed value of $\alpha$, which is equal to $1.27{\pm}0.03$, particularly when hydrodynamic interaction effects are taken into account.

• Journal of Advanced Marine Engineering and Technology
• /
• v.33 no.1
• /
• pp.62-70
• /
• 2009

In this paper, the fluid flow behavior past a pair of rectangular cylinders placed in a two dimensional horizontal channel has been investigated using Lattice-Boltzmann Method(LBM). The LBM has built up on the D2Q9 model and the single relaxation time method called the Lattice-BGK(Bhatnagar-Gross-Krook)model. Streamlines, velocity, vorticity and pressure contours are provided to analyze the important characteristics of the flow field for a wide range of non dimensional parameters that present in our simulation. Special attention is paid to the effect of spacing(d) between two cylinders and the blockage ratio A(=h/H), where H is the channel height and h is the rectangular cylinder height. for different Reynolds numbers. The first cylinder is called upstream cylinder and the second one as downstream cylinder. The downstream fluid flow fields have been more influenced by its blockage ratios(A) and Reynolds numbers(Re) whereas the upstream flow patterns(in front of downstream cylinder) by the gap length(d) between two cylinders. Moreover, it is observed that after a certain gap, both upstream and downstream flow patterns are almost similar size and shape. The simulation result has been compared with analytical solution and it is found to be in excellent agreement.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.6
• /
• pp.505-509
• /
• 2001

In this paper, we evaluate the learning ability of non-monotonic DMM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.64 no.4
• /
• pp.241-245
• /
• 2015

Energy Distribution Function in pure $CH_4$, $CF_4$ and mixtures of $CF_4$ and Ar, have been analyzed over a range of the reduced electric field strength between 0.1 and 350[Td] by the two-term approximation of the Boltzmann equation (BEq.) method and the Monte Carlo simulation (MCS). The calculations of electron swarm parameters require the knowledge of several collision cross-sections of electron beam. Thus, published momentum transfer, ionization, vibration, attachment, electronic excitation, and dissociation cross-sections of electrons for $CH_4$, $CF_4$ and Ar, were used. The differences of the transport coefficients of electrons in $CH_4$, mixtures of $CH_4$ and Ar, have been explained by the deduced energy distribution functions for electrons and the complete collision cross-sections for electrons. The results of the Boltzmann equation and the Monte Carlo simulation have been compared with the data presented by several workers. The deduced transport coefficients for electrons agree reasonably well with the experimental and simulation data obtained by Nakamura and Hayashi. The energy distribution function of electrons in $CF_4$-Ar mixtures shows the Maxwellian distribution for energy. That is, $f({\varepsilon})$ has the symmetrical shape whose axis of symmetry is a most probably energy.