Turbulent forced convective flow in a conical diffuser: Hybrid and single nanofluids. (March 2023)

• Record Type: Journal Article
• Authors: Iachachene, Farida; Haddad, Zoubida; Arıcı, Müslüm; Jamei, Mehdi; Mataoui, Amina
• Is Part Of: Engineering analysis with boundary elements. Volume 148(2023)
• Page Start: 205
• Page End: 219
• Publication Date: 2023-03
• Subjects: Conical diffuser -- Hybrid nanofluid -- Single nanofluid -- Turbulent flow -- Forced convection -- Gene expression programming; GEP Gene Expression Programming -- GA Genetic algorithm -- ML Machine learning -- NFs Nanofluids -- UDF User Defined Function -- RRSE Root relative square error; Boundary element methods -- Periodicals; Engineering mathematics -- Periodicals; Équations intégrales de frontière, Méthodes des -- Périodiques; Mathématiques de l'ingénieur -- Périodiques; Boundary element methods; Engineering mathematics; Periodicals; 620.00151
• DOI: 10.1016/j.enganabound.2022.12.027
• Languages: English
• ISSNs: 0955-7997

Abstract

Highlights: A computational study is performed on turbulent forced convection in a diffuser. A significant gap between experimental and theoretical based models is revealed. Single NFs have a negative effect on heat transfer, while for hybrid NFs, heat transfer increases first and then reduces. Difference between single and hybrid NFs diminishes as Reynolds number increases. Gene Expression Programming model can accurately predict the Nusselt number. Abstract: Turbulent forced convective flow of hybrid and single nanofluids in a conical diffuser is investigated numerically. Simulations are conducted for various Reynolds ( Re =10000-70000) and different concentrations ( ϕ =0-1.5 vol%) at equal ratio of TiO2 :SiO2 . The impact of using theoretical and experimental correlations for dynamic viscosity and thermal conductivity on turbulent forced convection of TiO2 showed that the mean Nusselt ( Nu ) number is considerably reduced with the use of the experimental model. However, when the theoretical model is used, Nu varies insignificantly. Addition of TiO2 nanoparticles decreases the heat transfer inside the diffuser, whereas addition of TiO2 –SiO2 nanoparticles either enhances or decreases the heat transfer rate. Compared to the pure fluid, hybrid nanofluids show a maximum enhancement of 5% and a maximum decrease of 9.7% at ϕ = 0.5 vol% and ϕ = 0.5 vol% at Re =10000, respectively. However, TiO2 nanofluids show a maximum decrease of 19% at ϕ =1.5 vol% and Re = 30000. As Re increases, the deviations between TiO2 and SiO2 -TiO2 nanofluids diminish. Moreover, the Gene Expression Programming model can accurately evaluate Nu versus Re number and nanoparticle concentration.