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A Neural Regression Model for Predicting Thermal Conductivity of CNT Nanofluids with Multiple Base Fluids

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Abstract

High thermal conductivity of carbon nanotube nanofluids (knf) has received great attention. However, the current researches are limited by experimental conditions and lack a comprehensive understanding of knf variation law. In view of proposition of data-driven methods in recent years, using experimental data to drive prediction is an effective way to obtain knf, which could clarify variation law of knf and thus greatly save experimental and time costs. This work proposed a neural regression model for predicting knf. It took into account four influencing factors, including carbon nanotube diameter, volume fraction, temperature and base fluid thermal conductivity (kf). Where, four conventional fluids with kf, including R113, water, ethylene glycol and ethylene glycol-water mixed liquid were considered as base fluid considers. By training this model, it can predict knf with different factors. Also, change law of four influencing factors considered on the knf enhancement has discussed and the correlation between different influencing factors and knf enhancement is presented. Finally, compared with nine common machine learning methods, the proposed neural regression model shown the highest accuracy among these.

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References

  1. Zhang X., Song L., Cai L., et al., Optical visualization and polarized light absorption of the single-wall carbon nanotube to verify intrinsic thermal applications. Light-Science & Applications, 2008, 4(8): e318.

    Article  Google Scholar 

  2. Keblinski P., Phillpot S.R., Choi S.U.S., et al., Mechanisms of heat flow in suspensions of nano-sized particles (nanofluids). International Journal of Heat and Mass Transfer, 2002, 45(4): 855–863.

    Article  Google Scholar 

  3. Eastman J.A., Phillpot S.R., Choi S.U.S., et al., Thermal transport in nanofluids. Annual Review of Materials Research, 2004, 34(1): 219–246.

    Article  ADS  Google Scholar 

  4. Qiu L., Zhu N., Feng Y., et al., A review of recent advances in thermophysical properties at the nanoscale: From solid state to colloids. Physics Reports, 2020, 843: 1–81.

    Article  ADS  Google Scholar 

  5. Longon G.A., Zilio C., Ceseracciu E., et al., Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide-water nanofluids. Nano Energy, 2012, 1(2): 290–296.

    Article  Google Scholar 

  6. Aminian A., Predicting the effective thermal conductivity of nanofluids for intensification of heat transfer using artificial neural network. Powder Technology, 2016, 301: 288–309.

    Article  Google Scholar 

  7. Hemmat Esfe M., Motahari K., Sanatizadeh E., et al., Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation. International Communications in Heat and Mass Transfer, 2016, 76: 376–381.

    Article  Google Scholar 

  8. Yousefi F., Mohammadiyan S., Karimi H., Application of artificial neural network and PCA to predict the thermal conductivities of nanofluids. Heat and Mass Transfer, 2016, 52(10): 2141–2154.

    Article  ADS  Google Scholar 

  9. Shahsavar A., Bahiraei M., Experimental investigation and modeling of thermal conductivity and viscosity for non-Newtonian hybrid nanofluid containing coated CNT/Fe3O4 nanoparticles. Powder Technology, 2017, 318: 441–450.

    Article  Google Scholar 

  10. Esfahani J.A., Safaei M.R., Goharimanesh M., et al., Comparison of experimental data, modelling and non-linear regression on transport properties of mineral oil based nanofluids. Powder Technology, 2017, 317: 458–470.

    Article  Google Scholar 

  11. Bagherzadeh S.A., D’Orazio A., Karimipour A., et al., A novel sensitivity analysis model of EANN for F-MWCNTs-FeO/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs. Physica A: Statistical Mechanics and its Applications, 2019, 521: 406–415.

    Article  ADS  Google Scholar 

  12. Aisyah S., Harahap M., Siregar A.M.H., et al., Optimization of training backpropagation algorithm using nguyen widrow for angina ludwig diagnosis. Journal of Physics Conference Series, 2018, 1007: 012050.

