الفهرس 
INTRODUCTION & LITERATURE REVIEWOnly 14 pages are availabe for public view
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Abstract
Heat transfer and pressure DROP characteristics of three types of nanofluids through helical coil tubes under isothermal condition are investigated experimentally and using artificial neural networks. To carry out the experiments, three types of nanofluids TiO2/water, ZnO/water and Ag/water were prepared with nanoparticles volume concentration of 0.25 % and tested in helical coil. Nanoparticles TiO2, ZnO and Ag were prepared and were characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) and zeta sizing test was performed for nanofluids. Five coils are constructed with different geometries and operating conditions. The results showed that increasing coil pitch reduce both Nusselt number and pressure drop. Average Nusselt number enhancement up to 28.7 %, 17.8 % and 11.8 % was achieved at TiO2/water, ZnO/water and Ag/water respectively with average pressure DROP increasing up to 28.5%, 23.5% and 25.4% respectively. The optimum enhancement was achieved at 2 cm coil with enhancement up 41.8 %, 21.8 % and 13.4 % at TiO2/water, ZnO/water and Ag/water respectively but on the other hand the optimum pressure DROP increasing was also observed up to 39.9%, 30.8% and 34.1% at TiO2/water, ZnO/water and Ag/water respectively. Hydrothermal performance index (HTPI) was estimated. The results showed that increase of the coil pitch decrease HTPI and utilizing nanofluids increase HTPI for the three types of nanofluids TiO2/water, ZnO/water and Ag/water. Via the experimental data, correlations were developed to predict Nusselt number and pressure DROP for each type of nanofluid. Artificial neural networks (ANNs) based on feed forward neural network (FFNN) and generalized regression neural network (GRNN), were utilized to predict Nusselt number and pressure drop. Both of ANNs showed a very good accuracy in predicting average Nusselt number and pressure drop.