A well-known Maxwell’s model for evaluation of effective thermal conductivity has been modified incorporating a correction factor s. It is an important quantity, which influences the effective thermal conductivity of composite materials together with other parameters like volume fraction and thermal conductivities of the constituents. Parameter estimation technique is used to determine the value of s. Optimized value of s is used in the modified Maxwell’s model to estimate effective thermal conductivity of composite materials. The effective thermal conductivity of composite materials using an artificial neural network approach has also been calculated. Materials of different sizes of filler particles/matrix have been considered for calculation of the effective thermal conductivity. Results obtained using modified Maxwell’s model and artificial neural network technique have been compared with the experimental results available in the literature which shows a good fit.
TavmanIH. Thermal and mechanical properties of copper powder filled poly (ethylene) composites. Powder Tech1997; 91: 63–67.
4.
MamunyaYePDavydenkoVVPissisP. Electrical and thermal conductivity of polymers filled with metal powders. Euro Polymer J2007; 38: 1887–1897.
5.
GanapathyDSinghKPhelenPE. An effective unit cell approach to compute the thermal conductivity of composites with cylindrical particles. J Heat Transfer2005; 127: 553–559.
6.
FilipCGarnierBDanesF. Effective thermal conductivity of a composite in a primitive tetragonal lattice of highly conducting spheres in resistive thermal contact with the isolating matrix. J Heat Transfer2007; 129: 1617–1626.
7.
ShenMXCuiYXJingH. Thermal conductivity model of filled polymer composites. Int J Mineral Metallurgy Mater2011; 18: 623–631.
8.
HongHRenliFYanchunH. Thermal conductivity of ceramic particle filled polymer composites and theoretical predictions. J Mater Sci2007; 42: 6749–6754.
9.
SinghRSharmaPKBhoopalRS. Prediction of effective thermal conductivity of polymer composite. Indian J Pure Appl Phys2011; 49: 344–349.
10.
MaxwellJC. A treatise on electricity and magnetism, New York: Clarendon Press, 1954.
11.
BruggemanG. Calculation of various physics constants in heterogeneous substances I dielectricity constants and conductivity of mixed bodies from isotropic substances. Annalen der Physik1935; 24: 636–664.
12.
HamiltonRLCrosserOK. Thermal conductivity of heterogeneous two-component systems. Industr Eng Chem Fundamental1962; 1: 187–191.
13.
ChengSVachonR. A technique for predicting the thermal conductivity of suspensions, emulsions and porous materials. Int J Heat Mass Transfer1970; 13: 537–546.
14.
AgariYUedaANagaiS. Thermal conductivity of a polymer composite. J Appl Polymer Sci1993; 49: 1625–1634.
15.
ZhouWWangCAnQ. Thermal properties of heat conductive silicon rubber filled with hybrid fillers. J Comp Mater2008; 42: 173–187.
16.
TekceHSKumlutasDTavmanIH. Effect of particle shape on thermal conductivity of copper reinforced polymer composites. J Reinf Plastics Comp2007; 26: 113–121.
17.
BoudenneAIbosLGehinE. Anomalous behavior of thermal conductivity and diffusivity in polymeric materials filled with metallic particles. J Mater Sci2005; 401: 4163–4167.
18.
SinghRBhoopalRSKumarS. Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach. Building Environ2011; 46: 2603–2608.
19.
FangQBibyGHaqueE. Neural network modeling of physical properties of ground wheat. Cereal Chem1998; 75: 251–253.
20.
HussainMARahmanMS. Thermal conductivity prediction of fruits and vegetables using neural networks. Int J Food Properties1999; 2: 121–137.
21.
SablaniSSBaikODMarcotteM. Neural networks for predicting thermal conductivity of bakery products. J Food Eng1995; 52: 299–304.
22.
SablaniSSRahmanMS. Using neural networks to predict thermal conductivity of food as a function of moisture content, temperature and apparent porosity. Food Res Int2003; 36: 617–623.
23.
HolotescuSStoianFD. Evaluation of effective thermal conductivity of composite polymers by considering the filler distribution law. J Zheizang Univ Sci A2009; 10: 704–709.
24.
WeidenfellerBHoferMSchillingFR. Thermal conductivity, thermal diffusivity, and specific heat capacity of particle filled polypropylene. Comp Part A2004; 35: 423–429.
25.
YiXWangJ. Effects of interfacial thermal barrier resistance and particle shape and size on the thermal conductivity of AlN/PI composites. Com Sci Tech2004; 64: 1623–1628.
26.
XuZXieSZhuB. Preparation and properties of polyimide/aluminum nitride composites. Polymer Testing2004; 23: 797–801.
27.
YuSHingPHuX. Thermal conductivity of polystyrene-aluminum nitride composite. Comp Part A2002; 33: 289–292.
