Skip to main content
SpringerLink
Log in

Predictability evaluation of support vector regression methods for thermophysical properties, heat transfer performance, and pumping power estimation of MWCNT/ZnO–engine oil hybrid nanofluid

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

In this paper, the efficiency of support vector regression (SVR) model to predict the thermophysical properties, heat transfer performance, and pumping power of MWCNT/ZnO–engine oil hybrid nanofluid has been demonstrated. Temperature and solid concentration were considered as inputs in order to train the model to estimate thermophysical properties including thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both internal laminar and turbulent flow regimes. The results took the mean of ten runs of fivefold cross-validation of a Gaussian kernel with a combination of the Bayesian optimization technique to find the best tuning parameters. In order to evaluate the performance of the proposed model, three popular statistical indices including root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) have been calculated. The results show that the SVR technique is highly suitable for thermophysical properties prediction as it can map complex relationships between variables. Finally, the studied thermophysical properties have been successfully developed using the proposed model. Regarding the developed results, increasing temperature resulted in increasing the thermal conductivity, dynamic viscosity, and pumping power of MWCNT/ZnO–engine oil hybrid nanofluid. Despite the dynamic viscosity, the thermal conductivity increased as the solid concentration increased. It was revealed that the machine-learning technique proposed in this research could be developed as a very useful predictive tool.

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

Similar content being viewed by others

References

  1. Jović S (2020) Adaptive neuro-fuzzy prediction of flow pattern and gas hold-up in bubble column reactors. Eng Comput. https://doi.org/10.1007/s00366-019-00905-y

    Article  Google Scholar 

  2. Koopialipoor M, Fahimifar A, Ghaleini EN, Momenzadeh M, Armaghani DJ (2020) Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Eng Comput 36:345–357. https://doi.org/10.1007/s00366-019-00701-8

    Article  Google Scholar 

  3. Liu L, Moayedi H, Rashid ASA, Rahman SSA, Nguyen H (2020) Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Eng Comput 36:421–433. https://doi.org/10.1007/s00366-019-00767-4

    Article  Google Scholar 

  4. Qiao W, Lu H, Zhou G, Azimi M, Yang Q, Tian W (2020) A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.118612

    Article  Google Scholar 

  5. Qiao W, Tian W, Tian Y, Yang Q, Wang Y, Zhang J (2019) The forecasting of PM2.5 using a hybrid model based on wavelet transform and an improved deep learning algorithm. IEEE Access 7:142814–142825. https://doi.org/10.1109/access.2019.2944755

    Article  Google Scholar 

  6. Qiao W, Huang K, Azimi M, Han S (2019) A novel hybrid prediction model for hourly gas consumption in supply side based on improved whale optimization algorithm and relevance vector machine. IEEE Access 7:88218–88230. https://doi.org/10.1109/ACCESS.2019.2918156

    Article  Google Scholar 

  7. Moayedi H, Nguyen H, Kok Foong L (2019) Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network. Eng Comput. https://doi.org/10.1007/s00366-019-00882-2

    Article  Google Scholar 

  8. Qiao W, Yang Z (2020) An improved Dolphin Swarm Algorithm based on kernel fuzzy C-means in the application of solving the optimal problems of large-scale function. IEEE Access 8:2073–2089. https://doi.org/10.1109/ACCESS.2019.2958456

    Article  Google Scholar 

  9. Qiao W, Yang Z, Kang Z, Pan Z (2020) Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.103323

    Article  Google Scholar 

  10. Chen XL, Fu JP, Yao JL, Gan JF (2018) Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Eng Comput 34:367–383. https://doi.org/10.1007/s00366-017-0547-5

    Article  Google Scholar 

  11. Huang JW, Chiang CW, Chang JW (2018) Email security level classification of imbalanced data using artificial neural network: the real case in a world-leading enterprise. Eng Appl Artif Intell 75:11–21. https://doi.org/10.1016/j.engappai.2018.07.010

    Article  Google Scholar 

  12. Talaat M, Gobran MH, Wasfi M (2018) A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine. Eng Appl Artif Intell 68:222–235. https://doi.org/10.1016/j.engappai.2017.10.014

    Article  Google Scholar 

  13. Alsarraf J, Moayedi H, Rashid ASA, Muazu MA, Shahsavar A (2019) Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system. Eng Comput. https://doi.org/10.1007/s00366-019-00721-4

    Article  Google Scholar 

  14. Tian H, Shu J, Han L (2019) The effect of ICA and PSO on ANN results in approximating elasticity modulus of rock material. Eng Comput 35:305–314. https://doi.org/10.1007/s00366-018-0600-z

    Article  Google Scholar 

  15. Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984. https://doi.org/10.1007/s00366-018-0644-0