    Article  Google Scholar 

  13. Wilamowski B.M., Yu H., Improved computation for Levenberg-Marquardt Ttraining. IEEE Transactions on Neural Networks, 2010, 21(6): 930–937.

    Article  Google Scholar 

  14. Zhang L., Suganthan P.N., A survey of randomized algorithms for training neural networks. Information Sciences, 2016, 364–365: 146–155.

    Article  Google Scholar 

  15. Kayri M., Predictive abilities of Bayesian regularization and Levenberg-Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Mathematical and Computational Applications, 2016, 21(2): 20.

    Article  MathSciNet  Google Scholar 

  16. Jiang W., Ding G., Peng H., Measurement and model on thermal conductivities of carbon nanotube nanorefrigerants. International Journal of Thermal Sciences, 2009, 48(6): 1108–1115.

    Article  Google Scholar 

  17. Soltanimehr M., Afrand M., Thermal conductivity enhancement of COOH-functionalized MWCNTs/ethylene glycol-water nanofluid for application in heating and cooling systems. Applied Thermal Engineering, 2016, 105: 716–723.

    Article  Google Scholar 

  18. Xing M., Yu J., Wang R., Experimental study on the thermal conductivity enhancement of water based nanofluids using different types of carbon nanotubes. International Journal of Heat and Mass Transfer, 2015, 88: 609–616.

    Article  Google Scholar 

  19. Shamaeil M., Firouzi M., Fakhar A., The effects of temperature and volume fraction on the thermal conductivity of functionalized DWCNTs/ethylene glycol nanofluid. Journal of Thermal Analysis and Calorimetry, 2016, 126(3): 1455–1462.

    Article  Google Scholar 

  20. Jha N., Ramaprabhu S., Thermal conductivity studies of metal dispersed multiwalled carbon nanotubes in water and ethylene glycol based nanofluids. Journal of Applied Physics, 2009, 106(8): 084317.

    Article  ADS  Google Scholar 

  21. Amrollahil A., Hamidi A.A., Rashidi A.M., The effects of temperature, volume fraction and vibration time on the thermo-physical properties of a carbon nanotube suspension (carbon nanofluid). Nanotechnology, 2008, 19(31): 315701.

    Article  Google Scholar 

  22. Glory J., Bonetti M., Helezen M., et al., Thermal and electrical conductivities of water-based nanofluids prepared with long multiwalled carbon nanotubes. Journal of Applied Physics, 2008, 103(9): 094309.

    Article  ADS  Google Scholar 

  23. Hemmat Esfe M., Saedodin S., Mahian O., et al., Heat transfer characteristics and pressure drop of COOH-functionalized DWCNTs/water nanofluid in turbulent flow at low concentrations. International Journal of Heat and Mass Transfer, 2014, 73: 186–194.

    Article  Google Scholar 

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Acknowledgements

This study is financially supported by Beijing Nova Program (No. Z201100006820065), National Natural Science Foundation of China (No. 51876007 and No. 51876008), Beijing Natural Science Foundation (No. 3202020) and Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities, No. RF-IDRY-19-004).

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Authors and Affiliations

  1. School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Hanying Zou, Yanhui Feng, Lin Qiu & Xinxin Zhang

  2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Cheng Chen, Muxi Zha, Kangneng Zhou, Ruoxiu Xiao & Zhiliang Wang

Authors
  1. Hanying Zou
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  2. Cheng Chen
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  3. Muxi Zha
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  4. Kangneng Zhou
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  5. Ruoxiu Xiao
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  6. Yanhui Feng
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  7. Lin Qiu
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  8. Xinxin Zhang
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  9. Zhiliang Wang
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Corresponding authors

Correspondence to Yanhui Feng or Lin Qiu.

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Zou, H., Chen, C., Zha, M. et al. A Neural Regression Model for Predicting Thermal Conductivity of CNT Nanofluids with Multiple Base Fluids. J. Therm. Sci. 30, 1908–1916 (2021). https://doi.org/10.1007/s11630-021-1497-1

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  • DOI: https://doi.org/10.1007/s11630-021-1497-1

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