IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v537y2020ics037843711931578x.html
   My bibliography  Save this article

Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method

Author

Listed:
  • Xiaohong, Dai
  • Huajiang, Chen
  • Bagherzadeh, Seyed Amin
  • Shayan, Masoud
  • Akbari, Mohammad

Abstract

Ridge regression is a regularization method which evaluated according to the experimental results of the hybrid nanofluid containing SiO2 and MWCNTs suspended in water and ethylene glycol as the base fluid concerned to the thermal conductivity versus different amounts of nanoparticles concentration and temperature. The novelty of this study is that this method is very useful for decreasing the variance of the fit and improves its future predictions Also, it is applicable especially in handling the small training data sets. Meanwhile, if the training data set is small, ridge regression can find a solution based on the cross validation and the ridge regression penalty. The findings showed that the fit is almost perfect because the fit line is almost identical with the Y=T line indicating the ideal fit. Also, the slope and y-intercept values of the fit line are 0.98 and 0.0076, respectively.

Suggested Citation

  • Xiaohong, Dai & Huajiang, Chen & Bagherzadeh, Seyed Amin & Shayan, Masoud & Akbari, Mohammad, 2020. "Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s037843711931578x
    DOI: 10.1016/j.physa.2019.122782
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711931578X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.122782?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ahmadi Balootaki, Azam & Karimipour, Arash & Toghraie, Davood, 2018. "Nano scale lattice Boltzmann method to simulate the mixed convection heat transfer of air in a lid-driven cavity with an endothermic obstacle inside," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 681-701.
    2. Saba, Fitnat & Ahmed, Naveed & Khan, Umar & Mohyud-Din, Syed Tauseef, 2019. "Impact of an effective Prandtl number model and across mass transport phenomenon on the γAI2O3 nanofluid flow inside a channel," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    3. Hemmat Esfe, Mohammad & Kamyab, Mohammad Hassan & Afrand, Masoud & Amiri, Mahmoud Kiannejad, 2018. "Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10W-40 engine oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 610-624.
    4. Talebizadehsardari, Pouyan & Shahsavar, Amin & Toghraie, Davood & Barnoon, Pouya, 2019. "An experimental investigation for study the rheological behavior of water–carbon nanotube/magnetite nanofluid subjected to a magnetic field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    5. Ghasemi, Ali & Hassani, Mohsen & Goodarzi, Marjan & Afrand, Masoud & Manafi, Sahebali, 2019. "Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 36-45.
    6. Mahyari, Amirhossein Ansari & Karimipour, Arash & Afrand, Masoud, 2019. "Effects of dispersed added Graphene Oxide-Silicon Carbide nanoparticles to present a statistical formulation for the mixture thermal properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 98-112.
    7. Safaei, Mohammad Reza & Hajizadeh, Ahmad & Afrand, Masoud & Qi, Cong & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 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: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 209-216.
    8. Karimipour, Arash & D’Orazio, Annunziata & Goodarzi, Marjan, 2018. "Develop the lattice Boltzmann method to simulate the slip velocity and temperature domain of buoyancy forces of FMWCNT nanoparticles in water through a micro flow imposed to the specified heat flux," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 729-745.
    9. Karimipour, Arash & Hemmat Esfe, Mohammad & Safaei, Mohammad Reza & Toghraie Semiromi, Davood & Jafari, Saeed & Kazi, S.N., 2014. "Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 150-168.
    10. Irani, Mohammadhossein & Afrand, Masoud & Mehmandoust, Babak, 2019. "Curve fitting on experimental data of a new hybrid nano-antifreeze viscosity: Presenting new correlations for non-Newtonian nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    11. Goodarzi, Marjan & D’Orazio, Annunziata & Keshavarzi, Ahmad & Mousavi, Sayedali & Karimipour, Arash, 2018. "Develop the nano scale method of lattice Boltzmann to predict the fluid flow and heat transfer of air in the inclined lid driven cavity with a large heat source inside, Two case studies: Pure natural ," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 210-233.
    12. Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 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: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 406-415.
    13. Ruhani, Behrooz & Barnoon, Pouya & Toghraie, Davood, 2019. "Statistical investigation for developing a new model for rheological behavior of Silica–ethylene glycol/Water hybrid Newtonian nanofluid using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 616-627.
    14. Hemmat Esfe, Mohammad & Reiszadeh, Mahdi & Esfandeh, Saeed & Afrand, Masoud, 2018. "Optimization of MWCNTs (10%) – Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 731-744.
    15. Hemmat Esfe, Mohammad & Rostamian, Hossein & Esfandeh, Saeed & Afrand, Masoud, 2018. "Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 625-634.
    16. Toghraie, Davood & Sina, Nima & Jolfaei, Niyusha Adavoodi & Hajian, Mehdi & Afrand, Masoud, 2019. "Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    17. Ruhani, Behrooz & Toghraie, Davood & Hekmatifar, Maboud & Hadian, Mahdieh, 2019. "Statistical investigation for developing a new model for rheological behavior of ZnO–Ag (50%–50%)/Water hybrid Newtonian nanofluid using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 741-751.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tayfun Uyanık & Yunus Yalman & Özcan Kalenderli & Yasin Arslanoğlu & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel," Mathematics, MDPI, vol. 10(22), pages 1-21, November.
