IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p3859-d583183.html
   My bibliography  Save this article

Heat Transmission of Engine-Oil-Based Rotating Nanofluids Flow with Influence of Partial Slip Condition: A Computational Model

Author

Listed:
  • Azad Hussain

    (Department of Mathematics, University of Gujrat, Gujrat 50700, Pakistan)

  • Mubashar Arshad

    (Department of Mathematics, University of Gujrat, Gujrat 50700, Pakistan)

  • Aysha Rehman

    (Department of Mathematics, University of Gujrat, Gujrat 50700, Pakistan)

  • Ali Hassan

    (Department of Mathematics, University of Gujrat, Gujrat 50700, Pakistan)

  • Sayed K. Elagan

    (Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Nawal A. Alshehri

    (Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

This particular research was conducted with the aim of describing the impact of a rotating nanoliquid on an elasting surface. This specific study was carried out using a two-phase nanoliquid model. In this study engine oil is used as the base fluid, and two forms of nanoparticles are used, namely, titanium oxide and zinc oxide (TiO 2 and ZnO). Using appropriate similarity transformations, the arising system of partial differential equations and the related boundary conditions are presented and then converted into a system of ordinary differential equations. These equations are numerically tackled using powerful techniques. Graphs for nanoparticle rotation parameter and volume fraction for both types of nanoparticles present the results for the velocity and heat transfer features. Quantities of physical significance are measured and evaluated, such as local heat flux intensity and local skin friction coefficients at the linear stretching surface. Numerical values for skin friction and local heat flux amplitude are determined in the presence of slip factor.

Suggested Citation

  • Azad Hussain & Mubashar Arshad & Aysha Rehman & Ali Hassan & Sayed K. Elagan & Nawal A. Alshehri, 2021. "Heat Transmission of Engine-Oil-Based Rotating Nanofluids Flow with Influence of Partial Slip Condition: A Computational Model," Energies, MDPI, vol. 14(13), pages 1-13, June.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3859-:d:583183
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/3859/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/13/3859/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. W. Abbas & M. M. Magdy, 2020. "Heat and Mass Transfer Analysis of Nanofluid Flow Based on , , and over a Moving Rotating Plate and Impact of Various Nanoparticle Shapes," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, May.
    2. 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.
    3. Azad Hussain & Aysha Rehman & Sohail Nadeem & M. Y. Malik & Alibek Issakhov & Lubna Sarwar & Shafiq Hussain, 2021. "A Combined Convection Carreau–Yasuda Nanofluid Model over a Convective Heated Surface near a Stagnation Point: A Numerical Study," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, April.
    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. Ali Hassan & Azad Hussain & Mubashar Arshad & Jan Awrejcewicz & Witold Pawlowski & Fahad M. Alharbi & Hanen Karamti, 2022. "Heat and Mass Transport Analysis of MHD Rotating Hybrid Nanofluids Conveying Silver and Molybdenum Di-Sulfide Nano-Particles under Effect of Linear and Non-Linear Radiation," Energies, MDPI, vol. 15(17), pages 1-19, August.
    2. Azad Hussain & Mubashar Arshad & Aysha Rehman & Ali Hassan & S. K. Elagan & Hijaz Ahmad & Amira Ishan, 2021. "Three-Dimensional Water-Based Magneto-Hydrodynamic Rotating Nanofluid Flow over a Linear Extending Sheet and Heat Transport Analysis: A Numerical Approach," Energies, MDPI, vol. 14(16), pages 1-15, August.
    3. Wenxiong Xi & Mengyao Xu & Kai Ma & Jian Liu, 2022. "Heat Transfer Enhancement Methods Applied in Energy Conversion, Storage and Propulsion Systems," Energies, MDPI, vol. 15(19), pages 1-3, October.

    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. Azad Hussain & Mubashar Arshad & Aysha Rehman & Ali Hassan & S. K. Elagan & Hijaz Ahmad & Amira Ishan, 2021. "Three-Dimensional Water-Based Magneto-Hydrodynamic Rotating Nanofluid Flow over a Linear Extending Sheet and Heat Transport Analysis: A Numerical Approach," Energies, MDPI, vol. 14(16), pages 1-15, August.
    2. Meznah M. Alanazi & Awatif A. Hendi & Qadeer Raza & M. Zubair Akbar Qureshi & Fatima Shafiq Hira & Bagh Ali & Nehad Ali Shah & Jae Dong Chung, 2022. "Significance of Multi-Hybrid Morphology Nanoparticles on the Dynamics of Water Fluid Subject to Thermal and Viscous Joule Performance," Mathematics, MDPI, vol. 10(22), pages 1-23, November.
    3. Roy Setiawan & Reza Daneshfar & Omid Rezvanjou & Siavash Ashoori & Maryam Naseri, 2021. "Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17606-17627, December.
    4. Mohammed Algarni & Mashhour A. Alazwari & Mohammad Reza Safaei, 2021. "Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    5. Anum Shafiq & Ilyas Khan & Ghulam Rasool & El-Sayed M. Sherif & Asiful H. Sheikh, 2020. "Influence of Single- and Multi-Wall Carbon Nanotubes on Magnetohydrodynamic Stagnation Point Nanofluid Flow over Variable Thicker Surface with Concave and Convex Effects," Mathematics, MDPI, vol. 8(1), pages 1-15, January.
    6. Hemmat Esfe, Mohammad & Esfandeh, Saeed, 2020. "The statistical investigation of multi-grade oil based nanofluids: Enriched by MWCNT and ZnO nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    7. 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).
    8. Li, Zhixiong & Shahrajabian, Hamzeh & Bagherzadeh, Seyed Amin & Jadidi, Hamid & Karimipour, Arash & Tlili, Iskander, 2020. "Effects of nano-clay content, foaming temperature and foaming time on density and cell size of PVC matrix foam by presented Least Absolute Shrinkage and Selection Operator statistical regression via s," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    9. 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).
    10. 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).
    11. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
    12. 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.
    13. 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).
    14. 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).
    15. 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).
    16. 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).

    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:gam:jeners:v:14:y:2021:i:13:p:3859-:d:583183. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.