• 제목/요약/키워드: Discretization error

검색결과 77건 처리시간 0.032초

Simulation of Voltage and Current Distributions in Transmission Lines Using State Variables and Exponential Approximation

  • Dan-Klang, Panuwat;Leelarasmee, Ekachai
    • ETRI Journal
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    • 제31권1호
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    • pp.42-50
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    • 2009
  • A new method for simulating voltage and current distributions in transmission lines is described. It gives the time domain solution of the terminal voltage and current as well as their line distributions. This is achieved by treating voltage and current distributions as distributed state variables (DSVs) and turning the transmission line equation into an ordinary differential equation. Thus the transmission line is treated like other lumped dynamic components, such as capacitors. Using backward differentiation formulae for time discretization, the DSV transmission line component is converted to a simple time domain companion model, from which its local truncation error can be derived. As the voltage and current distributions get more complicated with time, a new piecewise exponential with controllable accuracy is invented. A segmentation algorithm is also devised so that the line is dynamically bisected to guarantee that the total piecewise exponential error is a small fraction of the local truncation error. Using this approach, the user can see the line voltage and current at any point and time freely without explicitly segmenting the line before starting the simulation.

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중심 및 상류 컴팩트 차분기법을 적용한 난류유동의 LES (On Large Eddy Simulation with Centered and Upwind Compact Difference Schemes)

  • 박노마;유정열;최해천
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2002년도 학술대회지
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    • pp.807-810
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    • 2002
  • The suitability of high-order accurate, central and upwind-biased compact difference schemes is evaluated for the large-eddy simulations of flows in complex geometry. Two flow geometries are considered: channel and circular cylinder. The effects of numerical dissipation and aliasing error on the evaluation of subgrid scale stress are investigated by extending the analysis by Ghosal (1) to centered and upwind compact schemes. It is shown that the failure of upwind schemes mainly comes from the aliasing error.

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AN A POSTERIORI ERROR ESTIMATE FOR MIXED FINITE ELEMENT APPROXIMATIONS OF THE NAVIER-STOKES EQUATIONS

  • Elakkad, Abdeslam;Elkhalfi, Ahmed;Guessous, Najib
    • 대한수학회지
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    • 제48권3호
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    • pp.529-550
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    • 2011
  • In this work, a numerical solution of the incompressible Navier-Stokes equations is proposed. The method suggested is based on an algorithm of discretization by mixed finite elements with a posteriori error estimation of the computed solutions. In order to evaluate the performance of the method, the numerical results are compared with some previously published works or with others coming from commercial code like Adina system.

Level Set 방법에 의한 상경계 추적 수치기법 연구 (Study on the Phase Interface Tracking Numerical Schemes by Level Set Method)

  • 김원갑;정재동
    • 대한설비공학회:학술대회논문집
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    • 대한설비공학회 2006년도 하계학술발표대회 논문집
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    • pp.116-121
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    • 2006
  • Numerical simulations for dendritic growth of crystals are conducted in this study by the level set method. The effect of order of difference is tested for reinitialization error in simple problems and authors founded in case of 1st order of difference that very fine grids have to be used to minimize the error and higher order of difference is desirable to minimize the reinitialization error The 2nd and 4th order Runge-Kutta scheme in time and 3rd and 5th order of WENO schemes with Godunov scheme are applied for space discretization. Numerical results are compared with the analytical theory, phase-field method and other researcher's level set method.

