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Thermal conductivity of non-Newtonian nanofluids: Experimental data and modeling using neural network

机译:非牛顿纳米流体的导热系数:使用神经网络的实验数据和建模

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Three different types of nanofluids were prepared by dispersing γ-Al_2O_3, TiO_2 and CuO nanoparticles in a 0.5 wt% of carboxymethyl cellulose (CMC) aqueous solution. Thermal conductivity of the base fluid and nanofluids with various nanoparticle loadings at different temperatures were measured experimentally. Results show that the thermal conductivity of nanofluids is higher than the one of the base fluid and the increase in the thermal conductivity varies exponentially with the nanoparticle concentration. In addition to increase with the nanoparticle concentration, the thermal conductivity of nanofluids increases with the temperature. Neural network models were proposed to represent the thermal conductivity as a function of the temperature, nanoparticle concentration and the thermal conductivity of the nanoparticles. These models were in good agreement with the experimental data. On the other hand, the Hamilton Crosser model was only satisfactory for low nanoparticle concentrations.
机译:通过将γ-Al_2O_3,TiO_2和CuO纳米颗粒分散在0.5 wt%的羧甲基纤维素(CMC)水溶液中,制备了三种不同类型的纳米流体。实验测量了在不同温度下具有各种纳米颗粒负载的基础流体和纳米流体的热导率。结果表明,纳米流体的热导率高于基础流体之一,并且热导率的增加随纳米颗粒浓度呈指数变化。除了随着纳米颗粒浓度的增加而增加之外,纳米流体的热导率也随着温度而增加。提出了神经网络模型来表示热导率与温度,纳米粒子浓度和纳米粒子的热导率的关系。这些模型与实验数据非常吻合。另一方面,Hamilton Crosser模型仅对低纳米颗粒浓度令人满意。

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