• Tunnel and Underground Space
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• v.12 no.4
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• pp.259-267
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• 2002

Recently, Umbrella Arch Method(UAM) is commonly used in order to enhance the stability of tunnel itself and stabilize the adjacent surface structure. But quantitative estimation of reinforcement effect is needed because UAM is designed and constructed only on the basis of empirical experience. By using 3-dimensional finite element method, parametric study is performed for elastic modulus of ground and overburden, and reinforcement effect is analyzed quantitatively. From the results, surface settlement decreases about 9%∼27% in soil tunnel, about 4%∼24% in weathered rock tunnel and 4%∼17% in soft rock tunnel when applied with UAM. The prediction equation for final surface settlement is suggested through regression analysis and the equation is expressed as exponential function which has variable Smax, unknown coefficient i and k.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.3 s.43
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• pp.33-42
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• 2005

Frequency domain Volterra model is applied to nonlinear parameter identification procedure for dynamic systems modeled by nonlinear function. The frequency domain Volterra kernels, which correspond io linear, quadratic, and cubic transfer functions in lime domain, are incorporated in nonlinear parametric identification procedure. The nonlinear transfer functions, which can be derived from the Volterra series representation of the nonlinear differential equation of the system by Schetzen's method(1980), are directly used for modeling input output relation. The error is defined by the difference between the observed output and the estimated output which is calculated by substituting the observed input to nonlinear frequency domain model. The system parameters are searched by minimizing the error. Volterra model guarantees enough accuracy and convergence and the estimated coefficients have a good agreement with their actual values not only in the linear frequency region but also in the legion where the $2^{nd}\;or\;3^{rd}$ order nonlinearity is dominant.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Proceedings of the KSME Conference
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• 2000.11b
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• pp.192-198
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• 2000

Three dimensional thermal cycle analysis of the plunger is carried out in repeated forming process of the TV glass, which is continued work of two dimensional analysis where an efficient method has been proposed. The plunger undergoes temperature fluctuation during a cycle due to the repeated contact and separation from the glass, which attains a cyclic steady state having same temperature history at every cycle. Straightforward analysis of this problem brings about more than 90 cycles to get reasonable solution. An exponential function fitting method is proposed, which finds exponential function to best approximate temperature values of 3 consecutive cycles, and new cycle is restarted with the fitted value at infinite time. Number of cases are analyzed using the proposed method and compared to the result of straightforward repetition, from which one finds that the method always reaches nearly convergent solution within $9{\sim}12$ cycles, but turns around afterwards without further convergence. Two step use is found most efficient, in which the exponential fitting is carried out fer the first 12 cycles, followed by simple repetition, which shows fast convergence expending only 6 additional cycles to get the accuracy within 2 error. This reduces the computation cycle remarkably from 90 to 18, which is 80% reduction. From the parametric studies, one reveals that the overall thermal behavior of the plunger in terms of cooling parameters and time is similar to that of 2 dimensional analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• Journal of KSNVE
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• v.6 no.2
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• pp.163-177
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• 1996

An investigation on thermohydraulic stability of flow oscillations in the CANada Deuterium Uranium-600(CANDU-6) heat transport system has been conducted. Flow oscillations in reactor coolant loops, comprising two heat sources and two heat sinks in series, are possibly caused by the response of the pressure to extraction of fluid in two-phase region. This response consists of two contributions, one arising from mass and another from enthalpy change in the two-phase region. The system computer code used in the investigation os SOPHT, which is capable of simulating steady states as well as transients with varying boundary conditions. The model was derived by linearizing and solving one-dimensional, homogeneous single- and two-phase flow conservation equations. The mass, energy and momentum equations with boundary conditions are set up throughout the system in matrix form based on a node-link structure. Loop stability was studied under full power conditions with interconnecting the two compressible two phase regions in the figure-of-eight circuit. The dominant function of the interconnecting pipe is the transfer of mass between the two-phase regions. Parametric survey of loop stability characteristics, i. e., damping ratio and period, has been made as a function of geometrical parameters of the interconnection line such as diameter, length, height and orifice flow coefficient. The stability characteristics with interconnection line has been clarified to provide a simple criterion to be used as a guide in scaling of the pipe.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.422-432
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• 2013

