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Stability, thermal performance and artificial neural network modeling of viscosity and thermal conductivity of Al2O3-ethylene glycol nanofluids

机译:Al2O3-乙二醇纳米流体粘度和导热系数的稳定性,热性能和人工神经网络建模

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摘要

The aim is to estimate the stability of Al2O3-ethylene glycol (EG) nanofluids using the particle size distribution and velocity ratio. The thermal conductivity and viscosityweremeasured under ultrasonic conditions for various time intervals, mass fraction (from 0 to 2.0 wt%), and temperature range (from 25 to 60 degrees C). Moreover, various criteria were presented to estimate the thermal performance in the convective heat transfer. Based on different sets of experimental data, new correlations and optimal artificial neural network models (ANN) were proposed. The results showed that Al2O3-EG nanofluids obtained by ultrasonation for 60 min exhibits the most encouraging properties. Moreover, the correlations for the experiment and ANN models can predict these two parameters. However, the ANN model ismore precise. It is expected that the results to be useful for other studies of nanofluids stability especially since it recommends suitable selecting criteria based on heat transfer behavior before real applications. (c) 2020 Elsevier B.V. All rights reserved.
机译:目的是估计使用粒度分布和速度比来估计Al 2 O 3-乙二醇(例如)纳米流体的稳定性。在超声波条件下的热导电和粘度偏离各种时间间隔,质量分数(0至2.0wt%)和温度范围(25至60℃)。此外,提出了各种标准以估计对流热传递中的热性能。基于不同的实验数据集,提出了新的相关性和最佳的人工神经网络模型(ANN)。结果表明,通过超声检测60分钟获得的Al 2 O 3 - 例如纳米流体表现出最令人鼓舞的特性。此外,实验和ANN模型的相关性可以预测这两个参数。但是,ANN模型是莫尔莫尔精确。 It is expected that the results to be useful for other studies of nanofluids stability especially since it recommends suitable selecting criteria based on heat transfer behavior before real applications. (c)2020 Elsevier B.V.保留所有权利。

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