IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v525y2019icp1418-1428.html
   My bibliography  Save this article

Robust Weighted Least Squares Support Vector Regression algorithm to estimate the nanofluid thermal properties of water/graphene Oxide–Silicon carbide mixture

Author

Listed:
  • Shahsavar, Amin
  • Bagherzadeh, Seyed Amin
  • Mahmoudi, Boshra
  • Hajizadeh, Ahmad
  • Afrand, Masoud
  • Nguyen, Truong Khang

Abstract

A new optimization/statistical approach of “Robust Weighted Least Squares Support Vector Regression” algorithm (RWLS-SVR) is provided for the first time. The experimental achieved amounts of the thermal conductivity for a new hybrid nanofluid of water/Graphene Oxide–Silicon Carbide, are examined at different values of temperature and nanoparticles volume fraction. A Support Vector Regression is a supervised learning regression algorithm based on the Support Vector Machine methodology. However in the Least Squares Support Vector Machine method, the inequality constraints are converted to equality constraints and the sum squared error function is employed. Moreover a LS-SVR is applied to the problem in order to calculate the error variables. Afterwards, the weights computed based on the error variables are applied to the optimization problem in order to reduce the effects of outliers on the final results. As a result, the WLS-SVR method does not significantly increase the computational burden, but it provides sparseness and robustness.

Suggested Citation

  • Shahsavar, Amin & Bagherzadeh, Seyed Amin & Mahmoudi, Boshra & Hajizadeh, Ahmad & Afrand, Masoud & Nguyen, Truong Khang, 2019. "Robust Weighted Least Squares Support Vector Regression algorithm to estimate the nanofluid thermal properties of water/graphene Oxide–Silicon carbide mixture," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1418-1428.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:1418-1428
    DOI: 10.1016/j.physa.2019.03.086
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119303218
    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.2019.03.086?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. 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.
    2. Safaei, Mohammad Reza & Karimipour, Arash & Abdollahi, Ali & Nguyen, Truong Khang, 2018. "The investigation of thermal radiation and free convection heat transfer mechanisms of nanofluid inside a shallow cavity by lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 515-535.
    3. Alipour, Pedram & Toghraie, Davood & Karimipour, Arash & Hajian, Mehdi, 2019. "Modeling different structures in perturbed Poiseuille flow in a nanochannel by using of molecular dynamics simulation: Study the equilibrium," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 13-30.
    4. 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.
    5. Jourabian, Mahmoud & Darzi, A. Ali Rabienataj & Toghraie, Davood & Akbari, Omid ali, 2018. "Melting process in porous media around two hot cylinders: Numerical study using the lattice Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 316-335.
    6. M. Goodarzi & M. R. Safaei & A. Karimipour & K. Hooman & M. Dahari & S. N. Kazi & E. Sadeghinezhad, 2014. "Comparison of the Finite Volume and Lattice Boltzmann Methods for Solving Natural Convection Heat Transfer Problems inside Cavities and Enclosures," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-15, February.
    7. 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.
    8. 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.
    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. Ma, Ting & Guo, Zhixiong & Lin, Mei & Wang, Qiuwang, 2021. "Recent trends on nanofluid heat transfer machine learning research applied to renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Moghadam, Iman Panahi & Afrand, Masoud & Hamad, Samir M. & Barzinjy, Azeez A. & Talebizadehsardari, Pouyan, 2020. "Curve-fitting on experimental data for predicting the thermal-conductivity of a new generated hybrid nanofluid of graphene oxide-titanium oxide/water," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).

    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. 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).
    2. Bahrami, Mehrdad & Akbari, Mohammad & Bagherzadeh, Seyed Amin & Karimipour, Arash & Afrand, Masoud & Goodarzi, Marjan, 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: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 159-168.
    3. 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.
    4. 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).
    5. Karimipour, Arash & Bagherzadeh, Seyed Amin & Taghipour, Abdolmajid & Abdollahi, Ali & Safaei, Mohammad Reza, 2019. "A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 89-97.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. Alsarraf, Jalal & Bagherzadeh, Seyed Amin & Shahsavar, Amin & Rostamzadeh, Mahfouz & Trinh, Pham Van & Tran, Minh Duc, 2019. "Rheological properties of SWCNT/EG mixture by a new developed optimization approach of LS-Support Vector Regression according to empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 912-920.
    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. Alnaqi, Abdulwahab A. & Sayyad Tavoos Hal, Sina & Aghaei, Alireza & Soltanimehr, Mehdi & Afrand, Masoud & Nguyen, Truong Khang, 2019. "Predicting the effect of functionalized multi-walled carbon nanotubes on thermal performance factor of water under various Reynolds number using artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 493-500.
    13. 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.
    14. 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.
    15. Alipour, Pedram & Toghraie, Davood & Karimipour, Arash & Hajian, Mehdi, 2019. "Modeling different structures in perturbed Poiseuille flow in a nanochannel by using of molecular dynamics simulation: Study the equilibrium," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 13-30.
    16. Afrouzi, Hamid Hassanzadeh & Ahmadian, Majid & Moshfegh, Abouzar & Toghraie, Davood & Javadzadegan, Ashkan, 2019. "Statistical analysis of pulsating non-Newtonian flow in a corrugated channel using Lattice-Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    17. 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).
    18. Alipour, Pedram & Toghraie, Davood & Karimipour, Arash, 2019. "Investigation the atomic arrangement and stability of the fluid inside a rough nanochannel in both presence and absence of different roughness by using of accurate nano scale simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 639-660.
    19. Najafi, Mohammad Javid & Naghavi, Sayed Mahdi & Toghraie, Davood, 2019. "Numerical simulation of flow in hydro turbines channel to improve its efficiency by using of Lattice Boltzmann Method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 390-408.
    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:525:y:2019:i:c:p:1418-1428. 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.