https://orcid.org/0000-0002-0169-306X
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ABSTRACT
Nucleate pool boiling of hydrazine-reduced graphene oxide (H-rGO) with rough surface is experimentally investigated. Using the created nucleate boiling setup, heat transfer coefficient (HTC) of nanofluids is investigated with rough surface. As compared with base fluid, HTC of nanofluids increases with concentrations. At maximum heat flux, HTC of higher concentration with rough surface is found as ~29.44 kW/m2 K. A detailed study on surface roughness, contact angle, and microscopic image of boiled surface is conducted. Surface roughness of nanofluid boiled heat surface decreases with increasing concentrations. Contact angle on boiled surfaces exhibits hydrophobic nature and increases with concentrations. Hence, it is difficult to interpret the HTC of nanofluids with a single parameter. It is suggested that HTC of nanofluids depends on modification of heating surface due to nanodeposits (formation of pores), which aid for the enhancement. In addition, a semi-analytical model is employed to determine the nucleation sites on the surface and it matches well with experimental results of HTC.
Nomenclature
| A | = | area of the heater surface (m2) |
| cp | = | specific heat of liquid (J/kg K) |
| Csf | = | surface fluid combination (-) |
| Dp | = | size of the flakes (nm) |
| g | = | acceleration due to gravity (m/s2) |
| h | = | heat transfer coefficient (W/m2K) |
| hlv | = | latent heat of vaporization (J/kg) |
| I | = | current (A) |
| ID | = | intensity of “D” band after baseline correction (counts) |
| IG | = | intensity of “G” band after baseline correction (counts) |
| La | = | in-plane crystallite size (nm) |
| M | = | molecular weight (kg/mol) |
| Na | = | nucleation site density (sites/m2) |
| P | = | pressure inside the system (Pa) |
| Pr | = | Prandtl number (-) |
| PR | = | reduced pressure (-) |
| q | = | heat flux (W/m2) |
| Q | = | heat transfer (W) |
| Ra | = | average surface roughness (µm) |
| T | = | temperature (K) |
| V | = | voltage (Volts) |
| Greek symbols | = | |
| λ | = | wave length of incident wave (m) |
| µ | = | dynamic viscosity (mPa s) |
| Δ | = | increment (-) |
| θ | = | angle between the incident ray and the scattering planes, contact angle (°) |
| θ* | = | contact angle of base fluid on the clean surface (°) |
| ρ | = | density (kg/m3) |
| σ | = | surface tension (mN/m) |
| Subscripts | = | |
| e | = | excess |
| l | = | liquid |
| nucl | = | nucleate boiling |
| o | = | initial |
| sat | = | saturation |
| v | = | vapor |
| w | = | wall |
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Kamatchi Rajaram
Kamatchi Rajaram has successfully completed his PhD from National Institute of Technology, Tiruchirappalli, India. He has more than 20 years of teaching cum research experience. Currently, he is working as Professor of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore, India. He is currently working in waste heat recovery systems, perovskite material, solar thermal energy, and solar steam generation by incorporating novel nanomaterials.
Mohan Raman
Mohan Raman received the B.E. degree in mechanical engineering from the Madras University, Chennai, in 2001 and M.E degree in Thermal engineering from Anna University, Chennai, in 2005 and a Ph.D. degree in Heat transfer and CPU cooling from Anna University Chennai in 2012. He currently works as Professor in Mechanical engineering department in Sona College of Technology, Salem. His current research interests include the Heat transfer, Thermal engineering and CPU Cooling.
Kalaimegam Dhanapal
Kalaimegam Dhanapal received the bachelor’s degree in Mechanical Engineering and the master’s degree in Thermal Engineering during the year 2010 and 2014 from Anna University, Chennai respectively. He also completed the Ph.D. degree in Mechanical Engineering at Anna University, Chennai in the area of Nanofluids during the year 2022. He has been working as Assistant Professor in the Department of Mechanical Engineering at Mahendra Engineering College (Autonomous), Mallasamudram, Namakkal, during the period August 2018 to till now. His areas of interests include nanofluids, composite materials, and renewable energy systems.
Suresh Muthusamy
Suresh Muthusamy received his Bachelor of Engineering in Electrical and Electronics Engineering, Master of Engineering in Power Electronics and Drives during the year 2009 and 2011 from Anna University, Chennai and Anna University, Coimbatore respectively. He then completed Doctor of Philosophy in Electrical Engineering from Anna University, Chennai during the year 2023 in the area of hybrid renewable energy systems. He worked as Assistant Professor in the Department of Electrical and Electronics Engineering at Kongu Engineering College (Autonomous), Perundurai, Erode, during the period June 2011 to January 2020. From January 2020 onwards, he has been working as Assistant Professor Senior Grade in the Department of Electronics and Communication Engineering at Kongu Engineering College (Autonomous), Perundurai, Erode. He published more than 110 research articles in well reputed and refereed national/international journals from Elsevier, Springer, Taylor & Francis, Wiley, SAGE publishers, ASME, ASTM International, MDPI, CRC Press, etc and indexed in SCI, SCIE, ESCI, Scopus and Web of Science with good impact factors. He presented several research articles in national & international conferences and also serving as the reviewer, editorial board member for about 75 international journals including International Journal of Energy Research-Wiley, IET Renewable Power Generation, IET Energy Systems integration-Wiley, IET Journal of Engineering, etc. He received 1208 google scholar citations with h-index of 20 and i-10 index of 36. To his credit, he has filed and published 28 Indian patents in IPR website, governed by Ministry of Commerce and Industry, Government of India. His areas of interests include hybrid renewable energy systems, power electronic converters, hybrid electric vehicles, applications of deep learning & machine learning algorithms and battery management systems.
Ponarun Ramamoorthi
Ponarun Ramamoorthi received B.E. degree in Electrical and Electronics Engineering from Anna University, Chennai, Tamil Nadu, India in 2009 and then completed M.E. degree in Power Electronics and Drives from Anna University, Chennai, Tamil Nadu, India in 2011. Currently, he is working towards the Ph.D degree in Electrical Engineering under the Faculty of Electrical Engineering, Anna University, Chennai. At present, he is working as Assistant Professor in the Department of Electrical and Electronics Engineering, Theni Kammavar Sangam College of Technology, Theni, Tamil Nadu, India.
Murugesan Govindasamy
Murugesan Govindasamy received B.E. in Electronics and Communication Engineering from Kongu Engineering College, Perundurai, Erode, in 1988, M.S. in Electronics and Control from Birla University, Pilani in 1994, and M.E. in Applied Electronics from College of Engineering Guindy, Anna University, Chennai. He completed PhD in the field of Communication Networks from Anna University, Chennai in 2009. He has 35 years of Teaching and 19 years of Research experience. Presently he is working as Professor ECE department and Chief Coordinator-Academic at Kongu Engineering College, Perundurai, Erode. His interests are network design algorithms, label switching, wireless embedded systems, image processing and electronic circuit design.
Om Prava Mishra
Om Prava Mishra received Bachelor of Engineering in Electronics and Communication Engineering from Institution of Engineers during the year 2007 and Master of Engineering in Digital System and Computer Electronics from JNTU Hyderabad during the year 2010. Currently, she is working towards the Ph.D. degree in Electronics and Communication Engineering at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai from January 2022 to till date. She published 3 papers in SCI journals and presented 3 papers in international Conferences. Her areas of interest include biomedical signal processing, image processing, artificial intelligence, nature inspired algorithms, renewable energy technologies, etc.