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최적화 방법을 이용한 Delaunay 격자의 내부 격자밀도 적응 방법 (Delaunay mesh generation technique adaptive to the mesh Density using the optimization technique)

  • 홍진태;이석렬;박철현;양동열
    • 한국소성가공학회:학술대회논문집
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    • 한국소성가공학회 2004년도 추계학술대회논문집
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    • pp.75-78
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    • 2004
  • A mesh generation algorithm adapted to the mesh density map using the Delaunay mesh generation technique is developed. In the finite element analyses of the forging processes, the numerical error increases as the process goes on because of discrete property of the finite elements or severe distortion of elements. Especially, in the region where stresses and strains are concentrated, the numerical discretization error will be highly increased. However, it is too time consuming to use a uniformly fine mesh in the whole domain to reduce the expected numerical error. Therefore, it is necessary to construct locally refined mesh at the region where the error is concentrated such as at the die corner. In this study, the point insertion algorithm is used and the mesh size is controlled by moving nodes to optimized positions according to a mesh density map constructed with a posteriori error estimation. An optimization technique is adopted to obtain a good position of nodes. And optimized smoothing techniques are also adopted to have smooth distribution of the mesh and improve the mesh element quality.

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A MESH-INDEPENDENCE PRINCIPLE FOR OPERATORS EQUATIONS AND THE STEFFENSEN METHOD

  • Argyros, Ioannis-K.
    • Journal of applied mathematics & informatics
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    • 제4권2호
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    • pp.323-340
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    • 1997
  • In this study we prove the mesh-independence principle via Steffensen's method. This principle asserts that when Steffensen's method is applied to a nonlinear equation between some Banach spaces as well as to some finite-dimensional discretization of that equation then the behavior of th discretized process is asymptoti-cally the same as that for the original iteration. Local and semilo-cal convergencve results as well as an error analysis for Steffensen's method are also provided.

Asymptotics for realized covariance under market microstructure noise and sampling frequency determination

  • Shin, Dong Wan;Hwang, Eunju
    • Communications for Statistical Applications and Methods
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    • 제23권5호
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    • pp.411-421
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    • 2016
  • Large frequency limiting distributions of two errors in realized covariance are investigated under noisy and non-synchronous high frequency sampling situations. The first distribution characterizes increased variance of the realized covariance due to noise for large frequency and the second distribution characterizes decreased variance of the realized covariance due to discretization for large frequency. The distribution of the combined error enables us to determine the sampling frequency which depends on a nuisance parameter. A consistent estimator of the nuisance parameter is proposed.

A NUMERICAL METHOD FOR SOLVING THE FREDHOLM INTEGRAL EQUATION OF THE SECOND KIND

  • Sridharan, V.;Jayashree, P.R.
    • Journal of applied mathematics & informatics
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    • 제5권2호
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    • pp.293-300
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    • 1998
  • The numerical method is used to solve the Fredholm integral equation of the second kind with weak singular kernels using the Toeplitz matrices. The solution has a computing time requir-ment of O(N2) where 2N+1 is the number of discretization points used. Also the error estimate is computed. Some numerical Exam-ples are computed using the Mathcad package.

SUPERCONVERGENCE OF CRANK-NICOLSON MIXED FINITE ELEMENT SOLUTION OF PARABOLIC PROBLEMS

  • Kwon, Dae Sung;Park, Eun-Jae
    • Korean Journal of Mathematics
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    • 제13권2호
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    • pp.139-148
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    • 2005
  • In this paper we extend the mixed finite element method and its $L_2$-error estimate for postprocessed solutions by using Crank-Nicolson time-discretization method. Global $O(h^2+({\Delta}t)^2)$-superconvergence for the lowest order Raviart-Thomas element ($Q_0-Q_{1,0}{\times}Q_{0,1}$) are obtained. Numerical examples are presented to confirm superconvergence phenomena.

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A NUMERICAL METHOD FOR SOLVING THE NONLINEAR INTEGRAL EQUATION OF THE SECOND KIND

  • Salama, F.A.
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • 제7권2호
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    • pp.65-73
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    • 2003
  • In this work, we use a numerical method to solve the nonlinear integral equation of the second kind when the kernel of the integral equation in the logarithmic function form or in Carleman function form. The solution has a computing time requirement of $0(N^2)$, where (2N +1) is the number of discretization points used. Also, the error estimate is computed.

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