Thermal Conduction: Computational Model and Engineering Applications

Bashir M. Suleiman; Heba Y. Youssef; Kais Daoudi; Zaher Aghbari; Azar A. Amani; Mounir Kaidi

doi:10.4028/p-ma1fy9

Paper Titles

Mechanical, Thermal and Morphological Properties of Poly(Lactic Acid) and Poly(Butylene Adipate-co-Terephthalate) Blends with Organoclay
p.35

Modelling the Toughness of Nanostructured Polyhedral Oligomeric Silsesquioxane Composites Fabricated by Stereolithography 3D Printing: A Response Surface Methodology and Artificial Neural Network Approach
p.41

Property Improvement of Recycled Nylon 6 by Thermoplastic Poly(Ether-Ester) Elastomer and Wollastonite
p.47

Magneto-Transport Properties and Hole Self-Doping due to Excess Oxygen Addition in Polycrystalline LaMnO₃
p.55

Thermal Conduction: Computational Model and Engineering Applications
p.61

First Principle Calculation and Thermodynamic Analysis of Coexisting Phase of Cu-Cr-Sn Copper Alloy
p.71

Machine Learning Enabled Prediction of Electromagnetic Interference Shielding Effectiveness of Poly(Vinylidene Fluoride)/Mxene Nanocomposites
p.77

Tailoring the Everolimus Immobilisation Time on Poly(L-Lactic Acid/ Poly(D-Lactic Acid) Scaffold for Biodegradable Stent Coating Development
p.85

Self-Healing of Epoxy-Loaded Halloysite Nanotubes/Polysulfone Nanocomposite Membrane
p.91

HomeMaterials Science ForumMaterials Science Forum Vol. 1053Thermal Conduction: Computational Model and...

Thermal Conduction: Computational Model and Engineering Applications

Abstract:

MATLAB was used to compute the effective thermal conduction of different samples of hard isotropic low porosity composites. The computational algorithms intended to use the Effective Medium Theory (EMT) Model to estimate the effective thermal conductivity (k_eff) of homogeneous composites. It estimates k_eff of a homogeneous mixture of components with known volume fractions and components’ conductivities. Starting with preliminary indicators to check the homogeneity conditions, we follow two approaches one for the measured samples and the other for a hypothetical sample with a certain specific desired property. One approach is to use preliminary indicators of surface homogeneity of the measured samples either via electron and/or via optical transmission microfilm scanning. The other approach is used for both measured and hypothetical samples by assuming a layered structure of parallel and series of slabs to compute upper and lower bounds of conduction. We used MATLAB to implement a fine precision computing algorithm to investigate the composite samples. The results predicted by the EMT model were examined for validation. The deviation of k_eff from the experiment for the homogenous samples is between 3 to 28% depending on the uniformity of distribution of phases within the composite matrix.

You might also be interested in these eBooks

Material Science and Engineering Technology X

View Preview

Info:

Periodical:

Materials Science Forum (Volume 1053)

Pages:

61-70

DOI:

https://doi.org/10.4028/p-ma1fy9

Citation:

Cite this paper

Online since:

February 2022

Authors:

Bashir M. Suleiman*, Heba Y. Youssef, Kais Daoudi, Zaher Aghbari, Azar A. Amani, Mounir Kaidi

Keywords:

Computing Algorithm, Computing Models, Homogenous Materials, MATLAB, Thermal Conductivity

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] J. Lee, S. Thompson, and T. Lacy Jr., Thermal spreading analysis of a transversely isotropic heat spreader, International Journal of Thermal Sciences 118 (2017) 461-47, https://doi.org/10.1016/j.ijthermalsci.2017.05.009.

DOI: 10.1016/j.ijthermalsci.2017.05.009

Google Scholar

[2] L. Codecasa and L. Di Rienzo, Compact thermal models for stochastic thermal analysis,, Microelectron. J., vol. 45, no. 12, p.1770–1776, Dec. 2014,.

DOI: 10.1016/j.mejo.2014.04.040

Google Scholar

[3] A. A. Merrikh, Compact thermal modeling methodology for predicting skin temperature of passively cooled devices,, Appl. Therm. Eng., vol. 85, p.287–296, Jun. 2015,.

DOI: 10.1016/j.applthermaleng.2015.04.007

Google Scholar

[4] S. J. Zinkle and L. L. Snead, Thermophysical and mechanical properties of SiC/SiC composites," Art. no. DOE/ER--0313/24, 1998, Accessed: Sep. 18, 2020. [Online]. Available: http://inis.iaea.org/Search/search.aspx,orig_q=RN:30039835.

DOI: 10.2172/330618

Google Scholar

[5] Maxwell J.C. (1892), A Treatise on Electricity and Magnetism, third ed., vol. 1, Dover, New York, 1954, Oxford: Clarendon pp.435-41.

Google Scholar

[6] L. R. S. R.S, LVI. On the influence of obstacles arranged in rectangular order upon the properties of a medium,, Lond. Edinb. Dublin Philos. Mag. J. Sci., vol. 34, no. 211, p.481–502, Dec. 1892,.

DOI: 10.1080/14786449208620364

Google Scholar

[7] Z. Hashin, Assessment of the Self Consistent Scheme Approximation: Conductivity of Particulate Composites,, J. Compos. Mater., Jul. 2016,.

Google Scholar

[8] J. D. Felske, Effective thermal conductivity of composite spheres in a continuous medium with contact resistance,, Int. J. Heat Mass Transf., vol. 47, no. 14, p.3453–3461, Jul. 2004,.

DOI: 10.1016/j.ijheatmasstransfer.2004.01.013

Google Scholar

[9] T. W. Noh, P. H. Song, and A. J. Sievers, Self-consistency conditions for the effective-medium approximation in composite materials,, Phys. Rev. B, vol. 44, no. 11, p.5459–5464, Sep. 1991,.

DOI: 10.1103/physrevb.44.5459

Google Scholar

[10] Wiener O. (1912) Abh. Math. -Phys. Kl. König. Sächs. Ges. Wiss.(Leipz.) 32, 509.

Google Scholar

[11] B. Ghanbarian and H. Daigle, Thermal conductivity in porous media: Percolation-based effective-medium approximation,, Water Resour. Res., vol. 52, no. 1, p.295–314, 2016,.

DOI: 10.1002/2015wr017236

Google Scholar

[12] U. Schärli and L. Rybach, On the thermal conductivity of low-porosity crystalline rocks,, Tectonophysics, vol. 103, no. 1, p.307–313, Mar. 1984,.

DOI: 10.1016/0040-1951(84)90092-1

Google Scholar

[13] B. M. Suleiman, S. E. Gustafsson, E. Karawacki, R. Glamheden, and U. Lindblom, The Effective Thermal Conductivity of a Multi-Phase System,, J. Therm. Anal. Calorim., vol. 51, no. 2, p.349–360, Feb. 1998,.

DOI: 10.1007/bf03340177

Google Scholar

[14] G. Ivan, S. Nikolay, L. Vladislav, and P. Andrei, Solving of Mathematical Problems in the C# Based on Integration with MATLAB,, in 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), May 2020, p.432–435,.

DOI: 10.1109/usbereit48449.2020.9117751

Google Scholar