    Article  Google Scholar 

  16. Choi JA, Eastman SUS (1995) Enhancing thermal conductivity of fluids with nanoparticles, ASME FED, vol 231, pp 99–103, International mechanical engineering congress and exhibition, San Francisco, CA (United States)

  17. Alarifi IM, Alkouh AB, Ali V, Nguyen HM, Asadi A (2019) On the rheological properties of MWCNT-TiO2/oil hybrid nanofluid: an experimental investigation on the effects of shear rate, temperature, and solid concentration of nanoparticles. Powder Technol 355:157–162. https://doi.org/10.1016/J.POWTEC.2019.07.039

    Article  Google Scholar 

  18. Asadi A, Alarifi IM, Ali V, Nguyen HM (2019) An experimental investigation on the effects of ultrasonication time on stability and thermal conductivity of MWCNT-water nanofluid: finding the optimum ultrasonication time. Ultrason Sonochem. https://doi.org/10.1016/j.ultsonch.2019.104639

    Article  Google Scholar 

  19. Asadi A, Alarifi IM, Foong LK (2020) An experimental study on characterization, stability and dynamic viscosity of CuO–TiO2/water hybrid nanofluid. J Mol Liq. https://doi.org/10.1016/j.molliq.2020.112987

    Article  Google Scholar 

  20. Asadi A, Pourfattah F (2019) Heat transfer performance of two oil-based nanofluids containing ZnO and MgO nanoparticles; a comparative experimental investigation. Powder Technol 343:296–308. https://doi.org/10.1016/J.POWTEC.2018.11.023

    Article  Google Scholar 

  21. Asadi A, Asadi M, Rezaniakolaei A, Rosendahl LA, Wongwises S (2018) An experimental and theoretical investigation on heat transfer capability of Mg (OH) 2/MWCNT-engine oil hybrid nano-lubricant adopted as a coolant and lubricant fluid. Appl Therm Eng 129:577–586. https://doi.org/10.1016/j.applthermaleng.2017.10.074

    Article  Google Scholar 

  22. Lyu Z, Asadi A, Alarifi IM, Ali V, Foong LK (2020) Thermal and fluid dynamics performance of MWCNT-water nanofluid based on thermophysical properties: an experimental and theoretical study. Sci Rep. 10:5185. https://doi.org/10.1038/s41598-020-62143-3

    Article  Google Scholar 

  23. Pourfattah F, Abbasian Arani AA, Babaie MR, Nguyen HM, Asadi A (2019) On the thermal characteristics of a manifold microchannel heat sink subjected to nanofluid using two-phase flow simulation. Int J Heat Mass Transf 143:118518. https://doi.org/10.1016/j.ijheatmasstransfer.2019.118518

    Article  Google Scholar 

  24. Hadavand M, Yousefzadeh S, Ali Akbari O, Pourfattah F, Minh Nguyen H, Asadi A (2019) A numerical investigation on the effects of mixed convection of Ag-water nanofluid inside a sim-circular lid-driven cavity on the temperature of an electronic silicon chip. Appl Therm Eng. https://doi.org/10.1016/j.applthermaleng.2019.114298

    Article  Google Scholar 

  25. Alarifi IM, Nguyen HM, Naderi Bakhtiyari A, Asadi A (2019) Feasibility of ANFIS-PSO and ANFIS-GA models in predicting thermophysical properties of Al2O3-MWCNT/oil hybrid nanofluid. Materials 12:3628. https://doi.org/10.3390/ma12213628

    Article  Google Scholar 

  26. Asadi A, Alarifi IM, Nguyen HM, Moayedi H (2020) Feasibility of least-square support vector machine in predicting the effects of shear rate on the rheological properties and pumping power of MWCNT–MgO/oil hybrid nanofluid based on experimental data. J Therm Anal Calorim. https://doi.org/10.1007/s10973-020-09279-6

    Article  Google Scholar 

  27. Sun F, Li X, Liao H, Zhang X (2017) A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component. Adv Mech Eng 9:1–9. https://doi.org/10.1177/1687814016685963

    Article  Google Scholar 

  28. Awad M, Khanna R (2015) Efficient learning machines: theories, concepts, and application for engineers and system designers, Apress

  29. Bagherzadeh SA, D’Orazio A, Karimipour A, Goodarzi M, Bach QV (2019) A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: outputs predicted analytically instead of numerically to more accuracy and less costs. Physica A 521:406–415. https://doi.org/10.1016/j.physa.2019.01.048

