纳米流体粘度的实验测量及预测建模研究进展

发布时间：2019-05-24 03:21

【摘要】：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。
[Abstract]:Because of its good heat transfer performance, nanofluid is considered to be a promising heat transfer enhancement working fluid in the future, and its viscosity characteristics are the key to the study of nanofluid. In this paper, the experimental data of viscosity of four kinds of water-based nanofluids are statistically analyzed, and the effects of volume fraction of nanoparticles, temperature and size of nanoparticles on the viscosity of nanofluids are quantitatively evaluated. On this basis, the limitations of different theoretical models of nanofluid viscosity are classified and discussed, and the application of artificial neural network in the prediction and modeling of nanofluid viscosity is reviewed. The results show that the volume fraction and temperature of nanoparticles are the important factors affecting the viscosity of nanofluids, but the characteristics of the effects of nanoparticles size on the viscosity of nanofluids have not yet been completely determined. In addition, due to the small size characteristics of nanoparticles, nanofluid preparation technology, testing technology and many other factors, the theoretical modeling of nanofluid viscosity and artificial neural network prediction are still in their infancy. How to effectively realize the modeling and prediction of nanofluid viscosity will become one of the important directions in the future development of nanofluid.
【作者单位】：中国人民解放军海军驻哈尔滨703所军事代表室;哈尔滨工程大学动力与能源工程学院;
【基金】：中央高校基本科研业务费专项基金资助项目(HEUCF160307)
【分类号】：TB302

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