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In this research, the influences of quadratic Boussinesq approximation and quadratic thermal radiation on the heat transfer analysis of magnetized Sisko nanofluid flow with Cattaneo–Christov heat flux through stretching surfaces are studied. The formulated mathematical model is solved by the finite difference technique, and heat transfer rate and skin friction coefficients are computed for acting parameters, i.e., magnetic field, Eckert number, Forchheimer parameter, thermal relaxation parameter, radiation parameter, porosity parameter and Biot number. For sensitivity analysis, the response surface method (RSM) with a face-centered central composite design is utilized. The RSM is elucidated by applying nonlinear regression, analysis of variance and goodness of fit. The results indicate that the friction coefficient and Nusselt number have positive sensitivities for the Forchheimer parameter. The heat transfer rate decreases with an increase in magnetic field, Biot number and thermal relaxation parameter values for shear thickening (n>1) and shear thinning (n<1). Further for n<1, a one unit increase in A1 leads to a 33% drop in SFC and 48% in LNN; and an increase of 8 units in Fr leads to a 67.18% rise in LNN.
In the present study, the effect of particle concentration, particle diameter and temperature on the thermal conductivity and viscosity of Al2O3/water nanofluids was investigated experimentally using design of experiment approach (full factorial design). Variables were selected at two levels each: particle concentration (0.1–1%), particle diameter (20–40nm) and temperature (10–40∘C). It was observed that the thermal conductivity of the Al2O3/water nanofluids increases with increasing concentration and temperature and decreases with increase in particle diameter, while viscosity increases with increasing particle diameter. Results showed that the interaction effect of concentration and temperature also has significant effect on the thermal conductivity of Al2O3/water nanofluids. For viscosity, the interaction of particle diameter and temperature was important. Utility concept was used to optimize the properties collectively for better heat transfer performance. The optimal combination for high thermal conductivity and low viscosity was obtained at higher level of particle concentration (1%), lower level of particle diameter (20nm) and higher level of temperature (40∘C). At this condition the increment in thermal conductivity and viscosity compared to base fluid was 11.51% and 6.37%, respectively.
Heat transfer coefficient is a key parameter for efficiency evaluation of heat exchangers. Good stability and high heat transfer coefficient are essential for the application of nanofluids in heat exchangers and solar systems. In this work, nanofluids with good stability were prepared, and the influence of vertical magnetic field on flow and heat exchange of magnetic nanofluids under laminar and turbulent conditions was mainly studied. The flow and heat transfer rules of Fe3O4 nanofluids with or without magnetic field conditions, magnetic field strength, magnetic field distribution, the nanoparticle concentration and nanofluids temperature were systematically studied by setting up an experimental platform. The results show that the intensity and distribution of magnetic field had a significant influence on the heat transfer of magnetic nanofluids, whether in laminar or turbulent flow. When the magnetic field strength is 800G and 1000G, the convective heat transfer coefficient increases by an average of 23.89% and 26.12%. However, the influence of magnetic field on its flow characteristics is not obvious, and the effect on resistance coefficient increases by only 2.01%. In addition, the characteristics of magnetic nanofluids also have a certain influence on its flow and heat transfer. When the temperature of magnetic nanofluids is increased, the convective heat transfer coefficient will increase. When the concentration of magnetic nanofluids is increased, the pressure drop will also increase, but it has little effect on the drag coefficient.