A System Identification method to develop parametric models linking free surface elevation and wave pressure is presented and two models are built allowing for either wave pressure or free surface elevation simulation. Linear, time invariant model structures with static nonlinearities are assumed and solutions are sought in a form of autoregressive model with extra input (ARX). An arbitrary chosen free-surface elevation and wave pressure dataset is used for estimation of the models, which are subsequently verified against datasets with similar pressure gauge depth but different free-surface elevation spectra due to different meteorological conditions. It is shown that free-surface simulation using System Identification methods can perform better than traditional linear transfer function derived from linear wave theory (LTF), while wave pressure simulation quality using presented methods is generally similar to that obtained with corrected LTF.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.

• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.2
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• pp.111-125
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• 2015

For engineers, generating a mesh in porous media (PMs) sometimes represents a smaller computational load than generating realistic stent geometries with computer fluid dynamics (CFD). For this reason, PMs have recently become attractive to mimic flow-diverter stents (FDs), which are used to treat intracranial aneurysms. PMs function by introducing a hydraulic resistance using Darcy's law; therefore, the pressure drop may be computed by test sections parallel and perpendicular to the main flow direction. However, in previous studies, the pressure drop parallel to the flow may have depended on the width of the gap between the stent and the wall of the test section. Furthermore, the influence of parameters such as the test section geometry and the distance over which the pressure drops was not clear. Given these problems, computing the pressure drop parallel to the flow becomes extremely difficult. The aim of the present study is to resolve this lack of information for stent modeling using PM and to compute the pressure drop using several methods to estimate the influence of the relevant parameters. To determine the pressure drop as a function of distance, an FD was placed parallel and perpendicular to the flow in test sections with rectangular geometries. The inclined angle method was employed to extrapolate the flow patterns in the parallel direction. A similar approach was applied with a cylindrical geometry to estimate loss due to pipe friction. Additionally, the pressure drops were computed by using CFD. To determine if the balance of pressure drops (parallel vs perpendicular) affects flow patterns, we calculated the flow patterns for an ideal aneurysm using PMs with various ratios of parallel pressure drop to perpendicular pressure drop. The results show that pressure drop in the parallel direction depends on test section. The PM thickness and the ratio of parallel permeability to perpendicular permeability affect the flow pattern in an ideal aneurysm. Based on the permeability ratio and the flow patterns, the pressure drop in the parallel direction can be determined.

• Advances in nano research
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• v.16 no.4
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• pp.413-426
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• 2024

This paper investigates the dynamic behavior of a simply supported viscoelastic plate made of functionally graded carbon nanotube reinforced composite under dynamic loading. Carbon nanotubes are distributed in 5 different shapes: U, V, A, O and X, depending on the shape they form through the thickness of the plate. The displacement fields are derived in the Laplace domain using a higher-order shear deformation theory. Equations of motion are obtained through the application of the energy method and Hamilton's principle. The resulting equations of motion are solved using Navier's method. Transforming the Laplace domain displacements into the time domain involves Durbin's modified inverse Laplace transform. To validate the accuracy of the developed algorithm, a free vibration analysis is conducted for simply supported plate made of functionally graded carbon nanotube reinforced composite and compared against existing literature. Subsequently, a parametric forced vibration analysis considers the influence of various parameters: volume fractions of carbon nanotubes, their distributions, and ratios of instantaneous value to retardation time in the relaxation function, using a linear standard viscoelastic model. In the forced vibration analysis, the dynamic distributed load applied to functionally graded carbon nanotube reinforced composite viscoelastic plate is obtained in terms of double trigonometric series. The study culminates in an examination of maximum displacement, exploring the effects of different carbon nanotube distributions, volume fractions, and ratios of instantaneous value to retardation times in the relaxation function on the amplitudes of maximum displacements.