Error Estimates for the Finite Difference Solution of the Heat Conduction Equation: Consideration of Boundary Conditions and Heat Sources

2013 ◽  
Vol 336 ◽  
pp. 195-207
Author(s):  
Mohammad Mahdi Davoudi ◽  
Andreas Öchsner
Keyword(s):  
Heat Conduction ◽  
Finite Difference ◽  
Boundary Conditions ◽  
Heat Conduction Equation ◽  
Exact Result ◽  
Numerical Approach ◽  
Conduction Equation ◽  
Heat Sources ◽  
Numerical Implementation ◽  
Dependent Sources

This contribution investigates the numerical solution of the steady-state heat conduction equation. The finite difference method is applied to simple formulations of heat sources where still analytical solutions can be derived. Thus, the results of the numerical approach can be related to the exact solutions and conclusions on the accuracy obtained. In addition, the numerical implementation of different forms of boundary conditions, i.e. temperature and flux condition, is compared to the exact solution. It is found that the numerical implementation of coordinate dependent sources gives the exact result while temperature dependent sources are only approximately represented. Furthermore, the implementation of the mentioned boundary conditions gives the same results as the analytical reference solution.

Assessment of Models for Extracting Thermal Conductivity From Frequency Domain Thermoreflectance Experiments

2020 ◽  
Author(s):  
Siddharth Saurav ◽  
Sandip Mazumder
Keyword(s):  
Thermal Conductivity ◽  
Heat Conduction ◽  
Boundary Conditions ◽  
Frequency Domain ◽  
Heat Conduction Equation ◽  
Conduction Equation ◽  
Hyperbolic Heat Conduction ◽  
Phase Lag ◽  
Computational Domain ◽  
Fourier Heat Conduction

Abstract The Fourier heat conduction and the hyperbolic heat conduction equations were solved numerically to simulate a frequency-domain thermoreflectance (FDTR) experimental setup. Numerical solutions enable use of realistic boundary conditions, such as convective cooling from the various surfaces of the substrate and transducer. The equations were solved in time domain and the phase lag between the temperature at the center of the transducer and the modulated pump laser signal were computed for a modulation frequency range of 200 kHz to 200 MHz. It was found that the numerical predictions fit the experimentally measured phase lag better than analytical frequency-domain solutions of the Fourier heat equation based on Hankel transforms. The effects of boundary conditions were investigated and it was found that if the substrate (computational domain) is sufficiently large, the far-field boundary conditions have no effect on the computed phase lag. The interface conductance between the transducer and the substrate was also treated as a parameter, and was found to have some effect on the predicted thermal conductivity, but only in certain regimes. The hyperbolic heat conduction equation yielded identical results as the Fourier heat conduction equation for the particular case studied. The thermal conductivity value (best fit) for the silicon substrate considered in this study was found to be 108 W/m/K, which is slightly different from previously reported values for the same experimental data.

Numerical Modelling of Nanocomposites Using GA and FD Techniques

2019 ◽  
Vol 969 ◽  
pp. 478-483 ◽  
Author(s):  
Siddhartha Kosti ◽  
Jitender Kundu
Keyword(s):  
Genetic Algorithm ◽  
Heat Conduction ◽  
Finite Difference ◽  
Numerical Modelling ◽  
Structural Properties ◽  
Heat Conduction Equation ◽  
Conduction Equation ◽  
Weight Percentage ◽  
Finite Difference Technique ◽  
Finite Difference Techniques

Use of nanocomposites is increasing rapidly due to their enhanced thermal and structural properties. In the present work, the numerical modelling of nanocomposites is conducted with the help of the (GA) genetic algorithm and (FD) finite difference techniques to find out a set of nanocomposites with best thermal and structural properties. The genetic algorithm is utilized to find out the best set of nanocomposites on the basis of thermal and structural properties while the finite difference technique is utilized to solve the heat conduction equation. Different nanocomposites considered in the present work are Al-B4C, Al-SiC and Al-Al2O3. The weight percentage of these nanocomposites is varied to see its effect on the nanocomposites properties. In the end, the solidification curve for all the nanocomposites is plotted and analysed. Result reveals that GA helps in identifying the best set of nanocomposites while FD technique helps in predicting the solidification curve accurately. Increment in the wt. % of nanocomposites makes the solidification curve steeper.

The numerical solution of the heat conduction equation in one dimension

1964 ◽  
Vol 60 (4) ◽  
pp. 897-907 ◽  
Author(s):  
M. Wadsworth ◽  
A. Wragg
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Heat Conduction ◽  
Boundary Conditions ◽  
Numerical Method ◽  
Heat Conduction Equation ◽  
Linear Equations ◽  
Conduction Equation ◽  
Partial Differential ◽  
Direct Numerical Method

AbstractThe replacement of the second space derivative by finite differences reduces the simplest form of heat conduction equation to a set of first-order ordinary differential equations. These equations can be solved analytically by utilizing the spectral resolution of the matrix formed by their coefficients. For explicit boundary conditions the solution provides a direct numerical method of solving the original partial differential equation and also gives, as limiting forms, analytical solutions which are equivalent to those obtainable by using the Laplace transform. For linear implicit boundary conditions the solution again provides a direct numerical method of solving the original partial differential equation. The procedure can also be used to give an iterative method of solving non-linear equations. Numerical examples of both the direct and iterative methods are given.

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