Artificial neural network and numerical predictions on flow and heat transfer characteristics for buoyancy-driven flows in regard to dilatant fluids

Author(s):  
Sudhanshu Pandey ◽  
Yong Gap Park ◽  
Young Min Seo ◽  
Man Yeong Ha
2021 ◽  
Author(s):  
Fuat Kaya

Abstract The purpose of this paper is to study the effects of the use of Boron nitride (BN) as nano-particle on pressure drop and heat transfer in a microchannel. The governing equations for the fluid flow were solved by using Fluent CFD code and artificial neural network (ANN). Computational results acquired from Fluent CFD code and artificial neural network (ANN) for alumina (Al2O3) as nano-particle were compared with numerical values obtained in the literature for validation. On the basis of a water-cooled (only water, water+alumina and water+boron nitride) smooth microchannel were designed, and then the corresponding laminar flow and heat transfer were studied numerically. Results derived from the numerical tests (NT) and artificial neural network (ANN) show good agreement with the values mentioned in the literature and these results also show by the comparison research which was conducted considering the heat transfer and pressure loss parameters between BN and widely used alumina that BN is more convenient nano-particle.


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