Flow and heat transfer characteristics of water- MnZnFe2O4 magnetic nanofluid through an annulus were evaluated under the effect of non-uniform magnetic field using the two-phase Euler–Lagrange method. The effects of concentration, size of particles and magnitude of magnetic field gradient were investigated. The concentration distribution was found to be non-uniform, with its value lower near the walls. Velocity profile becomes flatter at the cross section of the annulus by applying the magnetic field. Increasing particle size, concentration and magnitude of the magnetic field gradient enhance the convective heat transfer coefficient. The effect of increasing magnitude of the magnetic field gradient on heat transfer and pressure drop is more significant for larger particles. Models of convective heat transfer coefficient and pressure drop were obtained in terms of the effective parameters using neural network. Meanwhile, optimization was implemented via genetic algorithm coupled with compromise programming technique in order to achieve the conditions with maximum heat transfer and minimum pressure drop. Based on the results obtained from optimization, application of the magnetic field is only recommended when heat transfer is considered to be more important than pressure drop.
کلید واژگان :Magnetic nanofluid, Mn–Zn ferrite, Heat transfer, Compromise programming, Neural network
ارزش ریالی : 600000 ریال
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