Thermo-hydraulic analysis and optimization of CuO/water nanofluid inside helically dimpled heat exchangers

• Published in: Journal of thermal analysis and calorimetry Vol. 143; no. 6; pp. 4009 - 4024
• Main Authors: Deymi-Dashtebayaz, Mahdi, Akhoundi, Mahla, Ebrahimi-Moghadam, Amir, Arabkoohsar, Ahmad, Jabari Moghadam, Ali, Farzaneh-Gord, Mahmood
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2021; Springer; Springer Nature B.V
• Subjects: Analysis; Analytical Chemistry; Chemistry; Chemistry and Materials Science; Computational fluid dynamics; Dimensionless numbers; Dimpling; Entropy; Flow characteristics; Fluid flow; Genetic algorithms; Heat exchange; Heat exchangers; Heat transfer; Hydraulics; Inorganic Chemistry; Mass transfer; Measurement Science and Instrumentation; Nanofluids; Optimization; Physical Chemistry; Polymer Sciences; Reynolds number; Thermodynamics; CuO/water nanofluid; Dimpled heat exchanger; Entropy generation minimization; Genetic algorithm
• ISSN: 1388-6150; 1588-2926
• DOI: 10.1007/s10973-020-09398-0

The use of dimple technology and the use of nanofluids in different heat exchanging systems are known as powerful tools for improving heat transfer and fluid flow conditions. This work aims to prepare a comprehensive thermo-hydraulic parametric study and optimization of CuO/water nanofluid flow inside dimpled heat exchangers. The modeling procedure is based on the combination of the heat and mass transfer, fluid flow characteristics as well as the second law of thermodynamics. The parametric study is done for evaluating three thermo-hydraulic criteria (i.e., entropy generation number, Bejan number and irreversibility distribution ratio) with changing some of the most important fluid conditions (namely Reynolds number, average flow temperature and nanofluid concentration) as well as pitch ratio of the heat exchanger. Finally, the optimization is done through the combination of entropy generation minimization approach and genetic algorithm method. The results indicate that among the decision parameters, the average flow temperature and the pitch ratio have the lowest and highest effect on the entropy generation, respectively. From the optimization process, the optimum values of Reynolds number, dimensionless average flow temperature, nanofluid concentration and pitch ratio are 4610.428, 1.077, 0.000216 and 0.00326, respectively. Graphic abstract