    Article  Google Scholar 

  30. Alrashed AAAA, Gharibdousti MS, Goodarzi M, de Oliveira LR, Safaei MR, Bandarra Filho EP (2018) Effects on thermophysical properties of carbon based nanofluids: experimental data, modelling using regression, ANFIS and ANN. Int J Heat Mass Transf 125:920–932. https://doi.org/10.1016/j.ijheatmasstransfer.2018.04.142

    Article  Google Scholar 

  31. Bahrami M, Akbari M, Bagherzadeh SA, Karimipour A, Afrand M, Goodarzi M (2019) Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: measure MSEs between targets & ANN for Fe–CuO/Eg–water nanofluid. Physica A 519:159–168. https://doi.org/10.1016/j.physa.2018.12.031

    Article  Google Scholar 

  32. Safaei MR, Hajizadeh A, Afrand M, Qi C, Yarmand H, Zulkifli NWBM (2019) Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO–TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data. Physica A 519:209–216. https://doi.org/10.1016/j.physa.2018.12.010

    Article  Google Scholar 

  33. Ghasemi A, Hassani M, Goodarzi M, Afrand M, Manafi S (2019) Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network. Physica A 514:36–45. https://doi.org/10.1016/j.physa.2018.09.004

    Article  Google Scholar 

  34. Kannaiyan S, Boobalan C, Nagarajan FC, Sivaraman S (2019) Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks. Chin J Chem Eng 27:726–736. https://doi.org/10.1016/j.cjche.2018.07.018

    Article  Google Scholar 

  35. Esfe MH, Esfandeh S, Afrand M, Rejvani M, Rostamian SH (2018) Experimental evaluation, new correlation proposing and ANN modeling of thermal properties of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles for internal combustion engines applications. Appl Therm Eng 133:452–463. https://doi.org/10.1016/j.applthermaleng.2017.11.131

    Article  Google Scholar 

  36. Moradikazerouni A, Hajizadeh A, Safaei MR, Afrand M, Yarmand H, Zulkifli NWBM (2019) Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: optimal artificial neural network and curve-fitting. Physica A 521:138–145. https://doi.org/10.1016/j.physa.2019.01.051

    Article  Google Scholar 

  37. Sedaghat F, Yousefi F (2019) Synthesizes, characterization, measurements and modeling thermal conductivity and viscosity of graphene quantum dots nanofluids. J Mol Liq 278:299–308. https://doi.org/10.1016/j.molliq.2019.01.073

    Article  Google Scholar 

  38. Hemmat Esfe M, Ahangar MRH, Toghraie D, Hajmohammad MH, Rostamian H, Tourang H (2016) Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40%) nanofluid using experimental data. J Therm Anal Calorim 126:837–843. https://doi.org/10.1007/s10973-016-5469-8

    Article  Google Scholar 

  39. Eshgarf H, Sina N, Esfe MH, Izadi F, Afrand M (2018) Prediction of rheological behavior of MWCNTs–SiO2/EG–water non-Newtonian hybrid nanofluid by designing new correlations and optimal artificial neural networks. J Therm Anal Calorim 132:1029–1038. https://doi.org/10.1007/s10973-017-6895-y

    Article  Google Scholar 

  40. Vakili M, Karami M, Delfani S, Khosrojerdi S, Kalhor K (2017) Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions. J Therm Anal Calorim 129:629–637. https://doi.org/10.1007/s10973-017-6217-4

    Article  Google Scholar 

  41. Maddah H, Aghayari R, Ahmadi MH, Rahimzadeh M, Ghasemi N (2018) Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM). J Therm Anal Calorim 134:2275–2286. https://doi.org/10.1007/s10973-018-7827-1

    Article  Google Scholar 

  42. Esfe MH, Rejvani M, Karimpour R, Abbasian Arani AA (2017) Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data. J Therm Anal Calorim 128:1359–1371. https://doi.org/10.1007/s10973-016-6002-9

    Article  Google Scholar 

  43. Vafaei M, Afrand M, Sina N, Kalbasi R, Sourani F, Teimouri H (2017) Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks. Physica E 85:90–96. https://doi.org/10.1016/j.physe.2016.08.020

    Article  Google Scholar 

  44. Hemmat Esfe M, Firouzi M, Afrand M (2018) Experimental and theoretical investigation of thermal conductivity of ethylene glycol containing functionalized single walled carbon nanotubes. Physica E 95:71–77. https://doi.org/10.1016/j.physe.2017.08.017

    Article  Google Scholar 

  45. Baghban A, Habibzadeh S, Ashtiani FZ (2019) Toward a modeling study of thermal conductivity of nanofluids using LSSVM strategy. J Therm Anal Calorim 135:507–522. https://doi.org/10.1007/s10973-018-7074-5

