Skip to main content

Advertisement

SpringerLink
Log in

Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

  • Original
  • Published:
Heat and Mass Transfer Aims and scope Submit manuscript

Abstract

The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20–70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction(v/v%) 0.05.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Singh PK, Anoop KB, Sundararajan T, Das S (2010) Entropy generation due to flow and heat transfer in nanofluids. Int J Heat Mass Transf 53:4757–4767

    Article  MATH  Google Scholar 

  2. Karami M, Shirani E, Avara A (2012) Analysis of entropy generation, pumping power, and tube wall temperature in aqueous suspensions of alumina particles. Heat Transfer Res 43(4):327–342

    Article  Google Scholar 

  3. Oztop FH, Abu-Nada E (2008) Numerical study of natural convection in partially heated rectangular enclosures filled with nanofluids. Int J Heat Fluid 29:1326–1336

    Article  Google Scholar 

  4. Bhuva BV, Soni S (2015) Experimental investigation of Exergy & energy analysis of double pipe heat exchanger using twisted tape. Int J Sci Res Dev| 3(4):2321–0613

    Google Scholar 

  5. Maddah H, Ghasemi N (2017) Experimental evaluation of heat transfer efficiency of nanofluid in a double pipe heat exchanger and prediction of experimental results using artificial neural networks. Heat Mass Transf:1–14. https://doi.org/10.1007/s00231-017-2068-6

  6. Aghayari R, Maddah H, Baghbani Arani B, Mohammadiun H, Nikpanje H (2015) An experimental investigation of heat transfer of Fe2O3/water nanofluid in a double pipe heat exchanger. Int J Nano Dimensions 6(5):517–524

    Google Scholar 

  7. Hosseinian Naeini A, Baghbani Arani J, Narooei A, Aghayari R, Maddah H (2016) Nanofluid thermal conductivity prediction model based on artificial neural network. Trans Phenom Nano Micro Scales 4(2):41–46

    Google Scholar 

  8. Naphon P (2011) Study on the Exergy loss of the horizontal concentric micro-fin tube heat exchangers. Int Commun Heat Mass Transfer 38:229–235

    Article  Google Scholar 

  9. Yilmaz M, Sara ON, Karsli S (2001) Performance evaluation criteria for heat exchangers based on second law analysis. Exergy Int J 1(4):278–294

    Article  Google Scholar 

  10. Naphon P (2006) Second law analysis of the heat transfer of the horizontal concentric tube heat exchanger. Int Commun Heat Mass Transfer 1029–1041

  11. Ningbo Z, Li S, Zhitao W, Yunpeng C (2014) Prediction of viscosity of nanofluids using artificial neural networks. Heat Transfer and Thermal Engineering. https://doi.org/10.1115/IMECE2014-40354

  12. Li P, Xie Y, Zhang D, Xie G (2016) Heat transfer enhancement and entropy generation of nanofluids laminar convection in microchannels with flow control devices. Entropy 18(134):1–15

    Google Scholar 

  13. Yu J, Zhang H-C, Xie Y, Shi L (2017) Entropy generation analysis and performance evaluation of turbulent forced convective heat transfer to nanofluids. Entropy 19(108):1–18

    MathSciNet  Google Scholar 

  14. Myers R (1976) Response Surface Methodology. Edwards Brothers, Ann Arbor, MI

    Google Scholar 

  15. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River

    MATH  Google Scholar 

  16. Xu P, Xu S, Yin H (2007) Application of delf-organization competitive neural network in fault diagnosis of suck rod pumping system. J Pet Sci Eng 58:43

    Article  Google Scholar 

  17. Vaferi B, Rahnam Y, Darvishi P, Toorani AR, Lashkarbolook M (2013) Phase equilibrium estimation of binary systems containing ethanol using optimal feed forward neural network. J Supercrit Fluids 84:80

    Article  Google Scholar 

  18. Sreekanth S, Ramasamy HS, Sablani SS, Prasher SO (1999) A neural network approach for evaluation of surface heat transfer coefficient. J Food Process Preserv 23:329–348

    Article  Google Scholar 

  19. Parcheco VA, Sen M, Yang KT, Meclain RL (2001) Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data. Int J Heat Mass Transf 44(4):763–770

    Article  MATH  Google Scholar 

  20. Haykin S (1994) Neural networks, a comprehensive foundation, 1st edn. Prentice Hall PTR, Upper Saddle River

    MATH  Google Scholar 

  21. Aydinalp M, Ugursal VI, Fung AS (2001) Predicting residential appliance, lighting, and space cooling energy consumption using neural networks. Proceeding of ITEC2001, International Thermal Energy Congress Cesme Turkey 417

  22. Hartman E, Keeler JD, Kowalski JM (1990) Layered neural networks with Gaussian hidden units as universal approximations. Neural Comput 2(2):210–215

    Article  Google Scholar 

  23. Montazer GA, Khoshniat H, Fathi V (2013) Improvement of RBF neural networks using Fuzzy-OSD algorithm in an online radar pulse classification system. Appl Soft Comput 13(9):3831–3838

    Article  Google Scholar 

  24. Iliyas SA, Elshafei M, Habib MA, Adeniran AA (2013) RBF neural network inferential sensor for process emission monitoring. Control Eng Pract 21(7):962–970

    Article  Google Scholar 

  25. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2):302–309

    Article  Google Scholar 

  26. Mmohammadiun M, Dashtestani F, Alizadeh M (2016) Exergy prediction model of a double pipe heat exchanger using metal oxide nanofluids and twisted tape based on the artificial neural network approach and experimental results. J Heat Transf 138:1–10

    Google Scholar 

  27. Box GEP, Draper NR (1975) Robust Designs. Biometrika 62:347–352

    Article  MathSciNet  MATH  Google Scholar 

  28. Hunter WG, Hunter JS (1978) Statistics for experimenters: an introduction to design, data analysis, and model building. Wiley, New York, NY

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran

    Nahid Ghasemi

  2. Department of Chemistry, Payame Noor University (PNU), P.O. Box 19395-3697, Tehran, Iran

    Reza Aghayari & Heydar Maddah

Authors
  1. Nahid Ghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Aghayari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heydar Maddah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nahid Ghasemi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghasemi, N., Aghayari, R. & Maddah, H. Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger. Heat Mass Transfer 54, 1707–1719 (2018). https://doi.org/10.1007/s00231-017-2261-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00231-017-2261-7

Keywords

Search

Navigation