Effect of graphene on the performance of heat exchangers and related simulation studies. (2021)

• Record Type: Journal Article
• Authors: Natesan, Kapilan; Prabhu S, Sadashiva; Varghese Kodathu, Samuel; Verma, Rohan
• Is Part Of: Materials today. Volume 46:Part 17(2021)
• Page Start: 8359
• Page End: 8365
• Publication Date: 2021
• Subjects: Graphene -- Nanofluid -- Heat transfer -- Performance -- Simulation; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.03.416
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Industrial growth depends on the availability of energy sources and energy utilization. For various applications which require effective heat transfer between cold fluid and hot fluid (CF and HF), the heat exchangers (HEs) are widely used. The phenomenon related to quicker heat flow can enhance the thermal performance of HEs, resulting in considerable savings in energy. Hence, various methods are employed to improve heat flow characteristics of HEs. The solid particles having higher thermal conductivity can be used to enhance the heat transfer between CF and HFs. The graphene-based nanoparticles (NPs) possess higher thermal conductivity and have better properties such as low erosion, corrosion, higher stability, and hence it is preferred in various HEs. With the advancement in technology, graphene-based NPs can help to reduce the size of the HEs. In this paper, performance enhancement of HEs due to graphene addition in HEs is summarized. Further, simulated studies were carried out to analyze the performance characteristics of HEs by exploring it by using different boundary conditions. The simulation studies can also be extended to other types of exchangers of different designs. In the present studies, the simulated results are compared with experimental values for HF (Water at 40°C) and CF (Water at 21°C) for various flow rates of CF. After a good agreement, simulation studies are extended to Graphene-Water nanofluid (NF), and the results are presented. The simulation studies have proven their capability in predicting parameters related to performance evaluation of HEs.