    2. Wei Wang & Linjian Li & Sheng Li & Fei Yin & Fang Liao & Tao Zhang & Xiaosong Li & Xiong Xiao & Yue Ma, 2022. "Average ordinary least squares‐centered penalized regression: A more efficient way to address multicollinearity than ridge regression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(3), pages 347-368, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmadi, Mohammad Hossein & Ghazvini, Mahyar & Maddah, Heydar & Kahani, Mostafa & Pourfarhang, Samira & Pourfarhang, Amin & Heris, Saeed Zeinali, 2020. "Prediction of the pressure drop for CuO/(Ethylene glycol-water) nanofluid flows in the car radiator by means of Artificial Neural Networks analysis integrated with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    2. Peng, Yeping & Parsian, Amir & Khodadadi, Hossein & Akbari, Mohammad & Ghani, Kamal & Goodarzi, Marjan & Bach, Quang-Vu, 2020. "Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3 – Cu nanoparticles dispersed in ethy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Moradikazerouni, Alireza & Hajizadeh, Ahmad & Safaei, Mohammad Reza & Afrand, Masoud & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2019. "Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 138-145.
    4. Tian, Zhe & Arasteh, Hossein & Parsian, Amir & Karimipour, Arash & Safaei, Mohammad Reza & Nguyen, Truong Khang, 2019. "Estimate the shear rate & apparent viscosity of multi-phased non-Newtonian hybrid nanofluids via new developed Support Vector Machine method coupled with sensitivity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    5. Tian, Zhe & Rostami, Sara & Taherialekouhi, Roozbeh & Karimipour, Arash & Moradikazerouni, Alireza & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2020. "Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experim," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    6. Rasti, Ehsan & Talebi, Farhad & Mazaheri, Kiumars, 2019. "Improvement of drag reduction prediction in viscoelastic pipe flows using proper low-Reynolds k-ε turbulence models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 412-422.
    7. Wei, Li & Arasteh, Hossein & abdollahi, Ali & Parsian, Amir & Taghipour, Abdolmajid & Mashayekhi, Ramin & Tlili, Iskander, 2020. "Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    8. Alsarraf, Jalal & Moradikazerouni, Alireza & Shahsavar, Amin & Afrand, Masoud & Salehipour, Hamzeh & Tran, Minh Duc, 2019. "Hydrothermal analysis of turbulent boehmite alumina nanofluid flow with different nanoparticle shapes in a minichannel heat exchanger using two-phase mixture model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 275-288.
    9. Al-Rashed, Abdullah A.A.A. & Ranjbarzadeh, Ramin & Aghakhani, Saeed & Soltanimehr, Mehdi & Afrand, Masoud & Nguyen, Truong Khang, 2019. "Entropy generation of boehmite alumina nanofluid flow through a minichannel heat exchanger considering nanoparticle shape effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 724-736.
    10. Arani, Ali Akbar Abbasian & Alirezaie, Ali & Kamyab, Mohammad Hassan & Motallebi, Sayyid Majid, 2020. "Statistical analysis of enriched water heat transfer with various sizes of MgO nanoparticles using artificial neural networks modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    11. Zarei, Amir & Karimipour, Arash & Meghdadi Isfahani, Amir Homayoon & Tian, Zhe, 2019. "Improve the performance of lattice Boltzmann method for a porous nanoscale transient flow by provide a new modified relaxation time equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    12. Sarafraz, M.M. & Tlili, I. & Tian, Zhe & Bakouri, Mohsen & Safaei, Mohammad Reza, 2019. "Smart optimization of a thermosyphon heat pipe for an evacuated tube solar collector using response surface methodology (RSM)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    13. Al-Rashed, Abdullah A.A.A., 2019. "Optimization of heat transfer and pressure drop of nano-antifreeze using statistical method of response surface methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 531-542.
    14. Hemmat Esfe, Mohammad & Abbasian Arani, Ali Akbar & Esfandeh, Saeed & Afrand, Masoud, 2019. "Proposing new hybrid nano-engine oil for lubrication of internal combustion engines: Preventing cold start engine damages and saving energy," Energy, Elsevier, vol. 170(C), pages 228-238.
    15. Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 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: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 406-415.
    16. Nafchi, Peyman Mirzakhani & Karimipour, Arash & Afrand, Masoud, 2019. "The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 1-18.
    17. Ahmadi, Mohammad Hossein & Baghban, Alireza & Sadeghzadeh, Milad & Hadipoor, Masoud & Ghazvini, Mahyar, 2020. "Evolving connectionist approaches to compute thermal conductivity of TiO2/water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    18. Ammar A. Melaibari & Yacine Khetib & Abdullah K. Alanazi & S. Mohammad Sajadi & Mohsen Sharifpur & Goshtasp Cheraghian, 2021. "Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials," Sustainability, MDPI, vol. 13(20), pages 1-17, October.
    19. Jourabian, Mahmoud & Rabienataj Darzi, A. Ali & Akbari, Omid Ali & Toghraie, Davood, 2020. "The enthalpy-based lattice Boltzmann method (LBM) for simulation of NePCM melting in inclined elliptical annulus," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    20. Safaei, Mohammad Reza & Hajizadeh, Ahmad & Afrand, Masoud & Qi, Cong & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 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: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 209-216.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s037843711931578x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.