The one-dimensional, cylindrical coordinate, non-linear partial differential equation of transient heat conduction through a hollow cylindrical thermal insulation material of a thermal conductivity temperature dependent property proposed by an available empirical
function is solved analytically using Kirchhoff’s transformation. It is assumed that this insulating material is initially at a uniform temperature. Then, it is suddenly subjected at its inner radius with a step change in temperature. Four thermal insulation materials were selected. An identical analytical solution was achieved when comparing the results of temperature distribution with available analytical solution for the same four case studies that assume a constant thermal conductivity. It is found that the characteristics of the
thermal insulation material and the pressure value between its particles have a major effect on the rate of heat transfer and temperature profile.
This research explores the integration of multi-walled carbon nanotubes (MWCNTs) into polyvinyl alcohol (PVA) matrices to enhance their structural, electrical and thermal properties. Fourier-transform infrared spectroscopy analysis confirms the formation of a PVA-MWCNT complex. The incorporation of MWCNTs, even at low concentrations, leads to increased electrical conductivity, making the composite material suitable for applications in flexible electronics and sensors. Dielectric studies reveal frequency-dependent electrical behavior, attributed to interfacial polarization, suggesting potential uses in capacitors and energy storage devices. The introduction of MWCNTs into PVA leads to N-type semiconductor behavior, as indicated by the Hall c
... Show MoreIn this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
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