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纳米流体因具有较好的传热性能而被认为是未来极具发展前景的强化传热工质,其粘度特性是研究纳米流体的关键。本文对四种常用水基纳米流体的粘度实验数据进行了统计分析,定量评估了纳米颗粒体积分数、温度与纳米颗粒尺寸三种因素对纳米流体粘度的影响规律。在此基础上,分类讨论了不同纳米流体粘度理论模型的局限性,综述了人工神经网络在纳米流体粘度预测建模中的应用现状。研究结果表明,纳米颗粒体积分数与温度是影响纳米流体粘度的重要因素,而纳米颗粒尺寸对纳米流体粘度的影响特征至今尚未完全确定;此外,受纳米颗粒小尺寸特征、纳米流体制备工艺以及测试技术等诸多因素的影响,有关纳米流体粘度的理论建模与人工神经网络预测均还处于起步阶段,如何有效实现纳米流体粘度的建模预测将成为纳米流体未来发展的重要方向之一。
Nanofluids are considered to be promising heat transfer fluids because of their good heat transfer properties. The viscosity characteristics of nanofluids are the key to the study of nanofluids. In this paper, the experimental data of four commonly used water-based nanofluids were statistically analyzed, and the influence of the three factors on the viscosity of nanofluids was quantitatively evaluated. On this basis, the limitations of theoretical models of viscosities of different nanofluids are discussed in detail, and the application status of artificial neural networks in viscosity prediction modeling of nanofluids is reviewed. The results show that the volume fraction of nanoparticles and temperature are important factors that affect the viscosity of nanofluids. The influence of nanoparticle size on the viscosity of nanofluids has not yet been completely determined. In addition, due to the small size of nanostructures, nanofluid preparation and testing Technology and many other factors, the theoretical modeling of nanofluid viscosity and artificial neural network prediction are still in its infancy. How to effectively predict the viscosity of nanofluids will become one of the important directions for the future development of nanofluids.