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Effect of Magnetic Baffles and Magnetic Nanofluid on Thermo-Hydraulic Characteristics of Dimple Mini Channel for Thermal Energy Applications

Author

Listed:
  • Basma Souayeh

    (Department of Physics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
    Laboratory of Fluid Mechanics, Physics Department, Faculty of Science of Tunis, University of Tunis EI Manar, Tunis 2092, Tunisia)

  • Suvanjan Bhattacharyya

    (Department of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Pilani Campus, Vidya Vihar 333 031, Rajasthan, India)

  • Najib Hdhiri

    (Laboratory of Fluid Mechanics, Physics Department, Faculty of Science of Tunis, University of Tunis EI Manar, Tunis 2092, Tunisia)

  • Fayçal Hammami

    (Laboratory of Fluid Mechanics, Physics Department, Faculty of Science of Tunis, University of Tunis EI Manar, Tunis 2092, Tunisia)

  • Essam Yasin

    (Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha 2713, Qatar)

  • S. Suresh Kumar Raju

    (Department of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Mir Waqas Alam

    (Department of Physics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Tarfa Alsheddi

    (Department of Physics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

  • Muneerah Al Nuwairan

    (Department of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia)

Abstract

The combined effect of a magnetic baffle and a dimple turbulator on the heat transfer and pressure drop is investigated computationally in a mini channel. Fe 3 O 4 magnetic nanofluid is used as a working fluid. The Reynolds number (Re) is varied from 150 to 210 and the magnetic field intensities range from 1200 G to 2000 G. Finite-volume based commercial computational fluid dynamics (CFD) solver ANSYS-Fluent 18.1 was used for the numerical simulations. A laminar viscous model is used with pressure-velocity coupling along with second-order upwind discretization and QUICK scheme for discretizing the momentum and energy equations. The results show that there is an increase of 3.53%, 10.77%, and 25.39% in the Nusselt numbers when the magnetic fields of 1200 G, 1500 G and 2000 G, respectively, are applied at x = 15 mm, as compared to the flow without a magnetic field when the pitch = 10 mm. These values change to 1.51%, 6.14% and 18.47% for a pitch = 5 mm and 0.85%, 4.33%, and 15.25% for a pitch = 2.5 mm, when compared to the flow without a magnetic field in the respective geometries. When the two sources are placed at x = 7.5 mm and 15 mm, there is an increase of 4.52%, 13.93%, and 33.08% in the Nusselt numbers when magnetic fields of 1200 G, 1500 G, and 2000 G are applied when the pitch = 10 mm. The increment changed to 1.82%, 8.16%, and 22.31% for a pitch = 5 mm and 1.01%, 5.96%, and 21.38% for a pitch = 2.5 mm. This clearly shows that the two sources at the front have a higher increment in the Nusselt numbers compared to one source, due to higher turbulence. In addition, there is a decrease in the pressure drop of 10.82%, 16.778%, and 26.75% when magnetic fields of 1200 G, 1500 G, and 2000 G, respectively, are applied at x = 15 mm, as compared to flow without magnetic field when the pitch = 10 mm. These values change to 2.46%, 4.98%, and 8.54% for a pitch = 5 mm and 1.62%, 3.52%, and 4.78% for a pitch = 2.5 mm, when compared to flow without magnetic field in the respective geometries. When two sources are placed at x = 7.5 mm and 15 mm, there is an decrease of 19.02%, 31.3%, and 50.34% in the pressure drop when the magnetic fields of 1200 G, 1500 G and 2000 G are applied when the pitch = 10 mm. These values change to 4.18%, 9.52%, and 16.52% for a pitch = 5 mm and 3.08%, 6.88%, and 14.88% for a pitch = 2.5 mm. Hence, with the increase in the magnetic field, there is a decrease in pressure drop for both the cases and the pitches. This trend is valid only at lower magnetic field strength, because the decrease in the pressure drop dominates over the increase in pressure drop due to turbulence.

Suggested Citation

  • Basma Souayeh & Suvanjan Bhattacharyya & Najib Hdhiri & Fayçal Hammami & Essam Yasin & S. Suresh Kumar Raju & Mir Waqas Alam & Tarfa Alsheddi & Muneerah Al Nuwairan, 2022. "Effect of Magnetic Baffles and Magnetic Nanofluid on Thermo-Hydraulic Characteristics of Dimple Mini Channel for Thermal Energy Applications," Sustainability, MDPI, vol. 14(16), pages 1-27, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10419-:d:894702
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    References listed on IDEAS

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    1. Jin, Dongxu & Quan, Shenglin & Zuo, Jianguo & Xu, Shiming, 2019. "Numerical investigation of heat transfer enhancement in a solar air heater roughened by multiple V-shaped ribs," Renewable Energy, Elsevier, vol. 134(C), pages 78-88.
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    3. Saeed Khojaste Effatpanah & Mohammad Hossein Ahmadi & Pasura Aungkulanon & Akbar Maleki & Milad Sadeghzadeh & Mohsen Sharifpur & Lingen Chen, 2022. "Comparative Analysis of Five Widely-Used Multi-Criteria Decision-Making Methods to Evaluate Clean Energy Technologies: A Case Study," Sustainability, MDPI, vol. 14(3), pages 1-33, January.
    4. Basma Souayeh & Suvanjan Bhattacharyya & Najib Hdhiri & Mir Waqas Alam, 2021. "Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape," Sustainability, MDPI, vol. 13(6), pages 1-24, March.
    5. Suvanjan Bhattacharyya & Devendra Kumar Vishwakarma & Shramona Chakraborty & Rahul Roy & Alibek Issakhov & Mohsen Sharifpur, 2021. "Turbulent Flow Heat Transfer through a Circular Tube with Novel Hybrid Grooved Tape Inserts: Thermohydraulic Analysis and Prediction by Applying Machine Learning Model," Sustainability, MDPI, vol. 13(6), pages 1-41, March.
    6. Amir Zolghadri & Heydar Maddah & Mohammad Hossein Ahmadi & Mohsen Sharifpur, 2021. "Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
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