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Heat transfer and entropy generation of turbulent forced convection flow of nanofluids in a heated pipe

机译:受热管中纳米流体湍流强迫对流的传热和熵产生

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Eulerian-Eulerian multi-phase mixture model is applied to numerically analyse the turbulent flow and heat transfer behaviour of water based Al_2O_3 and TiO_2 nanofluids in a pipe. The main goal of the present work is to investigate the effects of volume concentrations, Brownian motion and size diameter of nanoparticles on the flow and heat transfer. Analysis of entropy generation is presented in order to investigate the condition that optimises the thermal system. Results reveal that small diameter of nanoparticles with their Brownian motion has the highest heat transfer rate as well as thermal performance factor for x = 6%. Above all, the higher heat transfer rate is found while using the multi-phase model than the single-phase model (Saha and Paul [1]). Also, the optimal Reynolds number is found to be Re = 60 × 10~3 for x = 6% and d_p = 10 nm, which minimises the total entropy generation. Finally, it is showed that TiO_2-water nanofluid is the most energy efficient coolant than Al_2O_3-water nanofluid, and some new correlations have been proposed for the calculation of average Nusselt number.
机译:应用欧拉-欧拉多相混合物模型对管中水基Al_2O_3和TiO_2纳米流体的湍流和传热行为进行数值分析。本工作的主要目的是研究纳米颗粒的体积浓度,布朗运动和粒径对流动和传热的影响。为了研究优化热系统的条件,提出了熵产生的分析。结果表明,具有布朗运动的小直径纳米颗粒具有最高的传热速率以及x = 6%的热性能因子。最重要的是,使用多相模型比单相模型具有更高的传热率(Saha和Paul [1])。另外,对于x = 6%和d_p = 10 nm,最佳雷诺数被发现为Re = 60×10〜3,这使总的熵产生最小化。最后表明,TiO_2-水纳米流体是比Al_2O_3-水纳米流体最节能的冷却剂,并为计算平均Nusselt数提出了一些新的相关性。

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