纳米金刚石/氧化铜离子流体的热物理性质和性能分析:实验和人工神经网络预测

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摘要

将纳米金刚石/氧化铜(ND/CuO)纳米粒子分散到 1-乙基-3-甲基氯化咪唑[EMiM]Cl 中,制成离子流体。实验分析了颗粒体积负载从 0.2 % 到 1.0 %、温度从 30 ℃ 到 60 ℃ 时的热物理性质。通过理论方法了解了制备的离子液体在管中和螺旋线圈中流动时的性能系数(FoM),并对热物理性质进行了评估。与基础流体相比,0.2% 体积的离子流体的热传导率提高了 14.27%,1.0% 体积的离子流体的热传导率提高了 35.20%,温度为 60 °C。此外,在温度为 30 °C 时,1.0% 体积的离子流体的粘度比基础流体提高了 61.66%。其他特性如密度随体积负荷的增加而增加,比热随体积负荷的增加而降低。FoM 结果表明,当流体工作温度超过 35 ℃ 时,所有制备的离子液体在管中流动的体积浓度都有利于流体;另一方面,当工作温度高于 30 ℃ 时,制备的离子液体在螺旋线圈中流动的体积浓度为 0.8 % 和 1.0 % 时,都有利于流体。估计的热物理性质也被用作 Levenberg-Marquardt 算法人工神经网络的输入和输出数据。从 LM 算法的结果可以看出,预测数据与实验数据非常吻合。在所有数据中,热导率的相关系数(R2)为 0.99283,粘度为 0.9971,密度为 0.99887,比热为 0.99817。
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Thermophysical properties, and figures-of-merit analyses of nanodiamond/CuO ionanofluids: Experimental and artificial neural network predictions
The nanodiamond/copper oxide (ND/CuO) nanoparticles were dispersed into 1-ethyl-3-methylimidazolium chloride [EMiM]Cl to create ionanofluids. The experiments were performed to analyse the thermophysical properties under particle volume loadings from 0.2 % to 1.0 %, and temperature ranging from 30 to 60 °C. Theoretical approach was made to understand the Figures-of-Merit (FoM) of the prepared ionanofluids, while they flow in a tube, and helical coil through the evaluated thermophysical properties. The lower thermal conductivity augment of 14.27 % was noticed at 0.2% vol. and larger thermal conductivity enhancement of 35.20 % was noticed at 1.0% vol. and at a temperature of 60 °C, against the base fluid. Moreover, the viscosity of 1.0% vol. of ionanofluid was enhanced by 61.66 % at a temperature of 30 °C, over the base fluid. The other properties like density was increased with an increase of volume loadings, and specific heat was decreased with an increase of volume loadings. The FoM results indicated that, all the prepared volume concentrations of ionanofluids flow in a tube was benefited fluid when the fluid operating temperature was exceeds to 35 °C, on the other side, the prepared ionanofluids flow in a helical coil was benefited fluid when its operating temperature was above 30 °C for 0.8 %, and 1.0% vol. loadings. The estimated thermophysical properties were also used as input and output data for an artificial neural network of Levenberg-Marquardt (LM) algorithm. It is noticed from the LM algorithm results, the predicted data is well agreed with an experimental data. For all the data, the correlation coefficient (R2) of thermal conductivity is 0.99283, viscosity is 0.9971, density is 0.99887, and specific heat is 0.99817, was observed, respectively.
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