    Article  Google Scholar 

  46. Ahmadi MH, Ahmadi MA, Nazari MA, Mahian O, Ghasempour R (2019) A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J Therm Anal Calorim 135:271–281. https://doi.org/10.1007/s10973-018-7035-z

    Article  Google Scholar 

  47. Ahmadi MH, Baghban A, Ghazvini M, Hadipoor M, Ghasempour R, Nazemzadegan MR (2019) An insight into the prediction of TiO2/water nanofluid viscosity through intelligence schemes. J Therm Anal Calorim. https://doi.org/10.1007/s10973-019-08636-4

    Article  Google Scholar 

  48. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  49. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999. https://doi.org/10.1109/72.788640

    Article  Google Scholar 

  50. Ahmadi MA, Bahadori A (2016) Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine. Int J Ambient Energy 37:486–494. https://doi.org/10.1080/01430750.2015.1004105

    Article  Google Scholar 

  51. Zendehboudi A, Baseer MA, Saidur R (2018) Application of support vector machine models for forecasting solar and wind energy resources: a review. J Clean Prod 199:272–285. https://doi.org/10.1016/j.jclepro.2018.07.164

    Article  Google Scholar 

  52. Asadi M, Asadi A (2016) Dynamic viscosity of MWCNT/ZnO-engine oil hybrid nanofluid: an experimental investigation and new correlation in different temperatures and solid concentrations. Int Commun Heat Mass Transf. https://doi.org/10.1016/j.icheatmasstransfer.2016.05.019

    Article  Google Scholar 

  53. Asadi A (2018) A guideline towards easing the decision-making process in selecting an effective nanofluid as a heat transfer fluid. Energy Convers Manag 175:1–10. https://doi.org/10.1016/J.ENCONMAN.2018.08.101

    Article  Google Scholar 

  54. Gopi G, Dauwels J, Asif MT, Ashwin S, Mitrovic N, Rasheed U, Jaillet P (2013) Bayesian Support Vector Regression for traffic speed prediction with error bars. IEEE Conf Intell Transp Syst Proc ITSC. https://doi.org/10.1109/itsc.2013.6728223

    Article  Google Scholar 

  55. Law T, Shawe-Taylor J (2017) Practical Bayesian support vector regression for financial time series prediction and market condition change detection. Quant Finance 17:1403–1416. https://doi.org/10.1080/14697688.2016.1267868

    Article  MathSciNet  MATH  Google Scholar 

  56. Alade IO, Abd Rahman MA, Saleh TA (2019) Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm. Sol Energy 183:74–82. https://doi.org/10.1016/j.solener.2019.02.060

    Article  Google Scholar 

  57. Law MH, Kwok JT (2001) Bayesian support vector regression. In: Proceedings of international workshop on artificial intelligence and statistics, vol 8, pp 239–244

  58. Kojić PS, Popović SS, Tokić MS, Šijački IM, Lukić NLJ, Jovičević DZ, Petrović DLJ (2017) Hydrodynamics of an external-loop airlift reactor with inserted membrane. Braz J Chem Eng 34:493–505. https://doi.org/10.1590/0104-6632.20170342s20150399

    Article  Google Scholar 

  59. Alex JS, Schoelkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

    Article  MathSciNet  Google Scholar 

  60. Henao R, Yuan X, Carin L (2014) Bayesian nonlinear support vector machines and discriminative factor modeling. Adv Neural Inf Process Syst 2:1–9

    Google Scholar 

  61. Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N (2016) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104:148–175. https://doi.org/10.1109/JPROC.2015.2494218

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at Majmaah University for funding this work under project number No (RGP-2019-15).

Author information

Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Amin Asadi

  2. Faculty of Natural Sciences, Duy Tan University, Da Nang, 550000, Vietnam

    Amin Asadi

  3. Center for Advanced Laser Manufacturing, School of Mechanical Engineering, Shandong University of Technology, Zibo, China

    Ali Naderi Bakhtiyari

  4. Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah, 11952, Riyadh, Saudi Arabia

    Ibrahim M. Alarifi

  5. Engineering and Applied Science Research Center, Majmaah University, Al-Majmaah, 11952, Riyadh, Saudi Arabia

    Ibrahim M. Alarifi

Authors
  1. Amin Asadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Naderi Bakhtiyari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ibrahim M. Alarifi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amin Asadi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Asadi, A., Bakhtiyari, A.N. & Alarifi, I.M. Predictability evaluation of support vector regression methods for thermophysical properties, heat transfer performance, and pumping power estimation of MWCNT/ZnO–engine oil hybrid nanofluid. Engineering with Computers 37, 3813–3823 (2021). https://doi.org/10.1007/s00366-020-01038-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01038-3

Keywords

Search

Navigation