纳米流体粘度的实验测量及预测建模研究进展

A Review on the Experimental Measurement and Prediction Modeling of Nanofluids Viscosity

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 as one of the most promising cooling technologies due to its higher heat transfer performance,and the viscosity is the key characteristic of nanofluids. In this paper,the experimental viscosity data of four conventional water based nanofluids were statistically analyzed,and the effects of nanoparticle volume fraction,temperature and nanoparticle size on nanofluids viscosity were quantitatively assessed. Then the theoretical models of predicting nanofluids viscosity were summarized and their limitations were discussed. And the application of artificial neural networks to predict the viscosity of nanofluids was also reviewed. Results showed that nanoparticle volume fraction and temperature are the important influencing factors in the nanofluids viscosity,while the effect of nanoparticle size is still not conclusive. Additionally,due to the effects of small size characteristics of nanoparticles,preparation process of nanofluids and measuring technique of thermophysical property,the nanofluids viscosity prediction models,either theory based or artificial neural networks based,are still in the easy stage of development,and how to effectively establish nanofluids viscosity prediction model is one of important branches for nanofluids development.