科研之友
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摘要
Pool boiling is an effective means of heat transfer which can handle high fluxes in boiling while maintaining minimum temperature differences over the heated surface. Addition of nanoparticles and surfactants to the base fluid are potential methods for enhancing the pool boiling heat transfer coefficient which can be useful in thermal management of complex and high performance electronic systems. This communication provides a Radial Basis RBF) model to predict the behavior of a surfactant and three different types of nanoparticles on pool boiling heat transfer of water over a horizontal rod heater. In this model, the inputs include experimental condition parameters of heat flux, type of water (distilled or treated water), and concentration of SDS (Sodium Dodecyl Sulfate), nano-alumina, nano-silica with two different particle sizes, and nano-Zinc Oxide. Genetic algorithm is used for determination of the optimum values. Statistical error analysis with different parameters is also implemented for validation of the predictive model. The model is based on three sets of experiments. The effects of two different types of water, SOS and/or nano-alumina (a), the effects of two particle sizes of nano-silica (b), and the effect of combination of nano-Zinc oxide and SOS (c) on pool boiling heat transfer of water as based fluid. In all above sets of tests, the effects of the nanoparticles and SDS coated on the surface of the heater were investigated by repeating the experiments after cleaning all parts of the device except its heater surface. Furthermore, Surface Tension and Dynamic Viscosity, the two crucial thermo-physical characteristics of fluids, were determined for justifying the behavior of the materials used in the experiments. The results showed that the treated water has a better boiling performance in comparison to distilled water; the presence of SDS, Alumina, and the combination of Zinc oxide and SDS enhance the boiling performance of the base fluid; both kinds of nano-silica deteriorates the pool boiling heat coefficient of water and the smaller one has more reduction effect; the combination of the Zinc Oxide and SOS is the best coolant among the aforementioned ones. Finally, the experimental results are modeled using radial basis function neural networks and the outputs of the model are in great agreement with the experimental data.
- 出版日期2016-1
- 单位中国石油大学（北京）
