A Green's function for steady-state two-dimensional isotropic heat conduction across a homogeneously imperfect interface

2004 ◽  
Vol 20 (5) ◽  
pp. 391-399 ◽  
Author(s):  
W. T. Ang ◽  
K. K. Choo ◽  
H. Fan
Keyword(s):  
Heat Conduction ◽  
Steady State ◽  
Green’S Function ◽  
Green's Function ◽  
Imperfect Interface ◽  
Two Dimensional

Numerical Green’s Function for Steady-State Heat Conduction in Heterogeneous Media

1998 ◽  
Vol 120 (4) ◽  
pp. 284-286 ◽  
Author(s):  
Deok-Kee Choi ◽  
Seiichi Nomura
Keyword(s):  
Heat Conduction ◽  
Steady State ◽  
Green’S Function ◽  
Green's Function ◽  
Heterogeneous Media ◽  
Homogeneous Boundary Condition ◽  
State Heat ◽  
The Matrix ◽  
Steady State Heat ◽  
The Galerkin Method

Numerical Green's function for steady-state heat conduction problems is derived in a finite-sized medium that may contain inclusions (fibers) in the matrix phase. Green's function is approximated by employing the Galerkin method that uses permissible functions which satisfy the homogeneous boundary condition for the given geometry. The present approach allows physical fields in a medium that contain multiple inclusions to be expressed through isolated integrals semi-analytically while retaining all the relevant material parameters.

scholarly journals DIFFERENTIAL EQUATION APPROACH FOR ONE- AND TWO-DIMENSIONAL LATTICE GREEN'S FUNCTION

2007 ◽  
Vol 21 (02n03) ◽  
pp. 139-154 ◽  
Author(s):  
J. H. ASAD
Keyword(s):  
Differential Equation ◽  
Green’S Function ◽  
Recurrence Relation ◽  
Green's Function ◽  
Two Dimensional ◽  
Dimensional Lattice ◽  
One Dimensional ◽  
The One ◽  
Lattice Green's Function ◽  
Lattice Green’S Function

A first-order differential equation of Green's function, at the origin G(0), for the one-dimensional lattice is derived by simple recurrence relation. Green's function at site (m) is then calculated in terms of G(0). A simple recurrence relation connecting the lattice Green's function at the site (m, n) and the first derivative of the lattice Green's function at the site (m ± 1, n) is presented for the two-dimensional lattice, a differential equation of second order in G(0, 0) is obtained. By making use of the latter recurrence relation, lattice Green's function at an arbitrary site is obtained in closed form. Finally, the phase shift and scattering cross-section are evaluated analytically and numerically for one- and two-impurities.

scholarly journals Chemical Source Inversion Using Assimilated Constituent Observations in an Idealized Two-Dimensional System

2009 ◽  
Vol 137 (9) ◽  
pp. 3013-3025
Author(s):  
Andrew Tangborn ◽  
Robert Cooper ◽  
Steven Pawson ◽  
Zhibin Sun
Keyword(s):  
Kalman Filter ◽  
Green’S Function ◽  
Green's Function ◽  
Transport Model ◽  
Chemical Constituents ◽  
Gaussian Model ◽  
Satellite Observation ◽  
Dimensional System ◽  
Two Dimensional ◽  
Source Inversion

Abstract A source inversion technique for chemical constituents is presented that uses assimilated constituent observations rather than directly using the observations. The method is tested with a simple model problem, which is a two-dimensional Fourier–Galerkin transport model combined with a Kalman filter for data assimilation. Inversion is carried out using a Green’s function method and observations are simulated from a true state with added Gaussian noise. The forecast state uses the same spectral model but differs by an unbiased Gaussian model error and emissions models with constant errors. The numerical experiments employ both simulated in situ and satellite observation networks. Source inversion was carried out either by directly using synthetically generated observations with added noise or by first assimilating the observations and using the analyses to extract observations. Twenty identical twin experiments were conducted for each set of source and observation configurations, and it was found that in the limiting cases of a very few localized observations or an extremely large observation network there is little advantage to carrying out assimilation first. For intermediate observation densities, the source inversion error standard deviation is decreased by 50% to 90% when the observations are assimilated with the Kalman filter before carrying out the Green’s function inversion.

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