Hydriding and dehydriding properties of MgH_(2)-x wt.%NiMn_(9.3)Al_(4.0)Co_(14.1)Fe_(3.6)(x=10,25,50)nanocomposites have been investigated in present work.Doped alloy was prepared by arc melting method and ball milled...Hydriding and dehydriding properties of MgH_(2)-x wt.%NiMn_(9.3)Al_(4.0)Co_(14.1)Fe_(3.6)(x=10,25,50)nanocomposites have been investigated in present work.Doped alloy was prepared by arc melting method and ball milled with MgH_(2)to get nanocomposites.Onset temperature as low as 180℃was observed for MgH_(2)-50 wt%system which is 80℃lower than the as-milled MgH_(2)giving 131.34 KJ/mol activation energy.Structural analysis shows tetragonal,orthorhombic and monoclinic phases for MgH_(2),Al_(60)Mn_(11)Ni_(4)and Mg_(2)NiH_(4).Morphology by SEM were undertaken to investigate the effect of hydrogenation on nanostructured alloy.DSC studies show a single broad exothermic peak in the temperature range 48℃-353℃after alloy addition in MgH_(2).These results indicate that the hydrogenation properties of MgH_(2)nanocomposite have been improved compared to the as-milled MgH_(2).展开更多
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 contributio...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.展开更多
The present paper deals with the effect of geometry of filler particles on the effective thermal conductivity for polymer composites. In the earlier models, less emphasis has been given on the shape of filler particle...The present paper deals with the effect of geometry of filler particles on the effective thermal conductivity for polymer composites. In the earlier models, less emphasis has been given on the shape of filler particles. In this paper, expressions for effective thermal conductivity has been derived using the law of minimal thermal resistance and equal law of the specific equivalent thermal conductivity for three different shapes i.e. spherical, elliptical and hexagonal of filler particles respectively. Calculated values of effective thermal conductivity for various samples using the derived expressions then compared with experimental data available and other models developed in the literature. The results calculated are in good agreement with the earlier experimental data and the deviation, is least in our expressions showing the success of the model.展开更多
In this paper, non-linear dependence of volume fraction of inclusions on the effective thermal conductivity of composite materials is investigated. Proposed approximation formula is based on the Maxwell’s equation, i...In this paper, non-linear dependence of volume fraction of inclusions on the effective thermal conductivity of composite materials is investigated. Proposed approximation formula is based on the Maxwell’s equation, in that a non-linear term dependent on the volume fraction of the inclusions and the ratio of the thermal conductivities of the polymer continuum and inclusions is introduced in place of the volume fraction of inclusions. The modified Maxwell’s equation is used to calculate effective thermal conductivity of several composite materials and agreed well with the earlier experimental results. A comparison of the proposed relation with different models has also been made.展开更多
In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid ...In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.展开更多
基金Priyanka Meena is thankful to Malaviya National Institute of Technology(MNIT),Jaipur for providing Institute Assis-tant fellowship for PhD work.
文摘Hydriding and dehydriding properties of MgH_(2)-x wt.%NiMn_(9.3)Al_(4.0)Co_(14.1)Fe_(3.6)(x=10,25,50)nanocomposites have been investigated in present work.Doped alloy was prepared by arc melting method and ball milled with MgH_(2)to get nanocomposites.Onset temperature as low as 180℃was observed for MgH_(2)-50 wt%system which is 80℃lower than the as-milled MgH_(2)giving 131.34 KJ/mol activation energy.Structural analysis shows tetragonal,orthorhombic and monoclinic phases for MgH_(2),Al_(60)Mn_(11)Ni_(4)and Mg_(2)NiH_(4).Morphology by SEM were undertaken to investigate the effect of hydrogenation on nanostructured alloy.DSC studies show a single broad exothermic peak in the temperature range 48℃-353℃after alloy addition in MgH_(2).These results indicate that the hydrogenation properties of MgH_(2)nanocomposite have been improved compared to the as-milled MgH_(2).
文摘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.
文摘The present paper deals with the effect of geometry of filler particles on the effective thermal conductivity for polymer composites. In the earlier models, less emphasis has been given on the shape of filler particles. In this paper, expressions for effective thermal conductivity has been derived using the law of minimal thermal resistance and equal law of the specific equivalent thermal conductivity for three different shapes i.e. spherical, elliptical and hexagonal of filler particles respectively. Calculated values of effective thermal conductivity for various samples using the derived expressions then compared with experimental data available and other models developed in the literature. The results calculated are in good agreement with the earlier experimental data and the deviation, is least in our expressions showing the success of the model.
文摘In this paper, non-linear dependence of volume fraction of inclusions on the effective thermal conductivity of composite materials is investigated. Proposed approximation formula is based on the Maxwell’s equation, in that a non-linear term dependent on the volume fraction of the inclusions and the ratio of the thermal conductivities of the polymer continuum and inclusions is introduced in place of the volume fraction of inclusions. The modified Maxwell’s equation is used to calculate effective thermal conductivity of several composite materials and agreed well with the earlier experimental results. A comparison of the proposed relation with different models has also been made.
文摘In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.
