• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Heat and Mass Transfer >Effects on thermophysical properties of carbon based nanofluids: Experimental data, modelling using regression, ANFIS and ANN
【24h】

Effects on thermophysical properties of carbon based nanofluids: Experimental data, modelling using regression, ANFIS and ANN

机译：对碳基纳米流体热物理性质的影响：实验数据，使用回归，ANFIS和ANN建模

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Viscosity, density and thermal conductivity of Diamond-COOH and MWCNT-COOH nanoparticles dispersed in water was studied without adding any surfactants or additives for a range of 20 °C < T < 50 °C and 0.0 < φ < 0.2 vol%. Accordingly, based on the experimental data, a new correlation was introduced that predicts the nanofluids’ relative thermophysical properties. Besides the non-linear regression for minimum prediction error, an adaptive neuro-fuzzy inference system (ANFIS) and optimal artificial neural network (ANN) were developed. The model was fed by 120 experimental data. 70% of data points were included in the dataset training set and 30% were used as test set. The results of different theoretical models, predicted results and experimental data were compared together.The root-mean-square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the results. The models explored the influence of material type, nanoparticle concentration and temperature on the thermophysical properties of nanofluids. As the results show the majority of theoretical models define the thermophysical properties accurately, if correct values of base fluid properties are fed to them. Yet, the current soft-computing methods show less error in comparison to the existing correlations. The ANN is recommended for future studies, as it provides the best fits to the experimental data.
机译：研究了在20°C

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号