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Two semi-empirical approaches for prediction of elastic modulus of biphasic composites have been proposed. Developed relations are for pore free matrix and pore free filler and found to depend on nonlinear contribution of volume fraction of constituents as well as ratio of elastic properties of individual phases. These relations are applied for the calculation of effective elastic modulus mainly for Al2O3-NiAl, SiC-Al, Alumina-Zirconia, Al-Al2O3, W-glass and Flax-Resin composite materials. Theoretical predictions using developed relations are compared with experimental data. It is found that the predicted values of effective elastic modulus using modified relations are quite close to the experimental results

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In this work, the thermal conductivity of Al 2 O 3 nanofluids has been investigated for the sensitivity towards neutron irradiation. The solution combustion method has been used for the synthesis of Al 2 O 3 nanoparticles that have been used for the preparation of the nanofluids. Prepared nanofluids have been neutron-irradiated for 7 and 14 days. Dynamic Light Scattering, Scanning Electron Microscopy, and Ultraviolet-Visible Spectroscopy have been used to ascertain the change in properties before and after neutron-irradiation. Thermal conductivity has been measured for un-irradiated and neutron-irradiated nanofluids at 30°C using a KD2 pro thermal properties analyzer. The decrease in thermal conductivity has been observed after neutron-irradiation that further decreases with increased duration of exposure and concentration of nanoparticles. 5 and 10% decrease in thermal conductivity has been recorded after 7 and 14 days of neutron irradiation for change in concentration from 0 to 2 volume percent. Neutronirradiation sensitivity analysis revealed that heat transfer characteristics are sensitive at higher concentrations and during initial exposure of neutron-irradiations.

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Two-phase samples were prepared by mixing Fe, Cu, and Al particles (<50 mm) in lithium multipurpose grease with different weight fractions of Fe, Cu, and Al powders. Effective thermal conductivity of these samples has been measured by a laboratory-made thermal conductivity probe as a function of weight fraction of filled metal particles. Grease-Fe, Grease-Cu, and Grease-Al systems showed maximum thermal conductivity enhancement of 35.28%, 72.28%, and 97.40% at weight fraction of 0.3, 0.4, and 0.4 of Fe, Cu, and Al particles, respectively. An artificial neural network approach is used to model the effective thermal conductivity of these samples with three input parameters, viz. thermal conductivity of grease, thermal conductivity of metal particles, and weight fraction of metal particles, respectively. A theoretical prediction was also done using a model developed by Verma et al. Results obtained were compared based on coefficient of determination and mean absolute error between experimental and predicted values of effective thermal conductivity by artificial neural network and theoretical formulation. It is found that artificial neural network approach showed better agreement with the experimental results.